We welcome another scholar, Maira Perveen, from the TU Chemnitz neurorobotics lab for a thesis on Boolean transformations of indexed neural networks.
Why is this relevant?
We introduced a memory model (1, 2) that is based on biological cortical memory and uses neuron-centric principles. Now we want to add abstractions to it, such as integer-based synaptic weights and an overall Boolean structure, which we believe simplifies reasoning and analogy. Our overall goal is to provide functional models of brain-based thinking, which will open the door to investigating functional diseases like depressive, neurotic, or psychotic thought patterns. Such simplifications are also of extraordinary interest in technical applications because of an enormous increase in the efficiency of running a model.
