Consistent Recovery of Sensory Stimuli Encoded with MIMO Neural Circuits. (11th August 2009)

• Record Type: Journal Article
• Authors: Lazar, Aurel A.; Pnevmatikakis, Eftychios A.
• Other Names: Chen Zhe (Sage) Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2010(2010)
• Publication Date: 2009-08-11
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2010/469658
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.