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• W2020122583 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." @default.
• W2020122583 created "2016-06-24" @default.
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• W2020122583 date "2010-01-01" @default.
• W2020122583 modified "2023-10-16" @default.
• W2020122583 title "Consistent Recovery of Sensory Stimuli Encoded with MIMO Neural Circuits" @default.
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• W2020122583 doi "https://doi.org/10.1155/2010/469658" @default.
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