Decoding in neural systems: stimulus reconstruction from nonlinear encoding

Abstract

The encoding of information about the outside world in the temporal activity of sensory neurons is an extremely complex process that has eluded the understanding of the scientific community for decades. The reconstruction of sensory stimuli from observed neuronal activity provides a basis within which we might ascertain the nature of the sensory information encoded by the cells. We present a decoding strategy for predicting the sensory stimulus from the neuronal response that is based on the mechanisms of encoding. For a class of encoding mechanisms characterized by a linear function followed by a memoryless nonlinearity, referred to as Wiener systems, the Bayesian estimator is derived from the transformational properties of the nonlinearity. The result is a reconstruction paradigm in which the ability to predict sensory stimuli from the neuronal response depends heavily upon how well the encoding process has been characterized, and thus provides a measure or our understanding of the underlying physiological process.