| Summary: | Despite the ubiquitous presence of recurrent synaptic connections insensory neuronal systems, their general functional purpose is not wellunderstood. A recent conceptual advance has been achieved by theoriesof reservoir computing in which recurrent networks have been proposedto generate short-term memory as well as to improve neuronalrepresentation of the sensory input for subsequent computations.Here, we present a numerical study on the distinct effects ofinhibitory and excitatory recurrence in a canonical linearclassification task. It is found that both types of coupling improvethe ability to discriminate temporal spike patterns as compared to apurely feed-forward system, although in different ways. For a largeclass of inhibitory networks, the network's performance is optimal aslong as a fraction of roughly 50% of neurons per stimulus is activein the resulting population code. Thereby the contribution of inactiveneurons to the neural code is found to be even more informative thanthat of the active neurons, generating an inherent robustness ofclassification performance against temporal jitter of the inputspikes. Excitatory couplings are found to not only produce ashort-term memory buffer but also to improve linear separability ofthe population patterns by evoking more irregular firing as comparedto the purely inhibitory case. As the excitatory connectivity becomesmore sparse, firing becomes more variable and pattern separabilityimproves. We argue that the proposed paradigm is particularlywell-suited as a conceptual framework for processing of sensoryinformation in the auditory pathway.
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