# Predictions of energy efficient Berger-Levy model neurons with constraints

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## Keywords

Mutual Information Firing Rate Probability Distribution Function Model Neuron Conditional Probability DistributionInformation theory has been extensively applied to neuroscience problems. The mutual information between input and output has been postulated as an objective, which neuronal systems may optimize. However, only recently the energy efficiency has been addressed within an information-theoretic framework [1]. Here, the key idea is to consider capacity per unit cost (measured in bits per joule, bpj) as the objective. We are interested in how biologically plausible constraints affect predictions made by this new theory for bpj-maximizing model neurons.

*increase*and then decrease with the mean ISI while it only

*decreases*in the unconstrained setting. Thus, we demonstrated that constraints can affect predictions based on bpj-maximization, and should be explicitly taken into account. Ongoing work makes these predictions more quantitative via simulating biophysically realistic model neurons.

## Notes

### Acknowledgements

This research has been supported in part by the DAAD (German-Arabic/Iranian Higher Education Dialogue).

## References

- 1.Berger T, Levy WB: A Mathematical Theory of Energy Efficient Neural Computation and Communication. IEEE Trans on Information Theory. 2010, 56 (2): 852-874.CrossRefGoogle Scholar
- 2.Xing J, Berger T, Sejnowski TJ: A Berger-Levy energy efficient neuron model with unequal synaptic weights. Proc of IEEE Int Symp on Information Theory. 2012, 2964-2968.Google Scholar

## Copyright information

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