synaptic plasticity; short-term plasticity; long-term plasticity; sequence learning; spiking neuron model; recurrent network
Blom Sigrid, Pfister Jean-Pascal, Senn Walter, Nevian Thomas (2014), Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex, in Journal of Neuroscience
, 34(17), 5754-5764.
Surace Simone Carlo, Pfister Jean-Pascal (2013), Adaptive Gaussian Poisson process: a model for in vivo neuronal dy- namics, in Cosyne Abstracts 2013, Salt Lake City USA.
Brea Johanni, Senn Walter, Pfister Jean-Pascal (2013), Learning Activity Patterns in Recurrent Networks of Visible and Hidden Spiking Neurons, in Cosyne Abstracts 2013, Salt Lake City USA.
Brea Johanni, Senn Walter, Pfister Jean-Pascal (2013), Matching recall and storage in sequence learning with spiking neural networks., in The Journal of neuroscience : the official journal of the Society for Neuroscience
, 33(23), 9565-75.
Pfister Jean-Pascal (2011), A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations., in PNAS
, 108(48), 19383-19388.
Brea Johanni, Senn Walter, Pfister Jean-Pascal (2011), Sequence learning with hidden units in spiking neural networks, in Advances in Neural Information Processing Systems
, 24, 1422-1430.
Surace Simone, Pfister Jean-Pascal, Normative theory of STP predicts a link between short-term facilitation and refractoriness, in Cosyne Abstracts 2014, Salt Lake City USA.
Senn Walter, Pfister Jean-Pascal, Reinforcement Learning in Cortical Networks, in Encyclopedia of Computational Neuroscience
Learning and memory are the crucial processes by which our past experiences influence our present decisions. Despite their fundamental importance, we do not yet fully understand the behavior of synapses (the connections between neurons) which are widely believed to underlie learning and memory. Indeed synapses are highly dynamical elements with time constants from the millisecond range up to the hour or days range. Another source of complexity is that the biophysical processes underlying synaptic dynamics are mediated by more than hundred different proteins. Instead of studying synaptic dynamics from a bottom-up (biophysical) perspective, we will follow a top-down (normative) approach which has encountered an increasing success during the last 50 years. Indeed, this approach seems better suited if we want to better understand the functions of synaptic transmission and plasticity and not get lost in a mass of irrelevant details. Here, we propose two different projects that strive to understand the computational principles that govern synaptic plasticity across multiple time scales.The first project (that will be carried out by the PhD student) aims to link short-term plasticity and long-term plasticity. We will start from a normative model of short-term plasticity that we recently developed. This model sees short-term plasticity as an optimal (Bayesian) estimator of the presynaptic membrane potential. We will first validate this model with in vivo data that will be collected in Hahnloser's lab in Zurich. Then we will extend it in two different directions that will both include long-term aspects. First, we will relax the assumption that the synapse knows a priori the statistics of the presynaptic membrane potential. As a consequence the synapse must learn, at a slower time scale, the parameters describing the presynaptic dynamics. The second extension will be to assume that the task of the synapse is to estimate a (potentially non-linear) function of both the presynaptic membrane potential and the long-term weight. As a consequence, in this framework, the induction of long-term potentiation or long-term depression influences the short-term properties of the synapse. This formalism leads to directly testable predictions on the interaction between short-term and long-term plasticity.The second project aims to develop a normative model for temporal sequence learning in spiking recurrent networks. Indeed, several brain systems need to be able to store and recall precise sequence of spikes. For example, the juvenile songbirds learn a song from their fathers and reproduce it for the rest of their lives. As a consequence, juvenile songbirds must learn a stereotypical spiking pattern that will eventually produce the father's song. Here we will address this problem from a general perspective. Instead of learning a specific spiking temporal sequence, the recurrent network will learn a distribution of sequences. We will also derive the conditions under which the recall dynamics is stable. Finally, we will calculate the storage capacity of such networks.