A minimum-error, energy-constrained neural encoder predicts an instantaneous spike-rate code

For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the “history”). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates “history” into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation ( IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking.

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Minimum squared-error, energy-constrained encoding by adaptive threshold models of neurons

2015 IEEE International Symposium on Information Theory (ISIT) ◽

10.1109/isit.2015.7282673 ◽

2015 ◽

Cited By ~ 2

Author(s):

Erik C. Johnson ◽

Douglas L. Jones ◽

Rama Ratnam

Keyword(s):

Adaptive Threshold ◽

Threshold Models ◽

Squared Error ◽

Error Energy ◽

Energy Constrained

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Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

eLife ◽

10.7554/elife.37102 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 12

Author(s):

Hannah L Payne ◽

Ranran L French ◽

Christine C Guo ◽

TD Barbara Nguyen-Vu ◽

Tiina Manninen ◽

...

Keyword(s):

Eye Movements ◽

Purkinje Cells ◽

Spike Rate ◽

Additional Contribution ◽

Rapid Rate ◽

Rate Code ◽

Temporal Precision ◽

Additional Information ◽

Parameter Ranges ◽

Eye Velocity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3–5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

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