RT Journal Article
SR Electronic
T1 Matching Recall and Storage in Sequence Learning with Spiking Neural Networks
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 9565
OP 9575
DO 10.1523/JNEUROSCI.4098-12.2013
VO 33
IS 23
A1 Brea, Johanni
A1 Senn, Walter
A1 Pfister, Jean-Pascal
YR 2013
UL http://www.jneurosci.org/content/33/23/9565.abstract
AB Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.