On the Relation between Encoding and Decoding of Neuronal Spikes

TL;DR

This paper explores the relationship between neural encoding and decoding, showing how correlations within spike trains can influence the efficiency of decoding rate codes in neural responses.

Contribution

It provides a detailed analysis of how encoding and decoding relate, especially highlighting the impact of spike train correlations on decoding efficiency in neural coding.

Findings

Spike train correlations can improve decoding efficiency.

Rate decoders may not always be optimal for encoding.

Understanding encoding-decoding links aids neural information interpretation.

Abstract

Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding refers to the mapping from stimulus to response, and primarily focuses on understanding how neurons respond to a wide variety of stimuli, and on constructing models that accurately describe the stimulus-response relationship. Neural decoding, on the other hand, refers to the reverse mapping, from response to stimulus, where the challenge is to reconstruct a stimulus from the spikes it evokes. Since neuronal response is stochastic, a one-to-one mapping of stimuli into neural responses does not exist, causing a mismatch between the two viewpoints of neural coding. Here, we use these two perspectives to investigate the question of what rate coding is, in the simple setting of a single stationary stimulus parameter and a single stationary spike train represented by a renewal process. We show that when rate codes are defined in terms of encoding, i.e., the stimulus parameter is mapped onto the mean firing rate, the rate decoder given by spike counts or the sample mean, does not always efficiently decode the rate codes, but can improve efficiency in reading certain rate codes, when correlations within a spike train are taken into account.