Literature DB >> 19219040

Reinforcement learning in populations of spiking neurons.

Robert Urbanczik1, Walter Senn.   

Abstract

Population coding is widely regarded as an important mechanism for achieving reliable behavioral responses despite neuronal variability. However, standard reinforcement learning slows down with increasing population size, as the global reward signal becomes less and less related to the performance of any single neuron. We found that learning speeds up with increasing population size if, in addition to global reward, feedback about the population response modulates synaptic plasticity.

Mesh:

Year:  2009        PMID: 19219040     DOI: 10.1038/nn.2264

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


  11 in total

Review 1.  Neuromodulation, development and synaptic plasticity.

Authors:  R C Foehring; N M Lorenzon
Journal:  Can J Exp Psychol       Date:  1999-03

Review 2.  Information processing with population codes.

Authors:  A Pouget; P Dayan; R Zemel
Journal:  Nat Rev Neurosci       Date:  2000-11       Impact factor: 34.870

3.  Learning in spiking neural networks by reinforcement of stochastic synaptic transmission.

Authors:  H Sebastian Seung
Journal:  Neuron       Date:  2003-12-18       Impact factor: 17.173

4.  Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning.

Authors:  Jean-Pascal Pfister; Taro Toyoizumi; David Barber; Wulfram Gerstner
Journal:  Neural Comput       Date:  2006-06       Impact factor: 2.026

5.  Gradient learning in spiking neural networks by dynamic perturbation of conductances.

Authors:  Ila R Fiete; H Sebastian Seung
Journal:  Phys Rev Lett       Date:  2006-07-28       Impact factor: 9.161

6.  Solving the distal reward problem through linkage of STDP and dopamine signaling.

Authors:  Eugene M Izhikevich
Journal:  Cereb Cortex       Date:  2007-01-13       Impact factor: 5.357

7.  Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.

Authors:  Răzvan V Florian
Journal:  Neural Comput       Date:  2007-06       Impact factor: 2.026

8.  The tempotron: a neuron that learns spike timing-based decisions.

Authors:  Robert Gütig; Haim Sompolinsky
Journal:  Nat Neurosci       Date:  2006-02-12       Impact factor: 24.884

9.  Single neurons in the monkey hippocampus and learning of new associations.

Authors:  Sylvia Wirth; Marianna Yanike; Loren M Frank; Anne C Smith; Emery N Brown; Wendy A Suzuki
Journal:  Science       Date:  2003-06-06       Impact factor: 47.728

Review 10.  Dopamine, acetylcholine and nitric oxide systems interact to induce corticostriatal synaptic plasticity.

Authors:  Diego Centonze; Paolo Gubellini; Antonio Pisani; Giorgio Bernardi; Paolo Calabresi
Journal:  Rev Neurosci       Date:  2003       Impact factor: 4.353

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  36 in total

1.  A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice.

Authors:  Brice Bathellier; Sui Poh Tee; Christina Hrovat; Simon Rumpel
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-19       Impact factor: 11.205

2.  Prefrontal Neurons Encode a Solution to the Credit-Assignment Problem.

Authors:  Wael F Asaad; Peter M Lauro; János A Perge; Emad N Eskandar
Journal:  J Neurosci       Date:  2017-06-20       Impact factor: 6.167

3.  Striatal action-value neurons reconsidered.

Authors:  Lotem Elber-Dorozko; Yonatan Loewenstein
Journal:  Elife       Date:  2018-05-31       Impact factor: 8.140

4.  Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information.

Authors:  Iñigo Arandia-Romero; Seiji Tanabe; Jan Drugowitsch; Adam Kohn; Rubén Moreno-Bote
Journal:  Neuron       Date:  2016-02-25       Impact factor: 17.173

5.  Supervised learning with decision margins in pools of spiking neurons.

Authors:  Charlotte Le Mouel; Kenneth D Harris; Pierre Yger
Journal:  J Comput Neurosci       Date:  2014-05-28       Impact factor: 1.621

6.  Towards deep learning with segregated dendrites.

Authors:  Jordan Guerguiev; Timothy P Lillicrap; Blake A Richards
Journal:  Elife       Date:  2017-12-05       Impact factor: 8.140

7.  Spike-based decision learning of Nash equilibria in two-player games.

Authors:  Johannes Friedrich; Walter Senn
Journal:  PLoS Comput Biol       Date:  2012-09-27       Impact factor: 4.475

Review 8.  Control of synaptic plasticity in deep cortical networks.

Authors:  Pieter R Roelfsema; Anthony Holtmaat
Journal:  Nat Rev Neurosci       Date:  2018-02-16       Impact factor: 34.870

9.  Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail.

Authors:  Eleni Vasilaki; Nicolas Frémaux; Robert Urbanczik; Walter Senn; Wulfram Gerstner
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

10.  A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

Authors:  David Kappel; Robert Legenstein; Stefan Habenschuss; Michael Hsieh; Wolfgang Maass
Journal:  eNeuro       Date:  2018-04-24
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