Once the weights of these connections have been learned (see “Sensori-motor association learning in effector choice tasks” section), the field excites all neurons in the motor plan formation field corresponding to the cued effector.

Sensorimotor association learning in effector choice tasks.

We studied the neural mechanisms underlying sensorimotor association learning by training the model to distinguish between two cues which signal whether it is required to perform a reach (“green” cue) or saccade (“red” cue) to a target.

When the size of the graph (N) was varied to assess the robustness of the BIG ADO findings with respect to the parameter space, the degree (d) and the number of associations (edges) used to pre-train the network (T) also varied as d = N/5O and T = Nxd/4, in order to keep the fraction of associations learned during pre-training constant.

The fraction of spurious associations learned was substantially lower than that of real associations for degrees above 5 and rewiring probability below 50% (Fig.

In this model, formation of new binary connections (a form of structural plasticity) underlies associative learning , and knowledge is encoded by the connectivity of the network [ 10].

However, little is known about the associative learning between cue and required Visuomotor mapping, and how learning generalises to novel but similar conditions.

This type of associative learning is quite general and even extents to contextual cues that are not necessarily directly task relevant.

It follows that the associative learning process investigated here could very well be described by a Mixture-of-Kalman-Filters where each shape along the continuous shape scale is coupled to its own learning mechanism (see Bayesian Learning Model in Materials and Methods).