Patterns of activity in a recurrent neural network with overlapping inputs

The brain, with its network of neurons, is still a mystery to us.  Numerous studies have been performed to try to understand how the brain processes the information that we are exposed to through our senses.  In this talk, we try to answer some of those questions as we numerically investigate how the brain encodes these spatial and non-spatial elements through its neural network.  Within this network, there are three main layers: an input layer, a hidden layer, and an output layer.  Input into the hidden layer will pair a spatial element with a non-spatial element, such as color, and train the network to learn the pairing under reward modulated learning rules.  This is possible due to the changing strengths of the connections between the neurons.  Once the network has learned the correct space and object pairs, we will numerically investigate the strengths of the connections between the neurons in the hidden layer and also look at the activity of the network, through the output layer.  With the use of Matlab, we hope to answer some questions about how the brain processes information.