e-Book of Abstracts

Neuronal transmitters are released via the fusion of synaptic vesicles with the plasma membrane. Vesicles dock to the membrane via a protein complex termed SNARE, which contains membrane attached (t-SNARE) and vesicle attached (v-SNARE) proteins. The fusion occurs in response to a calcium inflow, and the vesicle protein Synaptotagmin (Syt) serves as a calcium sensor. A cytosolic protein Complexin (Cpx) interacts with the SNARE complex, restricting the spontaneous fusion. Although molecular interactions of these proteins have been extensively studied, it is still debated how the fusion proteins dynamically interact with each other and with lipid bilayers to trigger lipid merging. To elucidate this mechanism, we combined molecular dynamics (MD) simulations of Syt interacting with the SNARE complex, Cpx, and lipid bilayers and genetic approaches in Drosophila. Basing on MD simulations, we created a model of the molecular “fusion clamp” wherein Cpx dynamically interacts with v-SNARE, preventing full SNARE assembly. The model enabled us to predict new mutations in v-SNARE and Cpx that enhance Cpx ability to inhibit spontaneous fusion. To understand how Syt regulates Ca2+ dependent fusion, we employed MD simulations to investigate Syt conformational ensemble and its dependence on Ca2+ binding. Basing on the results of a genetic screen that revealed new loss-of-function mutations in Syt, we developed a model of the pre-fusion protein complex, including Syt interacting with the SNARE bundle, Cpx, and lipid bilayers. This model creates the basis for a systematic approach to manipulating the fusion machinery based on theoretical predictions derived from MD simulations.

Authors: Maria Bykhovskaia, A. Jagota, J. T. Littleton

Introduction: Most studies employ functional magnetic resonance (fMRI) imaging to study brain networks in vivo, using the blood oxygen level dependent (BOLD) effect. However, the BOLD effect is rather unspecific and represents a mixture of cerebral blood flow (CBF), blood volume (CBV) and oxygenation (CMRO2) changes. Therefore, we explore the neurophysiological basis of functional brain connectivity by using simultaneous positron emission tomography and MR imaging (PET/MR).

Methods: We simultaneously measure BOLD-fMRI and PET in small animals. Analysis is performed using ROI- and independent component analysis techniques. Different PET-tracers such as the glucose metabolism tracer [18F]FDG, the perfusion marker [15O]H2O, the serotonin transporter tracer [11C]DASB and the D2 dopamine receptor antagonist [11C]Raclopride are used in this project (DLR-01GQ1415).

Results: When comparing [18F]FDG-PET with BOLD-fMRI, we could show significant differences (p<0.005) in the constitution of a default mode-like network structure in rats. The Euclidean distance between regions does not correlate with PET/MR connectivity measurements. [18F]FDG-PET connectivity information follows a small world characteristic, i.e. measured and randomized clustering coefficient are nearly equal (CM approx. equal CR), whereas the characteristic path length (Lambda M < Lambda R) in the measured metabolic networks is smaller compared to randomized networks.

Conclusion: Brain networks, based on metabolic information, are in some respects complementary to the information derived from BOLD-fMRI. The typical resting state networks appear to be constructed from a multitude of metabolic and receptor specific subsystems, which can specifically be assessed by combined PET/MR imaging. Connectivity based on metabolic information (Cometomics) has a high potential for basic research as well as clinical translation.

Authors: Hans F. Wehrl, André Thielcke, Suril Gohel, Bernd Pichler, Bharat Biswal

Our collaborative study focused on the analysis and modeling of spinal cord circuits that generate the rodent locomotor rhythm, including the central rhythm generators (RGs), the left-right coordination of locomotor activity, and the effects of afferent stimulation. Coordination of activity between left and right sides of the cord is provided by contralaterally projecting commissural interneurons (CINs). We have constructed and analyzed a spinal cord model consisting of left and right RGs interacting bilaterally via three neuronal pathways mediated by different CINs. The CIN populations incorporated in the models include the genetically identified inhibitory (V0D) and excitatory (V0V) subtypes of V0 CINs and excitatory V3 CINs. The model also includes the ipsilaterally projecting excitatory V2a interneurons mediating excitatory drive to the V0V CINs. The proposed network architectures and CIN connectivity allow the models to closely reproduce and suggest mechanistic explanations for speed-dependent contributions of V0D and V0V CINs and V2a interneurons to left–right alternation of neural activity, and switching between left–right alternating and left–right synchronous hopping-like patterns in mutants lacking specific neuron classes. We have also investigated and modeled the phase resetting and speed accelerating effects of dorsal root stimulation on the locomotor activity generated in the spinal cord and the roles of slow modulatory actions of these sensory pathways. The computational models provide insights into the architecture of the spinal networks and the organization of different CIN and afferent pathways controlling locomotor activity, and propose testable predictions about the organization and operation of mammalian locomotor circuits.

Authors: Ilya A. Rybak, R. M. Harris-Warrick, N. A. Shevtsova, S. Dietz, O. Kiehn

Individual differences in reward learning have elicited increasing interest in the recent years, in particular concerning addiction and craving associated with drug-taking. Animal studies have shown that the same training situation can result in widely differing patterns of conditioned responses. Some individuals, sign-trackers (ST) are attracted to the signal while others, goal-trackers (GT) are directly attracted by the reward. M. Khamassi and colleagues (Paris) developed a computational model which accounts for a large range of ST/GT results in terms of the balance between concurrent learning processes and their dependence on dopamine signaling in the striatum. This has led to a series of experimental predictions, which the current project is testing using rigorous protocols. In Bordeaux, we observed the emergence of ST and GT populations and tested the algorithmic nature of their responses. GT, but not ST responses, were found to be sensitive to the value and the precise identity of rewards in devaluation procedures, as expected from the computational model. Using an anatomically selective pharmacogenetic approach in TH-Cre transgenic rats, we will then examine the role of tonic dopamine levels in several regions of the striatum on ST versus GT learning and expression. In Baltimore, M. Roesch and colleagues assess the role of behavior and dopamine (DA) in determining sign- versus goal-tracker status through fast-cyclic voltammetric measurements of phasic DA signals and optogenetic manipulation of DA neurons. These data are expected to shed new light on the determinants of variations in conditioned behavior.

Authors: Alain R. Marchand, Mehdi Khamassi, E. Coutureau, M. R. Roesch

Olfactory perception and behavior relies on the ability of olfactory neural circuits to generate stable representations of odor identity from olfactory cues that fluctuate over a large range of odor concentrations. Odor percepts are thought to emerge in piriform cortex, yet how information about odor identity is encoded in piriform neural networks remains poorly understood. To explore fundamental principles of cortical odor coding we have recorded, using in vivo two-photon calcium imaging of odor-evoked activity from large ensembles of piriform neurons in anesthetized mice. Population coding analyses reveal that odorant identity - at a given odorant concentration - can accurately be decoded from spatially distributed ensembles of piriform neurons. However, piriform response patterns change substantially over a 100-fold change in odor concentration, apparently degrading information about odor identity.

We now identify a concentration-invariant subnetwork of piriform neurons, which encodes odor identity independent of intensity. Our data suggest that distinct perceptual features of odors are encoded in distinct subpopulations of piriform neurons. In independent experiments we have identified genes selectively expressed in piriform neurons with distinct neural cell type identity and connectivity. Together, our results establish novel experimental and computational tools to decipher the contributions of piriform neural circuit components to the encoding of distinct stimulus features in olfactory cortex.

Author: Alexander Fleischmann

Purpose: In the retina, visual signals from photoreceptors are pooled by bipolar cells, rectified, and pooled again to generate responses of retinal ganglion cells (RGCs). This process creates “subunits” in the RGC receptive field (RF) that mediate nonlinear responses to fine texture and movement. We present a novel method to expose subunit organization based on RGC recordings.

Methods: Light responses of parasol RGCs in isolated primate retina were obtained using multi-electrode recordings. Responses were fitted with a model in which subunit activity is produced by a linear combination of stimulus intensities, then exponentiated and summed to determine RGC firing rate. The likelihood of the model parameters was maximized by alternating between estimating subunit membership via soft-clustering the spike-triggered stimulus ensemble, and estimating the linear weights associated with each subunit. To test the functional relevance of subunits, a “null” stimulus was presented, orthogonal to the linear RF estimated from the spike-triggered average.

Results: In simulated data, this estimation approach accurately recovered subunits asymptotically, and yielded subunit estimates that were local aggregates of actual subunits in the limited data regime. In real data, subunits were spatially localized and non-overlapping, as expected from bipolar inputs. Null stimulation revealed the presence of spatial nonlinearities, some of which could be explained by the estimated subunits.

Conclusions: A novel technique for estimating subunits of primate RGCs shows promising results in recovering putative bipolar cell spatial structure and explaining response nonlinearities.

Authors: E.J. Chichilnisky, N. Shah, N. Brackbill, A. Tikidji-Hamburyan, C. Rhoades, G. Goetz, A. Sher, A. Litke, L. Paninski, E. Simoncelli

Identifying essential nodes in the brain and manipulating their activity is important to understand brain function and to treat neurological and psychiatric conditions. Selection of targets in most clinical cases is based, however, on a trial-and-error strategy since targeted manipulations are conducted in the absence of guiding theory. Here we use network theory to identify the core essential nodes in system-wide brain networks formed in an experimental model of learning and memory. We first find that long-term potentiation (LTP) of synapses in the hippocampus (HC) induces a functional reorganization of long-range connections into a network of networks (NoN) composed by the HC, prefrontal cortex (PFC) and nucleus accumbens (NAc). Network optimization theory predicts that NAc is the essential core node in this memory related NoN, despite being a low connectivity node. This theoretical prediction is experimentally confirmed by inhibition of a single predicted core node in NAc by a targeted pharmacogenetic intervention which, remarkably, completely eliminates LTP-induced functional reorganization of the whole NoN. Besides unveiling a fundamental role of the NAc in the control of HC-PFC interactions in the context of memory, this result suggests the applicability of network optimization theory to design intervention protocols in the brain.

Authors: Hernan Makse, L. Parra, S. Canals

We present a novel method for estimation of the fiber orientation distribution (FOD) function based on diffusion-weighted Magnetic Resonance Imaging (D-MRI) data. We formulate the problem of FOD estimation as a regression problem through spherical deconvolution and a sparse representation of the FOD by a spherical needlets basis that form a multi-resolution tight frame for spherical functions. This sparse representation allows us to estimate FOD by an $l_1$-penalized regression under a non-negativity constraint. The resulting convex optimization problem is solved efficiently by an alternating direction method of multipliers (ADMM) algorithm. The proposed method leads to a sharp feature-preserving reconstruction of the FODs. Through extensive experiments, we demonstrate the effectiveness and favorable performance of the proposed method compared with two existing methods. We also apply the proposed method to real 3T D-MRI data sets of healthy elderly individuals. The results show realistic descriptions of crossing fibers with less noise than competing methods even with a relatively small number of gradient directions.

Authors: Jie Peng, H. Yan, O. Carmichael, D. Paul

The striatum is a major site of learning and memory formation for sensorimotor and cognitive association. One of the mechanisms underlying memory storage is synaptic plasticity - the long lasting, activity-dependent change in synaptic strength. Synaptic plasticity requires an elevation in intracellular calcium. A common hypothesis is that the amplitude and duration of calcium transients can determine the direction of synaptic plasticity. Calcium dependence of striatal plasticity if unclear, because it requires dopamine and because of the diversity in stimulation protocols. To test whether calcium can predict plasticity direction, we developed a calcium-based plasticity rule using a spiny projection neuron model with sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion. We utilize three spike-timing-dependent plasticity (STDP) induction protocols, in which post-synaptic potentials are paired with precisely timed action potentials and the timing of such pairing determines whether potentiation or depression will occur. Results show that despite the variation in calcium dynamics, a single, calcium-based plasticity rule, which explicitly considers duration of calcium elevations, can explain the direction of synaptic weight change for all three STDP protocols. Additional simulations show that the plasticity rule correctly predicts the NMDA receptor dependence of long-term potentiation and the L-type channel dependence of long term depression. We further show that the same calcium plasticity rule can predict the outcome of several frequency dependent paradigms. By utilizing realistic calcium dynamics, the model reveals mechanisms controlling synaptic plasticity direction, and shows that the dynamics of calcium, not just calcium amplitude, are crucial for synaptic plasticity.

Authors: Joanna Jedrzejewska-Szmek, S. Damodaran, D. B. Dorman, K. T. Blackwell

Parkinsonism is associated with a range of changes in neural activity in the basal ganglia, including enhanced bursting, oscillations, and correlations. While we know that these all stem in some way from depletion of the neurotransmitter dopamine, how the loss of dopamine induces these changes remains to be fully understood. I will discuss collaborative, interdisciplinary work exploring how the cellular architecture and network dynamics of an inhibitory loop in the basal ganglia, namely the pallidostriatal circuit, may yield exaggerated beta-band synchrony and locking to beta oscillations, specifically in the dopamine-depleted state, with possible implications for motor function. Our experiments reveal and characterize a specificity in the striatal targets of a pallidostriatal projection and suggest that the pallidal neurons involved exert a direct functional influence on motor capabilities under dopamine depletion. Our computational modeling predicts that the pallidostriatal pathway influences striatal output preferentially during periods of synchronized activity within globus pallidus (GPe) and that, under dopamine-depleted conditions, this effect becomes a key component of a positive feedback loop between the GPe and striatum that promotes synchronization and rhythmicity. Overall, our results generate novel predictions about the role of the pallidostriatal pathway in shaping basal ganglia activity in health and disease. The project involved in a collaboration with Aryn Gittis at Carnegie Mellon University and members of our groups, supported by a US National Science Foundation CRCNS award.

Authors: Jonathan Rubin, V. L. Corbit, T. C. Whalen, K. T. Zitelli, S. T. Crilly, A. H. Gittis

Sleep and awake states feature different levels of neuromodulators, which contribute uniquely to the excitability of neuronal circuits, network firing patterns, and the plasticity of their synapses. We investigated changes in the log-normally distributed firing rates of hippocampal CA1 neurons between different sleep and awake states and within each state individually. Firing-rate changes within non-REM sleep, REM sleep, and state transitions from non-REM to REM favored higher-firing neurons, with either smaller increases or stronger decreases among lower-firing neurons. In contrast, transitions from REM to non-REM sleep reduced variability across the population, resulting in higher firing among lower-firing neurons and vice versa. These dynamics yielded net decreases in firing rates across sleep, with the largest decrease occurring in lower-firing cells, and net increases during waking, with median quantiles showing the greatest firing increases. We previously showed that firing decreases across sleep were predicted by the density of spindles and sharp-wave ripples in non-REM epochs and were integrated over epochs of REM sleep. Overall, these results demonstrate that sleep/wake states affect lower and higher-firing neurons differently, with non-REM sleep playing a normalizing role, and are consistent with competitive interactions that favor higher-firing neurons, and greater plasticity in median and lower-firing cells.

Authors: Kamran Diba, H. Miyawaki

Muscle spindles are widely considered length and velocity encoders for our kinesthetic sense. They are a type of sensory organ that is particularly sensitive to stretch located within muscle, but their spike rates non-unique and history-dependent with respect to muscle length information, calling into question their ability to faithfully provide a sense of position to the central nervous system. Here, we hypothesize that muscle spindle spike rates are more closely related to force-related variables of the spindle-bearing muscle during stretch, due to the in-series connections of these sensors with their own muscle fibers. First, we show muscle spindle Ia afferents in cat fire in direct proportion to history-dependent passive muscle force and rate change in force across a range of stretch perturbations, with only a single set of parameters. We used supervised learning algorithms to find optimal summations of measured force and its first time-derivative that minimized the error between the model output and the measured spike rates. Second, we show that a similar force-based model, accounting for passive (i.e. non-contractile) tissues within the musculotendon, is capable of predicting history-dependent spike rates in anesthetized rats. Here, we included a model of passive tissues in the muscle and removed this estimated stiffness from the measured force before performing optimizations to predict spike rates. Our results suggest that multi-scale models of muscles and muscle spindles are necessary for understanding the underlying mechanisms of proprioceptive sensory encoding, and provide a mechanistic explanation for history-dependent phenomena observed at both the cellular and behavioral levels.

Authors: Kyle Blum, B. L. d'Incamps, Paul Nardelli, D. Zytnicki, T. Cope, L. Ting

Epilepsy is one of the most common neurological syndromes, affecting an estimated 50 million people worldwide. In one-third of these patients, seizures cannot be controlled despite maximal medication management. It is increasingly recognized that epileptic seizures represent an interplay of neural network dynamics. The complexity of these networks interactions which define the epileptogenic cortex and drive seizure initiation and spread makes understanding and treating epilepsy a unique challenge. Utilizing invasive brain voltage recordings from patients with intractable epilepsy, we infer dynamic functional networks during spontaneous seizures based on several standard coupling statistics (correlation, coherence, phase locking value). We apply a recently developed graph theory approach to identify and track in time well-connected subsets of nodes (a.k.a., communities) in the inferred functional networks. We show in simulation - of both abstract and biophysically motivated models - that the network inference and community detection methods perform accurately. As part of this work, we have developed the network inference and analysis procedures within an online version control system, and we will make the repository publicly available, for reuse and further development by the community. The dynamic network analysis and statistical modeling of human seizure data could provide new approaches to improve patient care of medically refractory epilepsy. In our continuing work, through prospective and retrospective studies, we will apply these methods to identify principled surgical targets, and predict which patients will - and will not - benefit from surgery and to suggest alternative surgical and control strategies.

Authors: Louis-Emmanuel Martinet, E. Spencer, E. Kolaczyk, M. Kramer, S. Cash

Neurotransmitter release at a fast chemical synapses is critical for communication of information between neurons in the brain, and depends on the spatio-temporal dynamics of calcium influx, its reaction with binding partners and free diffusion, all within the active zone, an area less than 0.05 µm2. Therefore, understanding of the geometrical relationship between the synaptic vesicles and the Ca2+ channels is critical for understanding the determinants of synaptic strength, time course, and plasticity. Because the spatial scale is in the nanometer range, direct experimental observation of the spatio-temporal dynamics driving synaptic vesicle fusion has not been possible. One strategy is therefore to use a computational approach to simulate the nonlinear dynamics of Ca2+ -triggered vesicle fusion. In our study we built a stochastic model of the presynaptic calcium channel gating, Ca2+ diffusion and binding, and synaptic vesicle fusion. To understand the influence of topography on synaptic diversity, we performed simulations designed to predict the different behavior of inhibitory and excitatory terminals within the cerebellar cortex. Our preliminary results suggest that inhibitory terminals use small clusters of Ca2+ channels to drive release from their periphery, as we described previously for the calyx of Held. While the excitatory terminals require a random placement of Ca2+ channels , as well as random placement of vesicles with an exclusion zone of > 30 nm. We therefore suggest that nanoscale distribution of Ca2+ channels and synaptic vesicles can differ between synapses and is a major factor influencing the diversity of function across synapse types.

Authors: Maria Reva, N. Rebola, D. DiGregorio

Widening economic inequity is a key concern for our society, and previous cohort-based studies have also suggested a link between economic inequity and depression. However, little is known about the underlying neural mechanism of the link, due to substantial individual differences. Here, we demonstrate that functional magnetic resonance imaging activity patterns in the amygdala/hippocampus induced by the inequity during an economic game can predict both present and future (measured one year later) depression index (BDI-II). Such predictions were not possible by behavioral measures of the participants. These findings reveal the connection between the sensitivity of the amygdala/hippocampus to economic inequity, and the present and future depression index, and suggest an important involvement of the amygdala and hippocampus into the effect of economic inequity on human mental states.

Author: Masahiko Haruno

In a neural map, cells of the same subtype perform the same computation in different places of the sensory/visual field. How these different cells code together a complex visual scene is unclear. It is commonly assumed that they will code for a single feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the retina, we show that a homogeneous population of fast OFF ganglion cells encode simultaneously two radically different features of a visual scene. Cells close to a moving object coded linearly for its position. More distant cells remained largely invariant to its position and responded non-linearly to speed changes. Cells switched from one computation to the other depending on the stimulus. We developed a model that predicts these results, and determined how it is implemented by the retinal network. Ganglion cells of a single type do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems.

Authors: Olivier Marre, S. Deny, U. Ferrari, E. Mace, P. Yger, R. Caplette, S. Picaud, G. Tkacik

Sounds in natural environments are complex mixtures from many different sources. The human auditory system performs the challenging task of reliably separating and grouping competing sounds in natural environments into percepts called "auditory streams". The present project combines human psychophysics with temporally and spatially high-resolution recordings (electrocorticography, ECoG) to characterize spatio-temporal neural features associated with human auditory perception. We recorded large-scale ECoG data from multiple auditory and auditory-related cortical areas during an auditory streaming paradigm, including stimuli that support auditory perceptual bistability. Auditory stimuli were sequences of pure tones presented in an ABA_ triplet repetition fashion, where we varied the frequencies of A and B tones by semitones. Triplets presented with 6 and 8 semitone differences were perceptually bistable stimuli. We characterized ECoG in the core auditory cortex in the time domain as averaged evoked potentials (AEPs), where responses were time-locked to the onset of each tone in the triplet. We then compared neural activity across all recording sites during the two auditory percepts using dynamic mode decomposition (DMD), uncovering differences in spatial DMD modes between the two percepts throughout cortex outside core auditory cortex. Our results suggest perceptual switching is correlated with neural activity across a network of cortical areas, and that it may be possible to describe these spontaneous switches using dynamic models derived from large-scale ECoG data.

Authors: Rodica Curtu, Bingni W. Brunton, X. Wang, K. V. Nourski

In natural vision, humans can perceive thousands of objects and actions, getting subjective impressions. Revealing how the brain represents these objective and subjective experiences tells us how the brain sees and structures the natural world. Here, I introduce some of our recent studies on visual representation in the human brain via predictive models of brain activity under natural vision. We recorded human brain activity evoked by natural movies using functional magnetic resonance imaging (fMRI). Using high-dimensional latent feature spaces derived from linguistics, we have developed voxel-wise encoding models and fit them to the movie-evoked activity in the human occipitotemporal cortex. Analyzing the fit models revealed semantic spaces that described the representations of objects, actions, and impressions in the human brain. Moreover, by solving an inverse problem, we have also succeeded in decoding objective and subjective experiences from the evoked brain activity.

Author: Shinji Nishimoto

Large-field visual stimulation such as when looking out of a moving train, causes optokinetic response (OKR) eye movements that are controlled by a sensorimotor circuitry involving a multitude of cortical and subcortical areas. The visual-motion sensitive parietal areas MT and MST have been shown by lesion studies to be critically involved in the control of OKR eye movements. Neural recordings during OKR suggested that the firing rate of MSTd neurons directly encodes stimulus velocity, which might then be used as control signal by brainstem nuclei. Other studies, however, found visual-motion responses modulated by eye movement-related signals in the same area. In our project, we characterize neural tuning in MSTd in the awake monkey using an information-theoretic method accounting for neuronal latencies. We showed that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding a single variable. Neural responses revealed a large diversity of tuning to combinations of retinal and extraretinal variables such as retinal speed, eye velocity, and direction of motion. Our results support the idea that signals in MSTd provide the possibility to construct motor output from appropriate combinations of neural responses by subsequent brain areas. The distributed and implicit multimodal coding observed in area MSTd thus seems to provide basis functions for flexible readout rather than coding single variables as previously suggested.

The presentation is based on and extends the paper "Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation" by L. Brostek, U. Büttner, M. J. Mustari, and S. Glasauer.

Authors: Stefan Glasauer, L. Brostek, U. Büttner, M. J. Mustari

The origins of extracellular field potentials in the brain are often unclear. Many modeling studies and interpretations of the EFP focus solely on extracellular potentials induced by synaptic currents on the dendrites and by spikes at the soma of a postsynaptic neuron.

Based on previous recordings of field potentials in the auditory brainstem, we here present a model of extracellular potentials from axon fiber bundles. The aim of the model is to show how a wide array of field potentials may be explained by the axons' anatomical features. In particular, we show that the branchings and terminations of axons in a typical projection area lead to a dipolar EFP structure. Dipoles have a farther spatial reach than the quadrupolar potentials traditionally associated with axons. The model predicts strong contributions of nerve tracts to the extracellular potential, which are currently neglected by other models.

Along with the theoretical description, our multichannel electrode recordings from the barn owl auditory brainstem show several features observed in the model. We recorded responses in the nucleus laminaris (NL) to tones, clicks, and white noise stimuli. The low­-frequency (<1kHz) component of the EFP response to auditory click stimulation in NL shows the polarity reversal predicted by the model. The low­ frequency component has a dipolar structure and extends spatially for at least several millimeters from the nucleus.

Authors: Thomas McColgan, P. Kuokkanen, C. Carr, R. Kempter

In [Denker et al. 2011, Cereb. Cortex], it was shown by Unitary Event analysis [Grün 2002a, 2002b, Neural Comput.] that the occurence of simultaneous spikes is more strongly locked to the phase of the LFP beta-oscillations than the activity of the other neurons, which was related to the existence of cell assemblies. To study the influence of remote brain areas visible in the LFP on the correlated single neuron activity in a small cortical subnetwork, we examine a balanced network of homogeneously connected binary model neurons [Ginzburg et al. 1994, PRE] receiving input from a sinusoidal perturbation [Kühn, Helias 2016, arxiv]. This simple model serves us to capture the main properties and illustrate mechanisms that cause time-modulated correlations. The Glauber dynamics of the network is simulated and approximated by mean-field theory. Treating the periodic input in linear response theory, the cyclostationary first two moments are analytically computed, which agree with their simulated counterparts over a wide parameter range. The zero-time lag correlations consist of two terms, one due to the modulated susceptibility (via external input and recurrent feedback) and one due to the time-varying autocorrelations. For some parameters, this leads to resonant correlations and non-resonant mean activities. Our results can help to answer the salient question how oscillations in mesoscopic signals and spike correlations interact. An interesting extension of our model is to include cell assemblies [Litwin-Kumar et al., 2012, Nature Neur.] allowing a closer comparison to experimental findings. Supported by the Helmholtz foundation (VH-NG-1028, SMHB); EU Grant 604102 (HBP). Simulations with NEST (www.nest-simulator.org).

Authors: Tobias Kühn, M. Denker, P. Mana, S. Grün, M. Helias

Odor perception is the result of a complex cascade of events initiated by the interaction of odorants with olfactory receptors (ORs). ORs are G protein-coupled transmembrane receptors (GPCRs) expressed in our olfactory receptor neurons (ORNs). Each ORN expresses only one type of ORs. Thus, OR activation is equivalent, in principle, to the behavior of a stimulated ORN. Although the odor of an odorant is fully encoded within its molecular structure, understanding structure-odor relationships requires decoding the combinatorial activation code of multiple ORs. This represent a fundamental step and major challenge towards our comprehension of odor perception in the brain. Using molecular dynamics simulations, we investigate the molecular mechanism of OR activation in general, as well as as specific OR-odorant recognition in particular. The in silico tool, combined with in vitro and in vivo studies on OR/ORN behavior, is aimed to develop a 'computational nose' that can predict odor perception using merely the molecular structure of an odorant as input. The study will advance current understanding of odor perception, and also benefit GPCR-related pharmaceutical research.

Authors: Xiaojing Cong, Jérôme Golebiowski, H. Matsunami, M. Ma

Pain is a common multi-dimensional experience, but pain mechanisms remain poorly understood. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. Our team use multidisciplinary approaches (animal behavior, in vivo electrophysiology, optogenetics, and neural decoding) to dissect central pain circuits. Neuroimaging studies in humans have suggested that the primary somatosensory cortex (S1) is implicated in sensory-discriminative component of pain, whereas the anterior cingulate cortex (ACC) is involved in the affective-aversive component of pain. The ACC receives nociceptive inputs from the medial thalamus and other cortical regions. Individual ACC neurons can respond to noxious stimuli by increasing firing rates. While previous studies have demonstrated that ACC is necessary and sufficient for the acquisition of stable aversive learning inherent in the chronic pain condition, its role in the aversive response to transient acute pain is less well characterized. Here we use rats to address the specific role of ACC in acute thermal pain. Our results showed that (1) ACC population activities correlate with the intensity of noxious stimuli, and individual ACC neuronal activity can encode pain intensity; (2) We develop state-space methods to reliably detect the onset of acute pain signals based on population spike activity; (3) We use machine learning methods to discriminate different pain intensity using ACC ensemble spike activity; (4) Our optogenetic experiments indicate that ACC neurons can bidirectionally control the aversive response to acute pain. Our ultimate goal is to develop a rodent brain-machine interface system to achieve real-time pain modulation.

Authors: Zhe (Sage) Chen, Jing Wang

Brain-computer interfaces (BCI) translate neural activity into movements of a computer cursor or robotic limb. BCIs are known for their ability to assist paralyzed patients. A lesser known, but increasingly important, use of BCIs is their ability to further our basic scientific understanding of brain function. In particular, BCIs are providing insights into the neural mechanisms underlying sensorimotor control that are currently difficult to obtain using limb movements. A key advantage of BCIs is that they simplify the output interface of the brain without (hopefully) simplifying away the complexities of brain processing that we seek to understand. We will demonstrate this advantage of BCI using two examples from our work. The first involves identifying a network-level explanation for why learning some tasks is easier than others. The second involves the use of internal models identified from neural population activity to explain why subjects make movement errors. These findings deepen our understanding of how neurons interact during learning, and suggest ways to accelerate learning of everyday skills.

Authors: Byron Yu, A. Batista, S. Chase

Volume and complexity of data acquired in neuroscience labs have increased dramatically over the past years. At the same time, scientific progress has become more and more dependent on collaborative efforts, exchange of data, and re-analysis of data. Ensuring that data acquired in the lab stays accessible and that it can be easily shared and re-used – i.e., understood – has become increasingly important, but also challenging. To help scientists deal with these challenges, we are developing methods and tools supporting efficient data management in the laboratory. Key elements are the collection and integration of metadata along with the data, and the consistent representation of data and metadata in open, machine-readable formats. Integrating annotation and organization of data in the laboratory data workflow has the immediate benefit of making data access and analysis efficient and reproducible, and at the same time eliminates the need of additional data preparation for data sharing or publication.

Author: Thomas Wachtler

In this talk Dr. Gnadt will introduce the BRAIN Initiative.

The Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative is part of a new Presidential focus aimed at revolutionizing our understanding of the human brain. By accelerating the development and application of innovative technologies, researchers will be able to produce a revolutionary new dynamic picture of the brain that, for the first time, shows how individual cells and complex neural circuits interact in both time and space. Long desired by researchers seeking new ways to treat, cure, and even prevent brain disorders, this picture will fill major gaps in our current knowledge and provide unprecedented opportunities for exploring exactly how the brain enables the human body to record, process, utilize, store, and retrieve vast quantities of information, all at the speed of thought.

The cortical networks that underlie behavior exhibit an orderly functional organization at local and global scales, an organization that is especially evident in the visual cortex of carnivores and primates. Here, the full range of stimulus orientations is represented in the activity of neighboring columns of neurons contained within a millimeter of cortical surface area, and these responses are shaped by a distributed network that shares similar functional properties and is arranged in an iterated fashion across several millimeters of the cortex. In this study, we use endogenous (spontaneous) cortical activity patterns to explore the fine scale functional architecture of this distributed network and how the coordinated local and global structure of the network arises during development. First, using in vivo imaging of calcium signals in the ferret, we show that in the mature visual cortex, the local structure of orientation columns is accurately predicted by correlations of spontaneous activity with neurons that lie at distances several millimeters away. Next, using chronic in vivo imaging techniques, we show that large scale modular correlation patterns, predictive of the mature organization, are evident at early stages of cortical development, prior to the formation of long range horizontal network connections. The early emergence of long-range modular correlations without long-range connections can be explained by local connections using a neural field model with connectivity consistent with observed local correlation structure. Our results suggest that local connections in early cortical circuits generate structured long-range network correlations that underlie the formation of distributed functional networks.

Authors: Matthias Kaschube, David Fitzpatrick, Bettina Hein, P. Huelsdunk, G. B. Smith, D. E. Whitney

In this talk we report on the two following abstracts:

Abstract 1:

Humans can easily identify visual objects independent of view angles and lighting, individual words in speech independent of volume and pitch, and smells independent of their concentrations. The computational principles and underling neural mechanisms responsible for invariant object recognition remain mostly unknown. Olfaction, being one of the most genetically tractable, anatomically compact and very relevant to rodent behavior sensory system, presents a great opportunity to explore and model such mechanisms.

Here we propose a new computational principle of concentration invariant odor identification based on temporal ranking of receptor activation. We assume that only a small population of first activated receptor neurons is fully responsible for odor identification. To test this model, which we called ‘primacy coding’, we performed optogenetic masking experiments, and demonstrated the relevance of short temporal interval at the beginning of the sniff cycle for odor identification. We propose a computational model of how such code can be read by the cortex. Furthermore we derive implications of primacy coding for the evolution of olfactory receptor genes and bulbar-cortical connectivity.

Abstract 2:

Sensory systems are constantly facing the problem of computing the stimulus identity that is invariant wrt several features. In the olfactory system, for example, odorant percepts have to retain their identity despite substantial variations in concentration, timing, and background. This computation is necessary for us to be able to navigate in chemical gradients or within variable odorant plumes. How can the olfactory system robustly represent odorant identity despite variable stimulus intensity? We propose a novel strategy for the encoding of intensity-invariant stimulus identity that is based on representing relative rather than absolute values of the stimulus features. We propose that, once stimulus features are extracted at the lowest levels of the sensory system, the stimulus identity is inferred on the basis of their relative amplitudes. Because, in this scheme, stimulus identity depends on relative amplitudes of features, identity becomes invariant with respect to variations in intensity and monotonous non-linearities of neuronal responses. For example, in the olfactory system, stimulus identity can be represented by the identities of p strongest responding odorant receptor types out of 1000. We show that this information is sufficient to ensure the robust recovery of a sparse stimulus (odorant) via l1 norm or elastic net loss minimization. Such a minimization has to be performed under the constraints imposed by the relationships between stimulus features. We map this problem onto a dual problem of minimization of a functional of Lagrange multipliers. The dual problem, in turn, can be solved by a neural network whose Lyapunov function represents the dual Lagrangian. We thus propose that the networks in the piriform cortex computing odorant identity implement dual computations with the sparse activities of individual neurons representing the Lagrange multipliers. Our theory yields predictions for the structure of olfactory connectivity.

Authors: Dmitry Rinberg, Alexei Koulakov

Sensory systems are constantly facing the problem of computing the stimulus identity that is invariant wrt several features. In the olfactory system, for example, odorant percepts have to retain their identity despite substantial variations in concentration, timing, and background. This computation is necessary for us to be able to navigate in chemical gradients or within variable odorant plumes. How can the olfactory system robustly represent odorant identity despite variable stimulus intensity? We propose a novel strategy for the encoding of intensity-invariant stimulus identity that is based on representing relative rather than absolute values of the stimulus features. We propose that, once stimulus features are extracted at the lowest levels of the sensory system, the stimulus identity is inferred on the basis of their relative amplitudes. Because, in this scheme, stimulus identity depends on relative amplitudes of features, identity becomes invariant with respect to variations in intensity and monotonous non-linearities of neuronal responses. For example, in the olfactory system, stimulus identity can be represented by the identities of p strongest responding odorant receptor types out of 1000. We show that this information is sufficient to ensure the robust recovery of a sparse stimulus (odorant) via l1 norm or elastic net loss minimization. Such a minimization has to be performed under the constraints imposed by the relationships between stimulus features. We map this problem onto a dual problem of minimization of a functional of Lagrange multipliers. The dual problem, in turn, can be solved by a neural network whose Lyapunov function represents the dual Lagrangian. We thus propose that the networks in the piriform cortex computing odorant identity implement dual computations with the sparse activities of individual neurons representing the Lagrange multipliers. Our theory yields predictions for the structure of olfactory connectivity.

Author: Alexei Koulakov

A problem of fundamental importance in neuroscience is to understand how properties of synaptic connectivity vary across behavior. A possible, but challenging, approach is to interpret fine (millisecond) timescale correlations among populations of spike trains, which can be recorded extracellularly, in such terms. There are many technical barriers to such an approach, including a paucity of ground truth data available for interpretative calibration. Here, we approach the question indirectly from the perspective of biophysical models. Working with simple models of monosynaptic transmission and varieties of background noise, we sought conditions for precise pre- and post-synaptic spike time relationships. A primary conclusion is that, in addition to connectivity parameters, low variability of subthreshold potentials appears to be an important ingredient. We describe nonparametric and semiparametric models for statistical inference of connectivity in this light, and discuss implications in an experimental context.

This is joint work with Jonathan Platkiewicz, Matthew Harrison, and Gyorgy Buzsaki.

Authors: Asohan Amarasingham, J. Platkiewicz, M. Harrison, G. Buzsaki

The cerebellum aids the learning and execution of fast coordinated movements, with acquired information being stored by plasticity of parallel fibre--Purkinje cell synapses. According to the current consensus, erroneously active parallel fibre synapses are depressed by complex spikes arising as climbing fibres signal movement errors. However, this theory cannot solve the credit assignment problem of using the limited information from a global movement evaluation to optimise behaviour by guiding the plasticity in numerous neurones. We identify the possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create an eligibility trace for plasticity and signal resulting error changes to guide plasticity. These error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors, generates specific predictions for synaptic plasticity rules that contradict the current consensus. However, in vitro plasticity experiments under physiological conditions verified our predictions, highlighting the sensitivity of plasticity studies to unphysiological conditions. Using numerical and analytical approaches we demonstrate the convergence and estimate the capacity of learning in our implementation. Finally, a similar mechanism may operate during optimisation of action sequences by the basal ganglia, where dopamine could both initiate movements and signal rewards, analogously to the dual perturbation and correction role of the climbing fibre outlined here.

Authors: Boris Barbour, J.-P. Nadal, G. Bouvier, C. Clopath, C. Bimbard, N. Brunel, V. Hakim

Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, among other features, particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a tendency for stronger synapses to be more stable over time, and a power-law-like distribution of synaptic lifetimes. While previous work has modeled some of these individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these features. In particular, we focus on the distribution of synaptic lifetimes in this model. We note that this distribution is power-law-like, and that its slope remains independent of numerous model parameters, being only strongly affected by the balance between depression and potentiation in STDP. These results suggest that this could provide a measure for the balance between these factors in experimentally observed data. As well, our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed features of recurrent cortical wiring dynamics. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities in various models, suggesting that these mechanisms are central to understanding both structure and function of cortical synaptic wiring.

Authors: Daniel Miner, J. Triesch

A hallmark of the respiratory response to hypercapnia is the emergence of active expiratory pattern in the abdominal motor output (AbN). This pattern consists of late-expiratory (late-E) bursts attributed to the recruitment of expiratory neurons in the parafacial respiratory group (pFRG) by mechanisms not completely understood. It has been previously shown that the pFRG late-E activity is suppressed by pons transection and the consequent inhibition of post-inspiratory (post-I) neurons of the Bötzinger complex. Therefore, pontine areas that control post-I activity may be involved in the control of hypercapnia-induced late-E activity. Here, we explored the role of the Kölliker-Fuse (KF) nucleus in modulating late-E respiratory activity during hypercapnia. Based on model simulations, we proposed that the KF area provides excitatory drive to BötC post-I inhibitory neurons, which, in turn, tonically inhibit the late-E neurons of the pFRG. Our model predicted that during hypercapnia: 1) KF inhibition lower the recruitment threshold of late-E activity; 2) KF excitation prevents emergence of late-E activity. These modeling predictions were tested experimentally using the arterially-perfused in situ rat preparation. We found that: i) KF inhibition with isoguvacine (10mM) advanced by 1s the onset of hypercapnia-induced late-E bursts in AbN; and ii) disinhibition of KF with gabazine (100muM) greatly attenuated AbN late-E activity. The model suggests that KF-driven inhibitory inputs to the pFRG, possibly through the post-I BötC neurons, may determine the presence and/or onset timing of AbN late-E bursts during hypercapnia. 

Authors: Daniel Zoccal, W. Barnett, A. P. Abdala, J. F. R. Paton, Y. Molkov

Humans can easily identify visual objects independent of view angles and lighting, individual words in speech independent of volume and pitch, and smells independent of their concentrations. The computational principles and underling neural mechanisms responsible for invariant object recognition remain mostly unknown. Olfaction, being one of the most genetically tractable, anatomically compact and very relevant to rodent behavior sensory system, presents a great opportunity to explore and model such mechanisms.

Here we propose a new computational principle of concentration invariant odor identification based on temporal ranking of receptor activation. We assume that only a small population of first activated receptor neurons is fully responsible for odor identification. To test this model, which we called ‘primacy coding’, we performed optogenetic masking experiments, and demonstrated the relevance of short temporal interval at the beginning of the sniff cycle for odor identification. We propose a computational model of how such code can be read by the cortex. Furthermore we derive implications of primacy coding for the evolution of olfactory receptor genes and bulbar-cortical connectivity.

Author: Dmitry Rinberg

The project “Model-driven single-neuron studies of cortical remapping in the dorsal and ventral visual streams” aims at investigating the neural circuits linking vision, attention and oculomotor planning by a combination of neuro-computational and experimental neurophysiological studies. We here report the developed neuro-computational model of perisaccadic perception based on predictive remapping and corollary discharge signals. Our proposed model rests on the assumption that parietal areas such as LIP receives two different kinds of eye position information. A proprioceptive information about eye position and a preparatory corollary discharge about the intended saccade displacement. We demonstrate that this model can explain two recently discovered types of perisaccadic spatial attention shifts: (i) attention lingers after saccade at the (irrelevant) retinotopic position, that is, the focus of attention shifts with the eyes and updates not before the eyes land to its original position (Golomb et al., 2008, J Neurosci.; Golomb et al., 2010, J Vis.). (ii) shortly before saccade onset, spatial attention is remapped to a position opposite to the saccade direction, thus, anticipating the eye movement (Rolfs et al., 2011, Nat Neurosci.). We show that both observations are not contradictory and emerge through the model dynamics. The former is explained by the proprioceptive eye position signal and the latter by the corollary discharge signal. Interestingly, both eye-related signals are core ingredients of the model and are required to explain data from mislocalization and displacement detection experiments. Thus, our model provides a comprehensive framework to discuss multiple experimental observations that occur around saccades.

Authors: Fred Hamker, J. Bergelt, J. Mazer

Dendritic spines are highly plastic and may partake in learning and memory storage processes governed at the ultra-structural level. In order to gain a quantitative understanding of the influence of the spine’s micro-scale architecture on the electro-chemical signals transferred to dendrites, we are developing a numerical framework to solve an electro-diffusion model described by the Poisson-Nernst-Planck (PNP) equations. Computational complexity is addressed by introducing a novel hybrid-dimensional discretization approach and developing specialized numerical methods based on Finite Volume discretization and geometric multigrid solvers. Ultra-structural reconstructions generated by our collaborators (A. Herz, D. Patirniche, M. Stemmler, LMU Munich, Germany) from 3D electron microscopy images (M. Ellisman, UCSD, USA) can be integrated into the numerical simulation framework in order to investigate the interplay between ultra-structural morphology on electro-chemical signals.

Authors: Gillian Queisser, Markus Breit, D. Patirniche, A. V. M. Herz

Centrifugal feedback projections from higher to lower brain areas are pervasive in the mammalian brain. They can provide the receiving brain area with information that is not available in the feedforward stream and thereby modify the information processing by the lower brain area in a task-dependent manner. These functions depend essentially on the network connectivity, but it is only poorly understood what controls its formation and specificity.

The rodent olfactory system is well suited to investigate the mechanisms controlling the evolution of network connectivity and functionalities arising from it. Even in adult animals the olfactory bulb, which directly receives the sensory information from the nose, is persistently rewired through the addition and controlled removal of granule cells (GCs), which comprise the bulb's dominant interneuron population, and through highly dynamic fluctuations of the dendritic spines connecting the inhibitory GCs with the bulb's principal neurons, mitral/tufted cells (MC/TC).

We developed computational models for the network evolution via spine fluctuations and adult neurogenesis of the GCs. Our modeling shows that Hebbian-type spine stability can explain the experimentally observed increase in spine stability with spine age. Our neurogenesis model focuses on the role of centrifugal projections from olfactory cortex onto GCs. The experimentally supported activity-dependence of GC survival leads to the formation of subnetworks that provide bidirectional connections between the bulbar and the cortical representations of learned stimuli. The resulting specificity of cortical inhibition of MC/TC via GCs allows, e.g., context-enhanced discrimination of occluded stimuli and cortical switching of bulbar processing.

Authors: Hermann Riecke, Kurt A. Sailor, Pierre-Marie Lledo, M. Wiechert

We present a new algorithm, functional connectivity hyperalignment, for building a common model of representational and connectivity spaces in the human cortex. Basing hyperalignment on functional connectivity makes it possible to hyperalign brains based on fMRI data obtained in the resting state as well as during viewing of a naturalistic movie. We show its application to both movie data and resting state data from the Human Connectome Project. Functional connectivity hyperalignment affords increases in intersubject correlation of representational geometry and between-subject multivariate pattern classification that approach that of time-series hyperalignment. Increases in intersubject correlation of functional connectivity vectors exceeds those obtained with time-series hyperalignment. Analysis of the spatial point spread function for connectivity vectors reveals a fine-grained structure with a spatial scale of a single voxel.

Authors: J. S. Guntipalli, James V. Haxby

Odors activate large populations of olfactory bulb mitral cells. These cells project to piriform cortex where they activate sparse and distributed cortical odor ensembles. We obtained simultaneous recordings from large populations of mitral cells and piriform cortex cells in awake, head-fixed mice to understand how the dense odor responses in bulb are transformed in cortex. Individual piriform cells could be activated or suppressed by different odors and a linear classifier could accurately decode odors from the population using single sniff-spike counts, indicating that these representations are reliable and robust. At the population level, odors evoked a sustained increase in net bulb output. By contrast, there was almost no net change in total cortical output, with a small, brief increase in spiking immediately followed by sustained suppression. We developed a spiking network olfactory bulb-piriform cortex model to reveal the origins of this transformation. In the model, odors activated distinct sets of glomeruli and associated mitral cells at specific phases throughout the sniff cycle. In piriform cortex, a small subset of pyramidal cells were activated by the earliest bulb inputs, which helped recruit other pyramidal cells that only received subthreshold bulb input through their recurrent collateral connections. However, this ramping cascade of cortical activity then recruits strong feedback inhibition that suppresses cortical spiking and discounts the impact of later bulb input. Thus, using a combination of experimental and computational approaches, we provide a mechanistic explanation for how the sparse cortical odor responses are largely defined by the earliest activated bulb inputs.

Author: Kevin M. Franks

We present an automated solution for detecting bad trials in magneto-/electroencephalography (M/EEG). Bad trials are commonly identified using peak-to-peak rejection thresholds that are set manually. This work proposes a solution to determine them automatically using cross-validation. We show that automatically selected rejection thresholds perform at par with manual thresholds, which can save hours of visual data inspection. We then use this automated approach to learn a sensor-specific rejection threshold.

Finally, we use this approach to remove trials with finer precision and/or partially repair them using interpolation. We illustrate the performance on three public datasets. The method clearly performs better than a competitive benchmark on a 19-subject Faces dataset.

Authors: Mainak Jas, D. Engemann, Y. Bekhti, A. Gramfort

Neuronal transmitters are released via the fusion of synaptic vesicles with the plasma membrane. Vesicles dock to the membrane via a protein complex termed SNARE, which contains membrane attached (t-SNARE) and vesicle attached (v-SNARE) proteins. The fusion occurs in response to a calcium inflow, and the vesicle protein Synaptotagmin (Syt) serves as a calcium sensor. A cytosolic protein Complexin (Cpx) interacts with the SNARE complex, restricting the spontaneous fusion. Although molecular interactions of these proteins have been extensively studied, it is still debated how the fusion proteins dynamically interact with each other and with lipid bilayers to trigger lipid merging. To elucidate this mechanism, we combined molecular dynamics (MD) simulations of Syt interacting with the SNARE complex, Cpx, and lipid bilayers and genetic approaches in Drosophila. Basing on MD simulations, we created a model of the molecular “fusion clamp” wherein Cpx dynamically interacts with v-SNARE, preventing full SNARE assembly. The model enabled us to predict new mutations in v-SNARE and Cpx that enhance Cpx ability to inhibit spontaneous fusion. To understand how Syt regulates Ca2+ dependent fusion, we employed MD simulations to investigate Syt conformational ensemble and its dependence on Ca2+ binding. Basing on the results of a genetic screen that revealed new loss-of-function mutations in Syt, we developed a model of the pre-fusion protein complex, including Syt interacting with the SNARE bundle, Cpx, and lipid bilayers. This model creates the basis for a systematic approach to manipulating the fusion machinery based on theoretical predictions derived from MD simulations.

Authors: Maria Bykhovskaia, A. Jagota, J. T. Littleton

Legged locomotion involves various gaits. It has been observed that fast running animals (e.g. cockroaches) employ the tripod gait (three legs lifted off the ground simultaneously) while slow walking animals (e.g. stick insects) use the tetrapod gait (only two legs lifted off the ground simultaneously). Fruit flies use both gaits over a typical speed range. Central pattern generators (CPGs) are networks of neurons in invertebrate thoracic ganglia, responsible for generating locomotive activities. In this work, we use bursting neuron models of CPGs to study the effect of stepping frequency on the transitions from tetrapod gaits at low speed to tripod gaits at higher speeds. To this end, we first derive 6-phase oscillator equations, each corresponding to a neural network controlling one leg. Then by assuming that the left legs maintain a half period phase difference with the right legs (bilateral symmetry), we reduce the 6 equations to 3 equations. Finally, we consider a dynamical system with 2 phase differences defined on a torus. We show that bifurcations occur from multiple stable tetrapod gaits to a unique stable tripod gait as speed increases. We discuss these results in relation to those of Yeldesbay, Tóth, and Daun on a model of stick insect gait transitions.

Authors: Z. Aminzare, V. Srivastava, Philip Holmes

Cortical sensory responses are highly variable. This variability can be correlated across neurons (due to some combination of dense intracortical connectivity, cortical activity level, and cortical state), with fundamental implications for population coding. Yet the interpretation of correlated response variability (or “noise correlation”) has remained fraught with difficulty, in part because of the restriction to extracellular neuronal spike recordings. Here, we measured response variability and its correlation at the most microscopic level of electrical neural activity, the membrane potential, by obtaining dual whole-cell recordings from pairs of cortical pyramidal neurons during visual processing. We found that during visual stimulation, correlated variability adapts towards an intermediate level and that this correlation dynamic is mediated by intracortical mechanisms. A model network with external inputs, synaptic depression, and structure reproduced the observed dynamics of correlated variability. These results establish that intracortical adaptation self-organizes cortical circuits towards a balanced regime at which network coordination maintains an intermediate level.

Authors: Ralf Wessel, N. Wright, M. Hoseini

What aspects of previous experiences guide decisions? Much research concerns how the brain computes the average, over many experiences, of rewards received for an option. But such a summary – produced by prominent models of dopaminergic incremental learning– is chiefly useful for repetitive tasks. Much less is understood about how the brain can flexibly evaluate new or changing options in more realistic tasks, which must rely on less aggregated information.

We describe studies which examine how decisions for reward can be guided by the most individuated memories, those for individual experiences or episodes. We combine novel behavioral methods with brain imaging and computational modeling to examine the relationship between hippocampal episodic memory and striatal incremental learning in decisions for reward.

Our findings suggest that both forms of learning guide decisions but that their relative contribution varies across trials as a function of the certainty of reward in the environment. Neurally, we identify both common and dissociable brain regions for how these two forms of learning guide choice. Notably, choices based on both forms of learning are related to BOLD activity in the hippocampus, raising the possibility that episodic retrieval may guide both forms of decisions. Computationally, episodic memories can support a family of learning algorithms that draw on sparse, individual experiences, such as Monte Carlo and kernel methods. These suggest novel, plausible hypotheses for how the brain solves more realistic decision problems based on rare events, and in particular how it implements a sort of reasoning known as "goal-directed" or "model-based' choice.

Authors: Raphael T. Gerraty, Daphna Shohamy, Nathaniel Daw, K. Duncan

Advancing methods to non-invasively image and interpret neural activity in humans on fine temporal and spatial scales is essential to understanding how the brain works. Magneto-EIectroencephalography (MEG/EEG) combined with structural imaging provides reliable recordings of cortical activity with millisecond precision. Simultaneous recordings from subcortical structures, such as thalamus, have been limited due to low signal amplitudes and difficulty in source localization. Further, our understanding of the generation of the macroscopic electrical currents producing these signals from cellular events is lacking. We address these issues by integrating MEG/EEG, computational neural modeling, and invasive electrophysiology in animals and human patients to optimize MEG/EEG inverse methods to localize distributed thalamocortical sources and to interpret the underlying cellular events.

We present recent results applying our multi-modal approach to study commonly measured somatosensory evoked responses and low frequency rhythms. We show how our new inverse methods enable improved localization of distributed current sources evoked across thalamus and cortex. We also describe development of a biophysically principled neural model uniquely designed to study the circuit mechanisms underlying MEG/EEG measured source activity. This model has led to novel hypothesis on the thalamocortical mechanisms underlying spontaneous low frequency rhythms and the impact of these rhythms on sensory processing. Invasive laminar recordings in animal models, and ECoG recordings in human patients, support the model derived predictions and suggest the mechanisms underlying these rhythms are preserved across recording modalities and species. Lastly, we describe ongoing experiments and tools that are being developed to share freely with the broader neuroscience community.

Authors: Stephanie Jones, Alexandre Gramfort, Y. Bekhti, J. Guerin, M. Jas, R. Law, S. Lee, P. Sundaram, M. Hamalainen, W. Asaad

Our project studies how rats use their whiskers (vibrissae) to sense air currents and the implications this sensory ability has for their behavior and neural processing. We have addressed the broader question of whisker-air interactions using three approaches: behavioral studies of rats, bio-inspired whiskered robotics, and complementary experiments and numerical simulations of the fluid dynamics of whisker-air interactions.

Nearly all eutherian mammals have facial whiskers arranged on the cheek in an ordered array of rows and columns. To date, most studies of sensing in the rodent vibrissal-trigeminal system have examined tactile stimulation in which the whisker comes into direct contact with an object. However, even when not in contact with a solid object, the vibrissae are immersed in a fluid medium - air - whose velocity fluctuations are continually transmitting momentum, and thus force, to the whisker. Thus, whisker-air interactions are likely to be an important component of vibrissal sensing that has helped shape both rodent behavior and brain evolution.

Recent publications from our project have quantified the mechanical response of isolated whiskers to airflow (Yu et al., 2016a) and also shown that rats can use their whiskers to track airflow (Yu et al., 2016b). In a parallel exploration of whisker functionality, we constructed a robot that uses four whiskers to steer towards the source of an air current using control signals derived from the fluctuations of the whiskers in the airstream. When the whisker signals were used as inputs to a neural network, the system could accurately recognize “flowscapes,” or local flow environments. Our poster presents our latest behavioral, robotic, experimental and simulation results and discusses the advances we have made in how whiskers can sense flow.

Authors: V. Gopal, A. Beverage, M. Genovese, I. Ghouse, Y. Yu, M. Graff, C. Bresee, Y. Man, W. Kou, S. Park, N. Patankar, M. Hartmann

Spike-timing dependent plasticity (STDP) is a universal rule of synaptic plasticity. According to this rule, positively correlated pre- and postsynaptic activity leads to long-term synaptic potentiation (LTP) whereas negative correlation induces long-term synaptic depression (LTD). As most plasticity involves signaling by intracellular calcium, the extracellular calcium concentration is likely to play a critical role. Yet, most if not all in vitro studies devoted so far to STDP used non-physiological Ca2+ concentration (2-3 mM) whereas the physiological Ca2+ concentration is approx. 1.5 mM. Theoretical models of STDP based on calcium dynamics indicate that different types of STDP curves can be obtained as a function of calcium (Graupner & Brunel, 2012). In particular, with lower calcium concentration, only LTD occurs. CA1 pyramidal neurons were recorded in whole-cell patch-clamp in acute slices of young rat hippocampus and STDP was examined at the Schaffer to CA1 pyramidal cell-synapse in physiological extracellular Ca2+ concentration (1.3/1.8 mM). Confirming theoretical predictions, no change was observed with 1.3 mM Ca2+ and only LTD could be induced by positive or negative correlation in 1.8 mM Ca2+. LTP could be restored if the number of post-synaptic spikes was increased from 1 to 3 or 4. Furthermore, LTP could be induced with a single post-synaptic spike when the pairing was performed in the presence of the beta-adrenergic agonist isoprenaline. These data suggest that the STDP rule is altered in physiological Ca2+ but that a normal STDP profile can be restored under specific activity regimes or by application of a neuromodulator.

Authors: Yanis Inglebert, J. Aljadeff, N. Brunel, D. Debanne

Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. We demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate in the standard domain (irMxNE) or in the Time-Frequency domain (irTF-MxNE). Source localization in the time-frequency domain has already been investigated using Gabor dictionary, however the choice of an optimal dictionary remains unsolved. Due to a mixture of signals, i.e. short transient signals (right after the stimulus onset) and slower brain waves, the choice of a single dictionary explaining simultaneously both signals types in a sparse way is difficult. We then introduce a method to improve the source estimation relying on a multi-scale dictionary, i.e. multiple dictionaries with different scales concatenated to fit short transients and slow waves at the same time. We demonstrate the benefits of the approach in terms of reduced leakage, temporal smoothness and detection of both signals types.

Authors: Yousra Bekhti, D. Strohmeier, M. Jas, R. Badeau, A. Gramfort

The signal attenuation in magnetic resonance (MR) diffusion experiments is sensitive not only to the random motion of water molecules in tissues, diffusion, but also to the pseudo-random flow through the vascular network, Intravoxel Incoherent Motion (IVIM). Traditionally the IVIM signal decay has been modeled as a mono-exponential function from which microcirculatory perfusion parameters can be extracted. In this work we propose a bi-exponential model to analyze the IVIM imaging data. Through experiments and numerical simulations we demonstrate the validity of this model and show that it provides a more complete description of the cerebral vasculature reflecting the presence of two separate vascular pools, corresponding to two different flow regimes. By evaluating the influence of the experimental parameters on the model outputs we aim to establish the optimum protocols for IVIM cerebral perfusion measurements. Specifically, we show that the fast flowing pool is harder to detect at long diffusion encoding times for which the bi- and mono-exponential models converge. On the contrary, employing spin echo acquisitions with short repetition times enhances the contribution to the IVIM signal of the fast flowing spins facilitating the differentiation and characterization of the two vascular pools. While introduced and validated in a preclinical setting the bi-exponential model we propose has potential clinical applications, as lesions or responses to therapy are likely to be characterized by a differential balance between the fast and slow vascular pools.

Author: Luisa Ciobanu

Widening economic inequity is a key concern for our society, and previous cohort-based studies have also suggested a link between economic inequity and depression. However, little is known about the underlying neural mechanism of the link, due to substantial individual differences. Here, we demonstrate that functional magnetic resonance imaging activity patterns in the amygdala/hippocampus induced by the inequity during an economic game can predict both present and future (measured one year later) depression index (BDI-II). Such predictions were not possible by behavioral measures of the participants. These findings reveal the connection between the sensitivity of the amygdala/hippocampus to economic inequity, and the present and future depression index, and suggest an important involvement of the amygdala and hippocampus into the effect of economic inequity on human mental states.

Author: Masahiko Haruno

I will first discuss how since the birth of AI two traditions have always been very active, labelled for simplicity “conscious and unconscious”, then how and why the second one, based on Big Data and Machine Learning, is dangerously taking the lead today. Thus I will discuss what more recent researches in neural networks such as deep learning, self-adapting nets and chaotic Hopfield networks can bring to neurosciences.

In this talk we report on the following abstracts:

Abstract 1:

Throughout adulthood the mouse olfactory bulb is repopulated with new granule cells (GC) neurons through a process termed adult neurogenesis. It is unknown whether this persistent remodeling can potentially drive synaptic plasticity, as the pre-existing circuitry needs to adapt to allow new neuron integration. In this study, we tracked the dendritic and spine development of adult-born GCs using 2-photon in vivo imaging. Overall, adult born GC dendritic structure stabilized at one month. In contrast, adult born GC distal apical dendritic spine dynamics plateaued at two months, but remained highly dynamic. We compared the spine dynamics of the adult born GCs with pre-existing, early-postnatal born GCs and found matching spine turnover. To test whether the GC spine dynamics correlated with synapse turnover of their synaptic partners, mitral/tufted cells (MC), we labeled mitral/tufted cells with fluorescently labeled gephryn, a GABA receptor structural protein. In vivo imaging of the gephryn puncta demonstrated dynamics matching GC spines. Using a computational model of GC-MC structural plasticity, we found that these dynamics enable the network to rapidly optimize its output in response to changes in the odor environment. Surprisingly, odor representations quickly settled into a steady state despite continued rapid remodeling of the circuit. GC to mitral/tufted cell synapses have so far been demonstrated to lack synaptic strength plasticity, therefore the olfactory bulb appears to be unique in the adult brain, where persistent structural remodeling plasticity may be its dominant form of plasticity.

Abstract 2:

Centrifugal feedback projections from higher to lower brain areas are pervasive in the mammalian brain. They can provide the receiving brain area with information that is not available in the feedforward stream and thereby modify the information processing by the lower brain area in a task-dependent manner. These functions depend essentially on the network connectivity, but it is only poorly understood what controls its formation and specificity. The rodent olfactory system is well suited to investigate the mechanisms controlling the evolution of network connectivity and functionalities arising from it. Even in adult animals the olfactory bulb, which directly receives the sensory information from the nose, is persistently rewired through the addition and controlled removal of granule cells (GCs), which comprise the bulb's dominant interneuron population, and through highly dynamic fluctuations of the dendritic spines connecting the inhibitory GCs with the bulb's principal neurons, mitral/tufted cells (MC/TC). We developed computational models for the network evolution via spine fluctuations and adult neurogenesis of the GCs. Our modeling shows that Hebbian-type spine stability can explain the experimentally observed increase in spine stability with spine age. Our neurogenesis model focuses on the role of centrifugal projections from olfactory cortex onto GCs. The experimentally supported activity-dependence of GC survival leads to the formation of subnetworks that provide bidirectional connections between the bulbar and the cortical representations of learned stimuli. The resulting specificity of cortical inhibition of MC/TC via GCs allows, e.g., context-enhanced discrimination of occluded stimuli and cortical switching of bulbar processing.

Authors: Kurt A. Sailor, Hermann Riecke, Pierre-Marie Lledo, M. T. Valley, M. T. Wiechert, G. J. Sun, H. Song

Midbrain dopaminergic (DA) neurons display two distinct patterns of spiking: low frequency tonic spiking and short burst-like episodes of high-frequency spiking. Synaptic inputs are suggested to be responsible for the bursts, but the mechanisms are not clear. In order to explore this, we model the circuitry of the ventral tegmental area (VTA) and analyzed the influence of synaptic inputs on the model. We find that the burstiness (the number of spikes in bursts) is drastically reduced when NMDA, but not AMPA receptors are blocked. When DA and GABAergic neurons are combined to simulate the local circuitry of the VTA, we find that high-frequency firing can be facilitated by the interaction between these two types of neurons. In particular, when GABA inputs are synchronized, the GABAa receptor currents contribute to high-frequency firing of the DA neuron by augmenting the fast AHP. When the GABA inputs are desynchronized, they only inhibit the DA neuron, and a burst emerges at the offset of these inputs, according to the disinhibition mechanism. Thus, the study suggests a combination of NMDA, AMPA and GABA receptor activation that contributes most significantly to the bursting pattern of the VTA DA neurons. These mechanisms are explored in the context of the action of alcohol and salient environmental stimuli.

Authors: Alexey Kuznetsov, Boris Gutkin, C. Lapish

Can a sensory stimulus be learned from an animal’s behavioral response? How are different behaviors organized and controlled by an animal? We study this question in the context of thermal escape response in the worm, C. elegans, a model for nociception. Here we develop a model of the stimulus-behavior relation to infer the perceived level of thermal nociception from the worm's stereotyped escape response. We analyze the behavior of ibuprofen-treated worms and a TRPV (transient receptor potential) mutant, and show that the perception of the thermal signal for the ibuprofen treated worms is lower than the wild-type, demonstrating a method for quantification of pharmacological effects. At the same time, our model shows that the mutant changes the worm's behavior beyond affecting sensory transduction. Thus the model also provides a method of assigning behavioral changes to the sensory or the motor system. Further, we use the recently developed algorithm called Sir Isaac for automated inference of the dynamical system underlying the escape behavior. The algorithm discovers that the behavior is approximated well by having one hidden degree of freedom beyond the observed worm velocity. This degree of freedom integrates the thermal signal and uses it through intertwined negative and positive feedback loops to initiate and stabilize the escape. According to the inferred model, the phase space of the worm’s behavior has two attractors, corresponding to forward and escape motions. The sensory stimulus affects the stability of these attractors, destroying the forward attractor and forcing the escape for large stimuli levels.

Authors: William Ryu, K. Leung, A. Mohammadi, B. C. Daniels, I. Nemenman

Odors activate large populations of olfactory bulb mitral cells. These cells project to piriform cortex where they activate sparse and distributed cortical odor ensembles. We obtained simultaneous recordings from large populations of mitral cells and piriform cortex cells in awake, head-fixed mice to understand how the dense odor responses in bulb are transformed in cortex. Individual piriform cells could be activated or suppressed by different odors and a linear classifier could accurately decode odors from the population using single sniff-spike counts, indicating that these representations are reliable and robust. At the population level, odors evoked a sustained increase in net bulb output. By contrast, there was almost no net change in total cortical output, with a small, brief increase in spiking immediately followed by sustained suppression. We developed a spiking network olfactory bulb-piriform cortex model to reveal the origins of this transformation. In the model, odors activated distinct sets of glomeruli and associated mitral cells at specific phases throughout the sniff cycle. In piriform cortex, a small subset of pyramidal cells were activated by the earliest bulb inputs, which helped recruit other pyramidal cells that only received subthreshold bulb input through their recurrent collateral connections. However, this ramping cascade of cortical activity then recruits strong feedback inhibition that suppresses cortical spiking and discounts the impact of later bulb input. Thus, using a combination of experimental and computational approaches, we provide a mechanistic explanation for how the sparse cortical odor responses are largely defined by the earliest activated bulb inputs.

Authors: Kevin M. Franks, Alexander Fleischmann

In this collaborative US-German project with Leibniz Institute for Neurobiology (LIN) experiments have been conducted using a well-established rodent learning model, the two-way active avoidance paradigm in the shuttle box, to analyze neuronal signatures of auditory discrimination learning in Mongolian gerbils implanted with an array of 5x4 electrodes over the auditory cortex. Our experiments confirm previous results on sudden behavioral change from the initial naive state to the avoidance state as learning progresses using CS+ and CS- stimuli.

• We defined various causality metrics, including the “inverse causality interaction” (ICI) index, to quantify the relationship between channel pairs before and after the animal acquired successful discrimination.
• We found that the number of channel pairs collected based on the ICI criterion is significantly increased after the animals transitioned to the avoidance stage.
• Spatial patterns between some electrode pairs emerged after the transition to the avoidance stage, indicating the formation of localized connectivity patterns between cortical areas as the result of the learning process.
• We have evaluated for classification performance of electrode pairs based on ICI index and demonstrated statistically significant discrimination between CS+ and CS- conditions after the transition to avoidance behavior. To investigate learning effects in the cortex, we used a simple graph theory model with coupled excitatory-inhibitory units and having rewiring effect (small-world properties).
• We studied the effect of the following parameters: proportion of inhibitory nodes, probability of long-range (non-local) connections, and intrinsic noise level.
• Our modeling study shows learning-induced changes in the discrimination properties can be reproduced by the graph theory model suggesting that connectivity changes may be responsible the observed behavioral transitions from naïve to avoidance state in gerbils.

Our findings underscore the importance of functional reorganization in early sensory areas and provide ISI as a possible neural correlate to behavioral changes.

We illustrate that in integrative neuroscience, computational modeling is tightly integrated with experimental data, and that this integration is very powerful to answer a number of problems in neuroscience. A first area where there is such a tight model-experiment association is about the study of single-neuron dynamics, and more generally, the integrative properties of single neurons. This subject requires three steps:

1. to measure from in vivo experiments the characteristics of neuronal activity such as conductance and sub-threshold dynamics, as well as spiking activity;
2. to design computational models of these dynamics, and predict how single neurons function in such states;
3. to integrate the models into in vitro experiments in order to test predictions and better understand the integrative properties of the neurons.

A second example of such tight experiments-theory interaction is to study network-level phenomena, such as the emergence of oscillations in the brain. Similar to above, the oscillations must first be characterized in vivo, then integrated into computational models which can generate predictions that can be tested in vitro.

We illustrate this interaction for the case of absence epileptic seizures in the brain. Such problems require a tight combination of in vivo and in vitro experiments, and computational models play the link between them.

Finally, we illustrate another example, where computational models are used to link between experiments and the design of dedicated "neuro-morphic" circuits. In this case, the biological experiments provide a number of principles, which are formulated by models, and later integrated by engineers into the design of electronic circuits integrating these principles. Such an approach is supported by EC-funded projects such as BrainScales and The Human Brain Project.

Neuroimaging includes a large portfolio of techniques capable of investigating the brain at different spatial scales. Combining the various measurements in order to understand how microscopic characteristics give rise to macroscopic behaviors is a major challenge of modern neuroscience. From a medical point of view, putting together results from different neuroimaging studies in order to formulate medical diagnoses is often one of the most difficult tasks faced by physicians.

This workshop working group will identify difficulties encountered when bringing together specific neuroimaging methods such as functional MRI, microscopic MR, PET and optical imaging from the point of view of signal modeling and data analysis. The discussions will be initiated by leading experts who will present advantages, disadvantages and limitations of different brain imaging technologies.