Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs

Robert R Kerr,Doreen A Thomas,David B Grayden,Matthieu Gilson,Anthony N Burkitt,Abigail Morrison

doi:10.1371/journal.pcbi.1002897

Robert R Kerr, Doreen A Thomas + Show 4 more

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https://doi.org/10.1371/journal.pcbi.1002897

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Abstract

Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.

Highlights

Spike-timing-dependent plasticity (STDP) is an experimentally observed learning rule that changes synaptic strengths based on the relative timing of pre- and post-synaptic spikes [1,2,3,4]
We use analytical and numerical methods to investigate the changes to the network structure caused by a specific learning rule during oscillatory neural activity
We find the conditions under which connections with propagation delays that resonate with the oscillations are strengthened relative to the other connections

Summary

Introduction

Spike-timing-dependent plasticity (STDP) is an experimentally observed learning rule that changes synaptic strengths based on the relative timing of pre- and post-synaptic spikes (action potentials) [1,2,3,4]. Gerstner et al first proposed it as an unsupervised Hebbian learning rule that could select feed-forward connections with specific axonal delays [5]. They showed that it could be used to achieve the high degree of temporal coherence that had been observed at frequencies of up to 8 kHz in the auditory brainstem of barn owls. This finding explained how a network could learn to perform sound localization using the time lag between the neural signals from the two ears. STDP has the ability to encode temporal correlations in neuronal activity, such as oscillations, into the functional structure of networks of neurons that have axonal and dendritic propagation delays

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