Publications associées à Bursting

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, James Gonzalo King, Rajnish Ranjan, Armando Romani, Mustafa Anil Tuncel, Aleksandra Zuzanna Teska, Polina Litvak, Matthias Wolf, Felix Schürmann, Daniel Keller, Werner Alfons Hilda Van Geit, Pramod Shivaji Kumbhar, Alexis Arnaudon, Jean-Denis Georges Emile Courcol, Giuseppe Chindemi, András Ecker, Michael Emiel Gevaert, Christian Andreas Rössert, Vishal Sood, Sirio Bolaños Puchet, James Bryden Isbister, Judit Planas Carbonell, Daniela Egas Santander, Omar Awile, Maria Reva, Joni Henrikki Herttuainen, Genrich Ivaska, Natali Barros Zulaica, Samuel Lieven D. Lapere, Christoph Pokorny, Elvis Boci, Thomas Brice Delemontex, Jorge Blanco Alonso, Tanguy Pierre Louis Damart, Alexander Dietz, Darshan Mandge, ,

Taming neuronal noise with large networks

Valentin Marc Schmutz

How does reliable computation emerge from networks of noisy neurons? While individual neurons are intrinsically noisy, the collective dynamics of populations of neurons taken as a whole can be almost deterministic, supporting the hypothesis that, in the br ...

EPFL2022

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Tilo Schwalger, Valentin Marc Schmutz

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...

PUBLIC LIBRARY SCIENCE2022

Approximating Relu Networks By Single-Spike Computation

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Evangelos Eleftheriou, Giovanni Cherubini, Angeliki Pantazi

Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

IEEE2022

A spiking central pattern generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards

Auke Ijspeert, Jonathan Patrick Arreguit O'Neill, Axel von Arnim, Emmanouil Angelidis

Central pattern generator (CPG) models have long been used to investigate both the neural mechanisms that underlie animal locomotion, as well as for robotic research. In this work we propose a spiking central pattern generator (SCPG) neural network and its ...

Iop Publishing Ltd2021

Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

James Bryden Isbister

How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further c ...

Nature Research2021

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

Marc-Oliver Gewaltig

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous i ...

SPRINGER2021

Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons

Tilo Schwalger

The macroscopic dynamics of large populations of neurons can be mathematically analyzed using low-dimensional firing-rate or neural-mass models. However, these models fail to capture spike synchronization effects and nonstationary responses of the populati ...

AMER PHYSICAL SOC2020

Receptor-induced transient responses in cells with oscillatory actin dynamics

Jose Negrete Jr

Living cells adjust their sensing and migratory machinery in response to changes in their environment. In this work, we show that cells of the social amoeba Dictyostelium discoideum modulate the dynamical state of their actin cytoskeleton in response to an ...

AMER PHYSICAL SOC2020

Somatostatin enhances visual processing and perception by suppressing excitatory inputs to parvalbumin-positive interneurons in V1

Carl Petersen, Graham Knott, Catherine Maclachlan, Seung-Hee Lee

Somatostatin (SST) is a neuropeptide expressed in a major subtype of GABAergic interneurons in the cortex. Despite abundant expression of SST and its receptors, their modulatory function in cortical processing remains unclear. Here, we found that SST appli ...

2020

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventio ...

Nature Research2020

A biomimetic electrical stimulation strategy to induce asynchronous stochastic neural activity

Silvestro Micera, Stéphanie Lacour, Edoardo D'Anna, Emanuele Formento, Sandra Gribi

Objective.Electrical stimulation is an effective method for artificially modulating the activity of the nervous system. However, current stimulation paradigms fail to reproduce the stochastic and asynchronous properties of natural neural activity. Here, we ...

2020

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Wulfram Gerstner, Tilo Schwalger, Valentin Marc Schmutz

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static s ...

2020

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Tilo Schwalger, Samuel Pavio Muscinelli

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

2019

Advances in understanding neural mechanisms of social dominance

Maria del Carmen Sandi Perez

Dominance hierarchy profoundly impacts social animals' survival, physical and mental health and reproductive success. As the measurements of dominance hierarchy in rodents become established, it is now possible to understand the neural mechanism mediating ...

2018

Layer-Dependent Short-Term Synaptic Plasticity Between Excitatory Neurons in the C2 Barrel Column of Mouse Primary Somatosensory Cortex

Carl Petersen, Sandrine Lefort

Neurons process information through spatiotemporal integration of synaptic input. Synaptic transmission between any given pair of neurons is typically a dynamic process with presynaptic action potentials (APs) evoking depressing or facilitating postsynapti ...

Oxford Univ Press Inc2017

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

Wulfram Gerstner, Tilo Schwalger, Moritz Deger

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...

Public Library of Science2017

Neuromorphic System with Phase-Change Synapses for Pattern Learning and Feature Extraction

Yusuf Leblebici, Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Angeliki Pantazi

Neuromorphic systems provide biologically inspired methods of computing, alternative to the classical von Neumann approach. In these systems, computation is performed by a network of spiking neurons controlled by the values of their synaptic weights, which ...

2017

Continuous attractor working memory and provenance of channel models

Alexander Kevin Seeholzer

The brain is a complex biological system composed of a multitude of microscopic processes, which together give rise to computational abilities observed in everyday behavior. Neuronal modeling, consisting of models of single neurons and neuronal networks at ...

EPFL2017

Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Angeliki Pantazi

Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication b ...

2016