DocumentCode :
1426892
Title :
Population Encoding With Hodgkin–Huxley Neurons
Author :
Lazar, Aurel A.
Author_Institution :
Dept. of Electr. Eng., Columbia Univ., Columbia, NY, USA
Volume :
56
Issue :
2
fYear :
2010
Firstpage :
821
Lastpage :
837
Abstract :
The recovery of (weak) stimuli encoded with a population of Hodgkin-Huxley neurons is investigated. In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-Huxley neuron and 2) devising a recovery algorithm for stimuli encoded with the I/O equivalent neuron(s). A Hodgkin-Huxley neuron with multiplicative coupling is I/O equivalent with an Integrate-and-Fire neuron with a variable threshold sequence. For bandlimited stimuli a perfect recovery of the stimulus can be achieved provided that a Nyquist-type rate condition is satisfied. A Hodgkin-Huxley neuron with additive coupling and deterministic conductances is first-order I/O equivalent with a Project-Integrate-and-Fire neuron that integrates a projection of the stimulus on the phase response curve. The stimulus recovery is formulated as a spline interpolation problem in the space of finite length bounded energy signals. A Hodgkin-Huxley neuron with additive coupling and stochastic conductances is shown to be first-order I/O equivalent with a Project-Integrate-and-Fire neuron with random thresholds. For stimuli modeled as elements of Sobolev spaces the reconstruction algorithm minimizes a regularized quadratic optimality criterion. Finally, all previous recovery results of stimuli encoded with Hodgkin-Huxley neurons with multiplicative and additive coupling, and deterministic and stochastic conductances are extended to stimuli encoded with a population of Hodgkin-Huxley neurons.
Keywords :
biology computing; encoding; interpolation; neural nets; neurophysiology; signal reconstruction; Hodgkin-Huxley neurons; Nyquist-type rate condition; additive coupling; deterministic conductance; multiplicative coupling; neural encoding; phase response curve; population encoding; project-integrate-and-fire neuron; stimuli encoded recovery algorithm; Additives; Biomedical signal processing; Encoding; Interpolation; Neurons; Neuroscience; Signal processing; Signal processing algorithms; Spline; Stochastic processes; Hodgkin–Huxley neurons; input–output (I/O) equivalence; neural encoding; population encoding; reproducing Kernel Hilbert spaces; splines; stimulus reconstruction;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2009.2037040
Filename :
5420291
Link To Document :
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