- Poster presentation
- Open Access
Functional identification of complex cells from spike times and the decoding of visual stimuli
© Lazar et al. 2015
- Published: 18 December 2015
- Visual Stimulus
- Neural Circuit
- Complex Cell
- Primary Visual Cortex
- Decode Algorithm
Neural circuits built with complex cells play a key role in modeling the primary visual cortex. The encoding capability of an ensemble of complex cells has not been systematically studied, however. Can visual scenes be reconstructed using the spike times generated by an ensemble of complex cells? Can the processing taking place in complex cells be identified with high accuracy? Processing by complex cells has the complexity of Volterra models . General Volterra based models call, among others, for efficient functional identification and decoding algorithms.
We demonstrate that complex cells exhibit Volterra dendritic stimulus processors (Volterra DSPs) that are analytically and computationally tractable. Decoding and identification problems arising in neural circuits built with complex cells can be efficiently solved as rank minimization problems . We provide (i) an algorithm that reconstructs the visual stimuli based on the spike times generated by circuits with widely employed complex cells models (Complex Cell Time Decoding Machines), and (ii) propose a mechanistic algorithm for functionally identifying the processing in complex cells using the spike times they generate (Complex Cell Channel Identification Machines). These algorithms are based on the key observation that the functional identification of processing in a single complex cell is dual to the problem of decoding stimuli encoded by an ensemble of complex cells.
The research reported here was supported by AFOSR under grant #FA9550-12-1-0232.
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