Week 2

Had my second project meeting yesterday, the plan for this week is to modify my C code and Topographica (hopefully only model scripts) to match in terms of the results produced and internal operations. Starting off with lateral-only square all-to-all projections with no learning. This means that the output matrix is produced by dot-producting an input matrix with a 4-dimensional matrix of weights: first two dimensions contain an M*M grid of neurons (at this stage the size and shape of the grid are the same as that of the input and output matrices).  At this stage, for simplicity each neuron is connected to all other neurons in the sheet, and therefore is represented by an M*M matrix of connection weights.Thus, the grid of neurons together with their weights can be expressed as M*M*M*M matrix, input and output - as M*M. Output is generated by dot-producting input with each neuron's weights, the result of each operation is a single number. After M*M such operations full output matrix is produced. "Lateral" term in our case means that the system uses the output matrix from previous iteration as input to produce new output, as opposed to using some external feed. No learning means that the neuron sheet is static at all points of the simulation.

At some point we want the neuron sheet to be M*K*L*N, i.e. use non-square patterns as well as for inputs and outputs; have limited connection fields (i.e. non-all-to-all), some form of learning and, perhaps, afferent projections (taking inputs from the outside). All that will be used to isolate any performance problems and see the potential for parallel scalability.