Richard Naud

发表

How Good Are Neuron Models?

Wulfram Gerstner, Richard Naud, W. Gerstner, 2009, Science.

Temporal whitening by power-law adaptation in neocortical neurons

W. Gerstner, Richard Naud, Christian Pozzorini, 2013, Nature Neuroscience.

Speed-invariant encoding of looming object distance requires power law spike rate adaptation

L. Maler, A. Longtin, Richard Naud, 2013, Proceedings of the National Academy of Sciences.

Full title: Counting on dis-inhibition: a circuit motif for interval counting and selectivity

G. Rose, A. Longtin, Richard Naud, 2015 .

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Blake A. Richards, Richard Naud, Anisha Goel, 2021, Neuroscience.

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms.

W. Gerstner, C. Petersen, Richard Naud, 2012, Journal of neurophysiology.

Noise Gated by Dendrosomatic Interactions Increases Information Transmission

A. Longtin, Richard Naud, A. Payeur, 2017, bioRxiv.

Perirhinal input to neocortical layer 1 controls learning

M. Larkum, M. Brecht, Naoya Takahashi, 2019, Science.

Modeling sound pulse counting in inferior colliculus

G. Rose, A. Longtin, Richard Naud, 2014, BMC Neuroscience.

Perirhinal input to neocortical layer 1 controls learning

M. Larkum, M. Brecht, Naoya Takahashi, 2020, Science.

Input-Driven Spiking in Fast-Spiking Cells Inhibition-Controlled Switch Between Intrinsic and Somatosensory Cortical Inhibitory Interneurons: An Integration of Broadband Conductance Input in Rat

Charles J. Wilson, W. Gerstner, C. Petersen, 2015 .

Noise Gated by Dendrosomatic Interactions Increases Information Transmission

A. Longtin, Richard Naud, A. Payeur, 2017, bioRxiv.

Can We Predict Every Spike

W. Gerstner, Richard Naud, 2013 .

Spike timing prediction with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier, 2013, 1311.3586.

Linking demyelination to compound action potential dispersion with a spike-diffuse-spike approach

A. Longtin, Richard Naud, R. Naud, 2018, The Journal of Mathematical Neuroscience.

Linking Demyelination to Compound Action Potential Dispersion with a Spike-Diffuse-Spike Approach

A. Longtin, Richard Naud, 2019 .

Spike-Diffuse-Spike Modelling of Axonal Propagation Links Demyelination to Compound Action Potential Dispersion

A. Longtin, Richard Naud, 2018, bioRxiv.

Neuronal Dynamics: Variability of spike trains and neural codes

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Neuronal Dynamics: Preface

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Encoding and decoding with stochastic neuron models

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

GENERALIZED INTEGRATE-AND-FIRE NEURONS

Wulfram Gerstner, Richard Naud, Werner M. Kistler, 2014 .

A ternary neural code resolves error and sharpening signals

Blake A. Richards, M. Larkum, Richard Naud, 2022, bioRxiv.

Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework

A. Longtin, Richard Naud, J. Béïque, 2021, bioRxiv.

1A Manuscript 22.8.9 FINAL

S. Haj-Dahmane, Richard Naud, J. Béïque, 2022 .

Dynamics of interacting finite-sized networks of spiking neurons with adaptation

W. Gerstner, Richard Naud, T. Schwalger, 2013 .

Dendritic excitability primarily controls overdispersion

Richard Naud, Zachary Friedenberger, 2022, bioRxiv.

Voltage Recordings and Genetic Algorithms Constraining Compartmental Models Using Multiple

Wulfram Gerstner, Noam Peled, Alon Korngreen, 2015 .

Editorial Manager(tm) for Biological Cybernetics Manuscript Draft Title: the Quantitative Single-neuron Modeling Competition the Quantitative Single-neuron Modeling Competition

Thomas K. Berger, Arnd, W. Gerstner, 2022 .

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Richard Naud, Alexandre Payeur, Friedemann Zenke, 2020, Nature Neuroscience.

Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Wulfram Gerstner, Christof Koch, Skander Mensi, 2015, PLoS Comput. Biol..

Cell-type-specific responses to associative learning in the primary motor cortex

Simon X. Chen, Richard Naud, Emerson F. Harkin, 2022, eLife.

Cell-Type Specific Responses to Associative Learning in the Primary Motor Cortex

Simon X. Chen, Richard Naud, Emerson F. Harkin, 2021, bioRxiv.

Firing patterns in the adaptive exponential integrate-and-fire model

Wulfram Gerstner, Claudia Clopath, Richard Naud, 2008, Biological Cybernetics.

Counting on dis-inhibition: a circuit motif for interval counting and selectivity in the anuran auditory system.

André Longtin, Richard Naud, Dave Houtman, 2015, Journal of neurophysiology.

Sparse bursts optimize information transmission in a multiplexed neural code

Richard Naud, Henning Sprekeler, Henning Sprekeler, 2018, Proceedings of the National Academy of Sciences.

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Henning Sprekeler, Richard Naud, F. Vercruysse, 2021, bioRxiv.

Parsing Out the Variability of Transmission at Central Synapses Using Optical Quantal Analysis

André Longtin, Richard Naud, Cary Soares, 2019, bioRxiv.

From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models

Wulfram Gerstner, Skander Mensi, Richard Naud, 2011, NIPS.

Wulfram Gerstner, Felipe Gerhard, Skander Mensi, 2011, Neural Computation.

Silences, spikes and bursts: Three‐part knot of the neural code

Richard Naud, K. Tóth, Emerson F. Harkin, 2023, The Journal of physiology.

A deep learning framework for neuroscience

Surya Ganguli, Yoshua Bengio, Claudia Clopath, 2019, Nature Neuroscience.

Spike-timing prediction in cortical neurons with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier, 2014, Front. Comput. Neurosci..

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Wulfram Gerstner, Richard Naud, W. Gerstner, 2012, PLoS Comput. Biol..

A User's Guide to Generalized Integrate-and-Fire Models.

Richard Naud, J. Béïque, Emerson F. Harkin, 2022, Advances in experimental medicine and biology.

The quantitative single-neuron modeling competition

Wulfram Gerstner, Thomas K. Berger, Richard Naud, 2008, Biological Cybernetics.

A benchmark test for a quantitative assessment of simple neuron models

Wulfram Gerstner, Richard Naud, Ryota Kobayashi, 2008, Journal of Neuroscience Methods.

Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

Wulfram Gerstner, Richard Naud, Tilo Schwalger, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

Neuronal Model Reduction

Richard Naud, Richard Naud, 2014, Encyclopedia of Computational Neuroscience.

Classes of dendritic information processing

Richard Naud, Alexandre Payeur, Jean-Claude Béïque, 2019, Current Opinion in Neurobiology.

Linear-nonlinear cascades capture synaptic dynamics

Richard Naud, K. Tóth, Daniel Trotter, 2020, bioRxiv.

The Performance (and Limits) of Simple Neuron Models: Generalizations of the Leaky Integrate-and-Fire Model

Wulfram Gerstner, Richard Naud, W. Gerstner, 2012 .

Particle tracking with application to the trafficking of synaptic receptors

Richard. Naud, Richard Naud, 2006 .

Linking demyelination to compound action potential dispersion with a spike-diffuse-spike approach

A. Longtin, Richard Naud, 2018, Journal of mathematical neuroscience.

Functional Subtypes of Synaptic Dynamics in Mouse and Human

Richard Naud, K. Tóth, Julian Rossbroich, 2023, bioRxiv.

A Synthetic Likelihood Solution to the Silent Synapse Estimation Problem.

Richard Naud, Kevin F H Lee, Cary Soares, 2020, Cell reports.

A synthetic likelihood solution to the silent synapse estimation problem

Richard Naud, Cary Soares, Jean-Claude Béïque, 2019, bioRxiv.

Sparse Bursts Optimize Information Transmission in a Multiplexed Neural Code

Richard Naud, Henning Sprekeler, Henning Sprekeler, 2017, bioRxiv.

Connecting levels of analysis in the computational era

A. Longtin, Richard Naud, R. Naud, 2023, 2305.06037.

Cellular Substrate of Eligibility Traces

Richard Naud, J. Béïque, Léa Caya-Bissonnette, 2023, bioRxiv.

Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package

Richard Naud, Jean-Claude Béïque, Michael Lynn, 2020, STAR protocols.

Synaptic dynamics as convolutional units

Richard Naud, Katalin Tóth, Julian Rossbroich, 2020 .

Neural burst codes disguised as rate codes

A. Gidon, Richard Naud, A. Payeur, 2021, Scientific Reports.

Capsule Deep Generative Model That Forms Parse Trees

Richard Naud, Yifeng Li, Pengcheng Xi, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Dendritic Mechanisms for In Vivo Neural Computations and Behavior

Lukas F Fischer, Brendan A. Bicknell, Christine Grienberger, 2022, The Journal of Neuroscience.

Channel Inactivation + Spider Mechanoreceptor Neurons: The Role of Na Simulation of Different Firing Patterns in Paired

A. S. French, L. Maler, A. Longtin, 2015 .

How Good Are Neuron Models?

W. Gerstner, Richard Naud, R. Naud, 2009, Science.

Serotonin predictively encodes value

Richard Naud, J. Béïque, Cooper D. Grossman, 2023, bioRxiv.

Inferring plasticity rules from single-neuron spike trains using deep learning methods

Richard Naud, J. Béïque, Xingyun Wang, 2023, bioRxiv.

Linear-nonlinear cascades capture synaptic dynamics

Richard Naud, Julian Rossbroich, John Beninger, 2014, bioRxiv.

Fast burst fraction transients convey information independent of the firing rate

M. Larkum, Richard Naud, J. Béïque, 2024, bioRxiv.