Abstract

Sari Andoni is with the Institute for Neuroscience, University of Texas at Austin, Austin TX 78712, USA -- Manish Saggar, Tekin Mericli, and Riston Miikkulainen are with the Department of Computer Sciences, University of Texas at Austin, Austin TX 78712, USA

Highlights

  • Open AccessExtracting the dynamics of the Hodgkin-Huxley model using recurrent neural networks. Address: 1Institute for Neuroscience, University of Texas at Austin, Austin TX 78712, USA and 2Department of Computer Sciences, University of Texas at Austin, Austin TX 78712, USA

  • Sixteenth Annual Computational Neuroscience Meeting: CNS*2007 William R Holmes Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here http://www.biomedcentral.com/content/pdf/1471-2202-8-S2-info.pdf

  • This paper proposes training of an artificial neural network to identify and model the physiological properties of a biological neuron, and mimic its input-output mapping

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Summary

Open Access

Extracting the dynamics of the Hodgkin-Huxley model using recurrent neural networks. Address: 1Institute for Neuroscience, University of Texas at Austin, Austin TX 78712, USA and 2Department of Computer Sciences, University of Texas at Austin, Austin TX 78712, USA. Its state depends on the interactions among its previous states, its intrinsic properties, and the synaptic input it receives. Some of these factors are included in Hodgkin-Huxley (HH) model, which describes the ionic mechanisms involved in the generation of an action potential. This paper proposes training of an artificial neural network to identify and model the physiological properties of a biological neuron, and mimic its input-output mapping. BMC Neuroscience 2007, 8(Suppl 2):P100 knowledge of its physiology Such a model can in turn be used as a tool for controlling a neuron in order to study its dynamics for further analysis

Methods and results
Conclusion

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