It is interesting to realize auto- & hetero- AM simultaneously as human brain acts. In this paper, a modification of basic Hopfield neural network model is proposed. This model can function simultaneously both as two classes of associative memory with less increase in memory. The result of computer simulation and analysis of the SNR is given.
Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into
prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from
relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D
hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input
the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State
Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural
network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have,
it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully.
What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this
paper we train this net with a refined version of its typical training method and get a better model.