Search Our Scientific Publications & Authors

PLoS Comput Biol 2021 Mar 2;17(3):e1007957. Epub 2021 Mar 2.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. Read More

Neural Comput 2021 Feb 22:1-29. Epub 2021 Feb 22.

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, U.S.A.

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. Read More

Epilepsia 2021 02 16;62(2):542-556. Epub 2021 Jan 16.

Medical Faculty, Institute for Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany.

Objective: Many antiseizure drugs (ASDs) act on voltage-dependent sodium channels, and the molecular basis of these effects is well established. In contrast, how ASDs act on the level of neuronal networks is much less understood.

Methods: In the present study, we determined the effects of eslicarbazepine (S-Lic) on different types of inhibitory neurons, as well as inhibitory motifs. Read More

J Neurosci 2020 12 9;40(50):9634-9649. Epub 2020 Nov 9.

Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, OX1 3SR, United Kingdom.

Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Read More

PLoS Comput Biol 2020 08 7;16(8):e1007659. Epub 2020 Aug 7.

The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America.

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. Read More

Front Comput Neurosci 2020 30;14:55. Epub 2020 Jun 30.

Center for Computational Biology, Flatiron Institute, New York, NY, United States.

Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for weighted summation of upstream neural activity in the soma and Hebbian/anti-Hebbian synaptic learning rules. However, synaptic plasticity in biological neurons often depends on the integration of synaptic currents over a dendritic compartment rather than total current in the soma. Read More

Cereb Cortex 2020 Jun;30(8):4381-4401

Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS UMR7241, INSERM U1050, PSL Research University, Paris, 75005, France.

The striatum integrates inputs from the cortex and thalamus, which display concomitant or sequential activity. The striatum assists in forming memory, with acquisition of the behavioral repertoire being associated with corticostriatal (CS) plasticity. The literature has mainly focused on that CS plasticity, and little remains known about thalamostriatal (TS) plasticity rules or CS and TS plasticity interactions. Read More

Sci Rep 2019 12 19;9(1):19451. Epub 2019 Dec 19.

Team Dynamic and Pathophysiology of Neuronal Networks, Center for Interdisciplinary Research in Biology, College de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, Paris, France.

Behavioural experience, such as environmental enrichment (EE), induces long-term effects on learning and memory. Learning can be assessed with the Hebbian paradigm, such as spike-timing-dependent plasticity (STDP), which relies on the timing of neuronal activity on either side of the synapse. Although EE is known to control neuronal excitability and consequently spike timing, whether EE shapes STDP remains unknown. Read More

Phys Rev Lett 2019 Aug;123(7):078101

RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan.

Hebbian learning of excitatory synapses plays a central role in storing activity patterns in associative memory models. Interstimulus Hebbian learning associates multiple items by converting temporal correlation to spatial correlation between attractors. Growing evidence suggests the importance of inhibitory plasticity in memory processing, but the consequence of such regulation in associative memory has not been understood. Read More

IEEE Trans Neural Netw Learn Syst 2020 06 31;31(6):2118-2128. Epub 2019 Jul 31.

This paper addresses the biological plausibility of both backpropagation (BP) and contrastive Hebbian learning (CHL) used in the Boltzmann machines. The main claim of this paper is that CHL is a general learning algorithm that can be used to steer feedforward networks toward desirable outcomes, and steer them away from undesirable outcomes without any need for the specialized feedback circuit of BP or the symmetric connections used by the Boltzmann machines. After adding perturbations during the learning phase to all the neurons in the network, multiple feedforward outcomes are classified into Hebbian and anti-Hebbian sets based on the network predictions. Read More

Neural Comput 2019 08 1;31(8):1519-1550. Epub 2019 Jul 1.

Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720, U.S.A.

Place cells in the hippocampus (HC) are active when an animal visits a certain location (referred to as a place field) within an environment. Grid cells in the medial entorhinal cortex (MEC) respond at multiple locations, with firing fields that form a periodic and hexagonal tiling of the environment. The joint activity of grid and place cell populations, as a function of location, forms a neural code for space. Read More

Front Neurosci 2019 19;13:189. Epub 2019 Mar 19.

Department of ECE, Purdue University, West Lafayette, IN, United States.

In this work, we propose ReStoCNet, a residual stochastic multilayer convolutional Spiking Neural Network (SNN) composed of binary kernels, to reduce the synaptic memory footprint and enhance the computational efficiency of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input layer followed by stacked convolutional layers for hierarchical input feature extraction, pooling layers for dimensionality reduction, and fully-connected layer for inference. In addition, we introduce residual connections between the stacked convolutional layers to improve the hierarchical feature learning capability of deep SNNs. Read More

Front Cell Neurosci 2019 15;13:98. Epub 2019 Mar 15.

Department of Biochemistry, Microbiology and Bio-informatics, Faculty of Science and Engineering, Neuroscience Axis, CHU de Québec Research Center (CHUL), Laval University, Québec, PQ, Canada.

Hippocampal inhibitory interneurons exhibit a large diversity of dendritic Ca mechanisms that are involved in the induction of Hebbian and anti-Hebbian synaptic plasticity. High resolution imaging techniques allowed examining somatic Ca signals and, accordingly, the recruitment of hippocampal interneurons in awake behaving animals. However, little is still known about dendritic Ca activity in interneurons during different behavioral states. Read More

Front Neural Circuits 2018 14;12:120. Epub 2019 Jan 14.

Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.

Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al. Read More

Nat Commun 2018 10 2;9(1):4046. Epub 2018 Oct 2.

Department of Bioengineering and the Helen Wills Neuroscience Institute, University of California-Berkeley, Berkeley, CA, 94708, USA.

Three-dimensional (3D) spatial cells in the mammalian hippocampal formation are believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here we present a hierarchical network model for the formation of 3D spatial cells using anti-Hebbian network. Read More

Front Comput Neurosci 2018 10;12:52. Epub 2018 Jul 10.

Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. Read More

Front Cell Neurosci 2018 5;12:182. Epub 2018 Jul 5.

Center for Interdisciplinary Research in Biology (CIRB), College de France, INSERM U1050, CNRS UMR7241, Paris Sciences et Lettres Research University, Paris, France.

Synaptic efficacy changes, long-term potentiation (LTP) and depression (LTD), underlie various forms of learning and memory. Synaptic plasticity is generally assessed under prolonged activation, whereas learning can emerge from few or even a single trial. Here, we investigated the existence of rapid responsiveness of synaptic plasticity in response to a few number of spikes, in neocortex in a synaptic Hebbian learning rule, the spike-timing-dependent plasticity (STDP). Read More

Neural Netw 2018 Oct 2;106:50-66. Epub 2018 Jul 2.

Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 42411, Republic of Korea. Electronic address:

We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Read More

Phys Rev E 2018 Apr;97(4-1):042301

CNR-Institute of Complex Systems, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Florence, Italy and Embassy of Italy in Israel, Trade Tower, 25 Hamered St., 68125 Tel Aviv, Israel.

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. Read More

Eur J Neurosci 2018 05 16;47(10):1266-1281. Epub 2018 Apr 16.

Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India.

Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the Velocity Driven Oscillatory Network (VDON) and Locomotor Driven Oscillatory Network. Read More

Sci Rep 2018 03 8;8(1):4193. Epub 2018 Mar 8.

Laboratório de Neurociência Experimental e Computacional (LANEC), Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), São João del-Rei, Brazil.

The role of GABAergic neurotransmission on epileptogenesis has been the subject of speculation according to different approaches. However, it is a very complex task to specifically consider the action of the GABAa neurotransmitter, which, in its dependence on the intracellular level of Cl, can change its effect from inhibitory to excitatory. We have developed a computational model that represents the dentate gyrus and is composed of three different populations of neurons (granule cells, interneurons and mossy cells) that are mutually interconnected. Read More

Cereb Cortex 2018 04;28(4):1396-1415

School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, 1015 Lausanne EPFL, Switzerland.

Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of spine volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of structural and spike-timing-dependent plasticity that explains the multicontact configuration of synapses in adult neocortical networks under steady-state and lesion-induced conditions. Read More

Neural Comput 2018 01 28;30(1):84-124. Epub 2017 Sep 28.

Center for Computational Biology, Flatiron Institute, New York, NY 10010, U.S.A., and NYU Langone Medical Center, New York 10016, U.S.A.

Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet derivations of single-layer networks with such local learning rules from principled optimization objectives became possible only recently, with the introduction of similarity matching objectives. What explains the success of similarity matching objectives in deriving neural networks with local learning rules? Here, using dimensionality reduction as an example, we introduce several variable substitutions that illuminate the success of similarity matching. Read More

Sci Rep 2017 07 18;7(1):5671. Epub 2017 Jul 18.

Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Spike-timing-dependent plasticity (STDP) is considered critical to learning and memory functions in the human brain. Across various types of synapse, STDP is observed as different profiles of Hebbian and anti-Hebbian learning rules. However, the specific roles of diverse STDP profiles in memory formation still remain elusive. Read More

Neural Comput 2017 09 9;29(9):2528-2552. Epub 2017 Jun 9.

Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130, U.S.A.

We consider the problem of optimizing information-theoretic quantities in recurrent networks via synaptic learning. In contrast to feedforward networks, the recurrence presents a key challenge insofar as an optimal learning rule must aggregate the joint distribution of the whole network. This challenge, in particular, makes a local policy (i. Read More

J Neurosci 2017 06 5;37(24):5798-5808. Epub 2017 May 5.

Research Institute on Addictions and

Prenatal ethanol exposure (PE) leads to increased addiction risk which could be mediated by enhanced excitatory synaptic strength in ventral tegmental area (VTA) dopamine (DA) neurons. Previous studies have shown that PE enhances excitatory synaptic strength by facilitating an anti-Hebbian form of long-term potentiation (LTP). In this study, we investigated the effect of PE on endocannabinoid-mediated long-term depression (eCB-LTD) in VTA DA neurons. Read More

Neuropharmacology 2017 Jul 10;121:261-277. Epub 2017 Apr 10.

Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex Paris, France. Electronic address:

Activity-dependent long-term potentiation (LTP) and depression (LTD) of synaptic strength underlie multiple forms of learning and memory. Spike-timing-dependent plasticity (STDP) has been described as a Hebbian synaptic learning rule that could account for experience-dependent changes in neural networks, but little is known about whether and how STDP evolves during development. We previously showed that GABAergic signaling governs STDP polarity and thus operates as a Hebbian/anti-Hebbian switch in the striatum. Read More

Nat Commun 2017 03 10;8:14566. Epub 2017 Mar 10.

Department of Bio and Brain Engineering, Program in Brain and Cognitive Engineering, KAIST, Daejeon 34141, Republic of Korea.

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. Read More

J Neurophysiol 2017 03 21;117(3):1229-1238. Epub 2016 Dec 21.

Kresge Hearing Research Institute, Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan;

Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with -methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. Read More

Eur J Neurosci 2017 04 2;45(8):1032-1043. Epub 2016 Aug 2.

Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Philippstr. 13, 10115, Berlin, Germany.

Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Read More