computational neuroscience; spiking neurons; learning; reinforcement learning; decision making; perceptual learning; psychophysics; memory; behavior; neurons; synaptic plasticity
Aberg KC, Herzog MH (2012), Different types of feedback change decision criterion and sensitivity differently in perceptual learning, in JOURNAL OF VISION
, 12(3), 1-11.
Tartaglia EM, Bamert L, Herzog MH, Mast FW (2012), Perceptual learning of motion discrimination by mental imagery, in JOURNAL OF VISION
, 12(6), 1-10.
Kompella VR, Luciw M, Schmidhuber J (2012), Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams, in NEURAL COMPUTATION
, 24(11), 2994-3024.
Senn Walter, Friedrich J. (2012), Spike-based Decision Learning of Nash Equilibria in Two-Player Games, in PLoS Comput Biol.
, 8(9), e1002691-e1002691.
Castro JE, Diessler S, Varea E, Marquez C, Larsen MH, Cordero MI, Sandi C (2012), Personality traits in rats predict vulnerability and resilience to developing stress-induced depression-like behaviors, HPA axis hyper-reactivity and brain changes in pERK1/2 activity, in PSYCHONEUROENDOCRINOLOGY
, 37(8), 1209-1223.
Aberg KC, Herzog MH (2012), About similar characteristics of visual perceptual learning and LTP, in VISION RESEARCH
, 61, 100-106.
Herzog MH, Aberg KC, Fremaux N, Gerstner W, Sprekeler H (2012), Perceptual learning, roving and the unsupervised bias, in VISION RESEARCH
, 61, 95-99.
Bisaz R, Sandi C (2012), Vulnerability of conditional NCAM-deficient mice to develop stress-induced behavioral alterations, in STRESS-THE INTERNATIONAL JOURNAL ON THE BIOLOGY OF STRESS
, 15(2), 195-206.
Ruter J, Marcille N, Sprekeler H, Gerstner W, Herzog MH (2012), Paradoxical Evidence Integration in Rapid Decision Processes, in PLOS COMPUTATIONAL BIOLOGY
, 8(2), 1-10.
Schiess Mathieu, Urbanczik Robert, Senn Walter (2012), Gradient estimation in dendritic reinforcement learning, in The Journal of Mathematical Neuroscience
, 2(2), 1-19.
Cuccu G, Luciw M, Schmidhuber J, Gomez F (2011), Intrinsically Motivated NeuroEvolution for Vision-Based Reinforcement Learning, in 2011 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING (ICDL)
Jimenez Rezende Danilo, Wierstra Daan, Gerstner Wulfram (2011), Variational Learning for Recurrent Spiking Networks, in NIPS 2011 Proceedings
Timmer M, Cordero MI, Sevelinges Y, Sandi C (2011), Evidence for a Role of Oxytocin Receptors in the Long-Term Establishment of Dominance Hierarchies, in NEUROPSYCHOPHARMACOLOGY
, 36(11), 2349-2356.
van der Kooij MA, Sandi C. (2011), Social memories in rodents: Methods, mechanisms and modulation by stress, in Neurosci Biobehav Rev
, 36(7), 1762-1773.
Kraev I, Henneberger C, Rossetti C, Conboy L, Kohler LB, Fantin M, Jennings A, Venero C, Popov V, Rusakov D, Stewart MG, Bock E, Berezin V, Sandi C (2011), A Peptide Mimetic Targeting Trans-Homophilic NCAM Binding Sites Promotes Spatial Learning and Neural Plasticity in the Hippocampus, in PLOS ONE
, 6(8), 1-13.
Luksys G, Sandi C (2011), Neural mechanisms and computations underlying stress effects on learning and memory, in CURRENT OPINION IN NEUROBIOLOGY
, 21(3), 502-508.
Friedrich J, Urbanczik R, Senn W (2011), Spatio-Temporal Credit Assignment in Neuronal Population Learning, in PLOS COMPUTATIONAL BIOLOGY
, 7(6), 1-13.
Sandi C (2011), Glucocorticoids act on glutamatergic pathways to affect memory processes, in TRENDS IN NEUROSCIENCES
, 34(4), 165-176.
Wiskott Laurenz, Berkes Pietro, Franzius Mathias, Sprekeler Henning, Wilbert Niko (2011), Slow Feature Analysis, in Scholarpedia
, 6(4), 5282-5282.
Toledo-Rodriguez M., Sandi C. (2011), Stress during Adolescence Increases Novelty Seeking and Risk-Taking Behavior in Male and Female Rats, in Front Behav Neurosci
, 5(17), 1-10.
Aberg KC, Herzog MH (2010), Does Perceptual Learning Suffer from Retrograde Interference?, in PLOS ONE
, 5(12), 1-6.
Fremaux N, Sprekeler H, Gerstner W (2010), Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity, in JOURNAL OF NEUROSCIENCE
, 30(40), 13326-13337.
Salehi B., Cordero MI., Sandi C. (2010), Learning under stress: the inverted-U-shape function revisited, in Learn Mem
, 17(10), 522-530.
Friedrich J, Urbanczik R, Senn W (2010), Learning Spike-Based Population Codes by Reward and Population Feedback, in NEURAL COMPUTATION
, 22(7), 1698-1717.
Vasilaki E, Fremaux N, Urbanczik R, Senn W, Gerstner W (2009), Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail, in PLOS COMPUTATIONAL BIOLOGY
, 5(12), 1-17.
Sprekeler Henning, Hennequin Guillaume, Gerstner Wulfram (2009), Code-Specific Policy-Gradient Rules for Spiking Neurons, in Advances in Neural Information Processing Systems
, 22, 1741-1749.
Aberg KC, Herzog MH (2009), Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult, in VISION RESEARCH
, 49(21), 2591-2598.
Luksys G, Gerstner W, Sandi C (2009), Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning, in NATURE NEUROSCIENCE
, 12(9), 1180-1180.
Tartaglia E., Aberg K.C, Herzog M.H. (2009), Modeling perceptual learning: Why mice do not play backgammon, in Learning & Perception
, 1(1), 155-163.
Urbanczik R, Senn W (2009), Reinforcement learning in populations of spiking neurons, in NATURE NEUROSCIENCE
, 12(3), 250-252.
Kneissler J., Urbankczik R., Senn W., Code-specific synaptic plasticity improves learning, in The Journal of Neuroscience
Clarke J., Friedrich A., Senn W., Tartaglia E., Mechesotti S., Herzog M., Human learning in non-Markovian decision making, in PLoS Computation Biology
Reward-based learning encompasses a broad class of algorithms in the field of machine learning that allow to optimize the behavior of an agent (e.g. of a real or simulated robot) so as to maximize the total expected reward. These algorithms describe learning in machines that is reminiscent of learning in animals or humans as studied in animal behavior (e.g. conditioning) or human psychophysics. Learning in humans or animals in turn is thought to be related to changes in synaptic connections between neurons in the brain. Hence the question arises whether models of synaptic plasticity on the level of spiking neurons can be connected to formal `reinforcement' learning models in machine learning and to human psychophysics and animal behavior.This project combines the expertise from two laboratories in computational neuroscience (EPFL-LCN/Wulfram Gerstner and Univ. Berne/Walter Senn) who have both previously worked on spike-based models of synaptic plasticity, with the machine learning expertise of the Schmidhuber group at IDSIA (Lugano) who have a long-standing track record in formal models of reinforcement learning, with the psychophysics laboratory of Michael Herzog (EPFL-LPSY) who has a long tradition in human vision and perceptual learning, and with the rodent behavior expertise of Carmen Sandi (EPFL-BMI).