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Learning and memory in the brain are implemented by complex, time-varyingchanges in neural circuitry. The computational rules according to whichsynaptic weights change over time are the subject of much research, and are notprecisely understood. Until recently, limitations in experimental methods havemade it challenging to test hypotheses about synaptic plasticity on a largescale. However, as such data become available and these barriers are lifted, itbecomes necessary to develop analysis techniques to validate plasticity models.Here, we present a highly extensible framework for modeling arbitrary synapticplasticity rules on spike train data in populations of interconnected neurons.We treat synaptic weights as a (potentially nonlinear) dynamical systemembedded in a fully-Bayesian generalized linear model (GLM). In addition, weprovide an algorithm for inferring synaptic weight trajectories alongside theparameters of the GLM and of the learning rules. Using this method, we performmodel comparison of two proposed variants of the well-knownspike-timing-dependent plasticity (STDP) rule, where nonlinear effects play asubstantial role. On synthetic data generated from the biophysical simulatorNEURON, we show that we can recover the weight trajectories, the pattern ofconnectivity, and the underlying learning rules.

A framework for studying synaptic plasticity with neural spike train data