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Recurrent coupling improves discrimination of temporal spike patterns

By Yuan Chun-Wei and Christian Leibold


Despite the ubiquitous presence of recurrent synaptic connections in sensory neuronal systems, their general functional purpose is notwell understood. A recent conceptual advance has been achieved by theories of reservoir computing in which recurrent networks have been proposed to generate short-term memory as well as to improve neuronal representation of the sensory input for subsequent computations. Here, we present a numerical study on the distinct effects of inhibitory and excitatory recurrence in a canonical linear classification task. It is found that both types of coupling improve the ability to discriminate temporal spike patterns as compared to a purely feed-forward system, although in different ways. For a large class of inhibitory networks, the network’s performance is optimal as long as a fraction of roughly 50% of neurons per stimulus is active in the resulting population code. Thereby the contribution of inactive neurons to the neural code is found to be even more informative than that of the active neurons, generating an inherent robustness of classification performance against temporal jitter of the input spikes. Excitatory couplings are found to not only produce a short-term memory buffer but also to improve linear separability of the population patterns by evoking more irregular firing as compared to the purely inhibitory case. As the excitatory connectivity becomes more sparse, firing becomes more variable, and pattern separability improves. We argue that the proposed paradigm is particularly well-suited as a conceptual framework for processing of sensory information in the auditory pathway.

Topics: Biologie, ddc:570
Year: 2012
DOI identifier: 10.3389/fncom.2012.00025
OAI identifier:
Provided by: Open Access LMU

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  13. (2007). NEST (neural simulation tool).
  14. (1982). Neural networks and physical systems with emergent collective computational abilities.
  15. (2004). Neural processing of amplitude-modulatedsounds.Physiol.
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  19. (1996). Representation of spatial orientation by the intrinsic Frontiers in Computational Neuroscience
  20. (2009). Reservoir computing approaches to recurrent neural network training.
  21. (1973). Selforganization of orientation sensitive cells in the striate cortex.
  22. (2009). SORN: a self-organizing recurrent neural network.
  23. (2008). Sparse representation of sounds in the unanesthetized auditory cortex.
  24. (2005). The Inferior Colliculus.N e wY o r k ,
  25. (2010). The SHOGUN machine learning toolbox.
  26. (1995). Theory of orientation tuning in visual cortex.

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