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Section: New Results
Neuro-inspired computational models
Participants : Yann Boniface, Benoît Chappet de Vangel, Bernard Girau, Patrick Hénaff.
Motion detection
We develop bio-inspired neural architectures to extract and segment the direction and speed components of the optical flow from sequences of images. Following this line, we have built additional models to code and distinguish different visual sequences. The structure of these models takes inspiration from the course of visual movement processing in the human brain, such as in area MT (middle temporal) that detects patterns of movement, or area FBA where neurons have been found to be sensitive to single spatio-temporal patterns. This work has been extended to complex movements: to fight, to wave, to clap, using real-world video databases [9] .
Multimodal learning through joint dynamic neural fields
We have developed a coherent multimodal learning for a system with multiple sensory inputs. To this aim, we modified the BCM synaptic rule, a local learning rule, to obtain the self organization of our neuronal inputs maps and we used a CNFT based competition to drive the BCM rule. In practice, we introduced a feedback modulation of the learning rule, representing multimodal constraints of the environment. We also introduced an unlearning term in the BCM equation to solve the problem of the different temporalities between the raise of the activity within modal maps and the multimodal learning of the organization of the maps [12] .
Adaptive sensori-motor loop
We develop bio-inspired neural controllers to control humanoids robot when they interact physically (or socially) with the human. We focus on the role of rythmicity in the interaction: how the phenomena of coupling, synchrony or others are involved in the interaction between humans? what models of neural structures can incorporate rythmicity intrinsically, and can include learning or adaptive mechanisms of the rythmicity.
Randomly spiking dynamic neural fields
We have defined a new kind of spiking neural field that is able to use only local links while transmitting spikes through the map by succesive random propagations. Such a model is able to be mapped onto FPGAs, while maintaining most properties of neural fields. This model has been validated from a behavioral point of view, and a fully scalable hardware implementation has been designed with several thousands of neurons on-chip. These first results are the object of an article that is currently reviewed after requested revisions.