An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains
Ryan Grgurich, Hugh T. Blair

TL;DR
This paper introduces an uncertainty principle for neural coding, showing that position and velocity information are encoded in spike trains via conjugate firing rate and co-firing rate codes, which trade off information capacity but can jointly encode conjugate variables.
Contribution
It reveals a novel uncertainty principle in neural coding, demonstrating how conjugate representations of position and velocity are mapped onto firing and co-firing rates, with methods for decoding these codes.
Findings
Firing rate and co-firing rate codes behave as conjugates, obeying an uncertainty principle.
Decoding methods (sigma and sigma-chi) can recover position and velocity from neural spike trains.
Neurons with different tuning properties distribute information across these conjugate codes.
Abstract
The hippocampal system contains neural populations that encode an animal's position and velocity as it navigates through space. Here, we show that such populations can embed two codes within their spike trains: a firing rate code (R) conveyed by within-cell spike intervals, and a co-firing rate code (R') conveyed by between-cell spike intervals. These two codes behave as conjugates of one another, obeying an analog of the uncertainty principle from physics: information conveyed in R comes at the expense of information in R', and vice versa. An exception to this trade-off occurs when spike trains encode a pair of conjugate variables, such as position and velocity, which do not compete for capacity across R and R'. To illustrate this, we describe two biologically inspired methods for decoding R and R', referred to as sigma and sigma-chi decoding, respectively. Simulations of head…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
