The Encoding and decoding of complex visual stimuli : a neural model to optimize and read out a temporal population code
The mammalian visual system has a remarkable capacity of processing a large amount of information within milliseconds under widely varying conditions into invariant representations. Recently a model of the primary visual system exploited the unique feature of dense local excitatory connectivity of t...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/94143 |
| Acceso en línea: | http://hdl.handle.net/10803/94143 |
| Access Level: | acceso abierto |
| Palabra clave: | Temporal Population Code Wavelets Face Recognition Vision Reconocimiento de Caras Visión 62 |
| Sumario: | The mammalian visual system has a remarkable capacity of processing a large amount of information within milliseconds under widely varying conditions into invariant representations. Recently a model of the primary visual system exploited the unique feature of dense local excitatory connectivity of the neo-cortex to match these criteria. The model rapidly generates invariant representations integrating the activity of spatially distributed modeled neurons into a so-called Temporal Population Code (TPC). In this thesis, we first investigate an issue that has persisted TPC since its introduction: to extend the concept to a biologically compatible readout stage. We propose a novel neural readout circuit based on wavelet transform that decodes the TPC over different frequency bands. We show that, in comparison with pure linear readouts used previously, the proposed system provides a robust, fast and highly compact representation of visual input. We then generalized this optimized encoding-decoding paradigm to deal with a number of robotics application in real-world tasks to investigate its robustness. Our results show that complex stimuli such as human faces, hand gestures and environmental cues can be reliably encoded by TPC which provides a powerful biologically plausible framework for real-time object recognition. In addition, our results suggest that the representation of sensory input can be built into a spatial-temporal code interpreted and parsed in series of wavelet like components by higher visual areas. |
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