Encoding visual stimuli with a population of Hodgkin-Huxley neurons
© Lazar and Zhou; licensee BioMed Central Ltd. 2010
Published: 20 July 2010
In recent years the increasing availability of multi-electrode recordings has led to the application of neural decoding techniques to the recovery of complex stimuli such as natural scenes. A linear decoding algorithm was presented in  for the reconstruction of natural scenes with recognizable moving objects using recordings from a neural population of the cat’s Lateral Geniculate Nucleus (LGN).
Most of the current models of encoding in the early visual system (retina, LGN, V1) consist of a linear receptive field followed by a non-linear spike generation mechanism. In  we considered a neural circuit architecture consisting of receptive fields in cascade with an equal number of spiking neural circuits. The neural circuits investigated were integrate-and-fire neurons and ON-OFF neurons with random thresholds and feedback. We demonstrated for the first time a decoding algorithm for natural scenes and shown its dependence on the noise level.
The work presented here was supported by AFOSR under grant number FA9550-09-1-0350.
- Stanley GB, Li FF, Dan Y: Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus. J Neurosci. 1999, 19 (18): 8036-8042.PubMedGoogle Scholar
- Lazar AA, Pnevmatikakis EA, Zhou Y: Encoding of Natural Scenes with Neural Circuits with Random Thresholds. BNET Technical Report #06-09, Department of Electrical Engineering, Columbia University, New York, NY. 2009Google Scholar
- Lazar AA: Population Encoding with Hodgkin-Huxley Neurons. IEEE Transactions on Information Theory. 2010, 56 (2): to appearGoogle Scholar
- Kim AJ, Lazar AA: Recovery of Stimuli Encoded with a Hodgkin-Huxley Neuron Using Conditional PRCs. In Phase Response Curves in Neuroscience, Springer. Edited by: Nathan W. Schultheiss, Astrid Prinz, and Rob Butera. 2010, to appearGoogle Scholar
This article is published under license to BioMed Central Ltd.