scholarly journals 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

Hippocampus ◽  
2020 ◽  
Vol 30 (4) ◽  
pp. 396-421 ◽  
Author(s):  
Ryan Grgurich ◽  
Hugh T. Blair
Keyword(s):  
Uncertainty Principle ◽  
Neural Coding ◽  
Spike Trains ◽  
Firing Rates ◽  
Neural Spike ◽  
Neural Spike Trains

scholarly journals 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

2019 ◽  
Author(s):  
Ryan Grgurich ◽  
Hugh T. Blair
Keyword(s):  
Firing Rate ◽  
Uncertainty Principle ◽  
Neural Coding ◽  
Spike Trains ◽  
Grid Cells ◽  
Rate Code ◽  
Biologically Inspired ◽  
Firing Rates ◽  
Neural Populations ◽  
Similar Information

AbstractThe 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 (Ṙ) 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 Ṙ, 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 Ṙ. To illustrate this, we describe two biologically inspired methods for decoding R and Ṙ, referred to as sigma and sigma-chi decoding, respectively. Simulations of head direction (HD) and grid cells show that if firing rates are tuned for position (but not velocity), then position is recovered by sigma decoding, whereas velocity is recovered by sigma-chi decoding. Conversely, simulations of oscillatory interference among theta-modulated “speed cells” show that if co-firing rates are tuned for position (but not velocity), then position is recovered by sigma-chi decoding, whereas velocity is recovered by sigma decoding. Between these two extremes, information about both variables can be distributed across both channels, and partially recovered by both decoders. These results suggest that neurons with different spatial and temporal tuning properties—such as speed versus grid cells—might not encode different information, but rather, distribute similar information about position and velocity in different ways across R and Ṙ. Such conjugate coding of position and velocity may influence how hippocampal populations are interconnected to form functional circuits, and how biological neurons integrate their inputs to decode information from firing rates and spike correlations.

scholarly journals A Simple Model for Low Variability in Neural Spike Trains

2018 ◽  
Vol 30 (11) ◽  
pp. 3009-3036 ◽  
Author(s):  
Ulisse Ferrari ◽  
Stéphane Deny ◽  
Olivier Marre ◽  
Thierry Mora
Keyword(s):  
Simple Model ◽  
Spike Train ◽  
Spike Trains ◽  
Firing Rates ◽  
Repeated Stimulus ◽  
Neural Noise ◽  
Poisson Models ◽  
Neural Spike ◽  
Neural Spike Trains ◽  
Two Parameters

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, that can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike-emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like a linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity, this model has the potential to explain low variability in other areas.

scholarly journals A simple model for low variability in neural spike trains

2018 ◽  
Author(s):  
Ulisse Ferrari ◽  
Stéphane Deny ◽  
Olivier Marre ◽  
Thierry Mora
Keyword(s):  
Simple Model ◽  
Spike Train ◽  
Spike Trains ◽  
Firing Rates ◽  
Repeated Stimulus ◽  
Neural Noise ◽  
Poisson Models ◽  
Neural Spike ◽  
Neural Spike Trains ◽  
Two Parameters

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas.

Correlation between neural spike trains increases with firing rate

Nature ◽  
2007 ◽  
Vol 448 (7155) ◽  
pp. 802-806 ◽  
Author(s):  
Jaime de la Rocha ◽  
Brent Doiron ◽  
Eric Shea-Brown ◽  
Krešimir Josić ◽  
Alex Reyes
Keyword(s):  
Firing Rate ◽  
Spike Trains ◽  
Neural Spike ◽  
Neural Spike Trains

scholarly journals The Neuroid revisited: A heuristic approach to model neural spike trains

2017 ◽  
Vol 33 (4) ◽  
pp. 331-343
Author(s):  
Erick Javier Argüello Prada ◽  
Ignacio Antonio Buscema Arteaga ◽  
Antonio José D’Alessandro Martínez
Keyword(s):  
Spike Trains ◽  
Heuristic Approach ◽  
Neural Spike ◽  
Neural Spike Trains

Automatic speech recognition with neural spike trains

2005 ◽  
Author(s):  
Marcus Holmberg ◽  
David Gelbart ◽  
Ulrich Ramacher ◽  
Werner Hemmert
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Spike Trains ◽  
Neural Spike ◽  
Neural Spike Trains
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