Consistent Recovery of Sensory Stimuli Encoded with MIMO Neural Circuits

Abstract EN

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.