We propose a bio-inspired MT model working in a fully spiking mode: our MT layer receives spiking inputs coming from a previous spiking V1 layer. The MT layer integrates this information to produce spikes as output. Interestingly, this spike to spike model allows us to study and model some of the dynamics existing in V1 and MT, and due to the causality of our cell representations it is also possible to integrate some top-down feedback. This model differs from existing ones such as e.g.  and , that generally have analogue entry and consider motion stimuli in a continuous regime (as plaids or gratings) discarding dynamic behaviours. In this model we also propose an implementation for the inhibition done between cells in V1 and MT. The interaction between V1 cells is done both for neighbouring cells with the same velocity and for cells with the same receptive field but different velocity orientations. On the other hand, the inhibition between MT cells is done to help the model in the detection of the pattern motion direction. The architecture and details of our model are shown in Figure 1.
Figure 1. Architecture of model here presented. The first layer is formed as an array of direction-selective V1 complex cells tuned for different speeds and directions of motion. Each V1 complex cell is modelled with a motion energy detector following . The second layer of the model corresponds to a spiking MT cell array. Each MT cell has as input the spike trains of the V1 complex cells inside its receptive field; all the V1 cells considered inside the MT receptive field have the same orientation, the model data being based on biological findings . The dashed lines represent the interactions between V1 and MT cells. The values of the weights wi are adjusted (they could also be found through learning as STDP) to tune the MT neuron for a certain motion pattern direction.
Interest of a spike to spike model
We are interested in validating the behaviour of our model with:
Dynamic. The activation of MT cells is not constant in time, it suddenly increases when the motion direction is changed. We study the dynamical effects as described in .
Motion recognition. We will show how the spiking output of MT can be successfully used to recognize biological motion starting from real video sequences .
This work was partially supported by the EC IP project FP6-015879, FACETS and CONICYT Chile.
J Opt Soc Am A 1985, 2:284-299. PubMed Abstract