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Computational Intelligence and Neuroscience
Volume 2010 (2010), Article ID 469658, 13 pages
Research Article

Consistent Recovery of Sensory Stimuli Encoded with MIMO Neural Circuits

Department of Electrical Engineering, Columbia University, New York, NY 10027, USA

Received 1 March 2009; Accepted 24 June 2009

Academic Editor: Zhe (Sage) Chen

Copyright © 2010 Aurel A. Lazar and Eftychios A. Pnevmatikakis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion. We formulate the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and show that the recovery has a unique solution. We provide explicit reconstruction algorithms for stimuli encoded with single as well as a population of integrate-and-fire neurons. We demonstrate how our reconstruction algorithms can be applied to stimuli encoded with ON-OFF neural circuits with feedback. Finally, we extend the formalism to multi-input multi-output neural circuits and demonstrate that vector-valued finite energy signals can be efficiently encoded by a neural population provided that its size is beyond a threshold value. Examples are given that demonstrate the potential applications of our methodology to systems neuroscience and neuromorphic engineering.