Modeling Cognition with Neural Networks

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Modeling Cognition with Neural Networks David Northmore Attempt to make a bridge between the theories of cognitive science and brain mechanisms, thereby guiding us to look for those mechanisms in the brain. Pointers enable one to keep track of 4-5 moving objects

Multiple Object Tracking Paradigm We know about these pointers through research on MOT. How to make a neural network, a parallel processor, with fixed architecture, select and latch onto one or a few moving targets

Modeling a pointer Moving stimuli Gain layers Pointer layer (+ 3 or 4) Neuron-like elements that are excitatory (Green) or inhibitory (Red) connections. Moving discs excite topographic points in gain layer. Response of each green unit is restrained by feedback inhibition from a red unit Gain units topgraphically activate Pointer layer. Lateral inhibitory WTA connections in pointer layer ensures only one locus. Feeds back to inhibitory units of gain layers, amplifying one locus. Multiple Pointer layers similarly connected with Gain Layers. Pointers are powerful (as in computer programs), but in different ways. Direct eye movements Selection of features Conjunction of features Serial operations Save to working memory Construct subjective “panorama” Pointer layer (+ 3 or 4)