- Poster presentation
- Open Access
Self-organizing neural maps for multi-modal associations
© Lefort et al; licensee BioMed Central Ltd. 2011
- Published: 18 July 2011
- Sensory Flow
- Sensitive Layer
- Neural Field
- Cortical Column
- Modal Architecture
More precisely, each perceptive map consists of generic cortical columns with computing and learning which are local and decentralized (see figure1 B). Sensitive layer receives the feed-forward flow coming from a sensor and uses the BCM learning rule . This synaptic learning rule is based on the hebbian one and is able to autonomously raise a selectivity to one stimulus of the upcoming flow. From a computational point of view, the BCM rule uses a sliding threshold between long term potentiation and long term depression, which has been confirmed by biological evidences . The cortical layer receives feedback influence coming from the associative map, whose activity represents the multi modal perception. The perceptive layer is based on the neural field theory  and it receives the sensitive activity modulated by the cortical one. Thanks to the competitive mechanism introduced by the lateral connectivity with a difference of Gaussian shape, an activity bump emerges at the map level, where the activity is the most spatially consistent, representing a consensus between the local sensation and the multi modal constraints.
To obtain a self-organization of the sensitive layer at the map level, the perceptive layer modulates the BCM activity, so that the spatial consistency of the activity bump is propagated to the selectivity organization. We have also introduced an unlearning term in the BCM learning rule in order to forget the current selectivity if it is not consistent with the received modulation. This unlearning mechanism provides plasticity to the self-organization in order to adapt to the multi modal constraints.
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