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A Biologically Plausible Computational Model for Auditory Object Recognition

¹Hearing Research Center and Center for Biodynamics, and ²Department of Biomedical Engineering, Boston University, Boston, Massachusetts

Submitted 11 June 2008; accepted in final form 29 October 2008

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

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				G. D. Grana, C. P. Billimoria, and K. Sen Analyzing Variability in Neural Responses to Complex Natural Sounds in the Awake Songbird J Neurophysiol, June 1, 2009; 101(6): 3147 - 3157. [Abstract] [Full Text] [PDF]