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How Far can Neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and
Agnieszka Pregowska1, Ehud Kaplan2,3,4, Janusz Szczepanski1
1Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawinskiego 5B, 02-106 Warsaw, Poland.
International Journal of Neural Systems
|March 8, 2019
Summary
Neural codes use either firing rates or precise spike timing. This study finds that information loss from correlations is small, suggesting temporal codes are an efficient alternative to rate codes.
Area of Science:
- Neuroscience
- Information Theory
- Computational Neuroscience
Background:
- Neurons can encode information using firing rates or precise spike timing.
- Understanding information loss due to neural signal correlations is crucial.
Purpose of the Study:
- To compare information loss in rate codes versus temporal codes.
- To quantify the impact of correlations on information transmission.
Main Methods:
- Compared information per symbol from binary Markov (temporal) and Bernoulli (rate) sources.
- Analyzed information transmission rates (ITRs) using a transition probability parameter.
- Utilized entropy grouping axiom to determine information loss for given word lengths.
Main Results:
- Information loss in correlated signals (temporal codes) is relatively small.
- A specific parameter significantly influences the relationship between ITRs and firing rates.
- Maximal and minimal bounds for the quotient of ITRs were calculated.
Conclusions:
- Temporal codes are energetically efficient and can effectively replace rate codes.
- Correlations minimally impact information content in neural signaling.
- Experimental results confirmed the theoretical findings.
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