Studies of spatial memory provide valuable insight into more general mnemonic functions, for by observing the activity of cells such as place cells, one can follow a subject’s dynamic representation of a changing environment. I investigate how place cells resolve conflicting neuronal input signals by developing computational models that integrate synaptic inputs on two scales. First, I construct reduced models of morphologically accurate neurons that preserve neuronal structure and the spatial
specificity of inputs. Second, I use a parallel implementation to examine the dynamics among a network of interconnected place cells. Both models elucidate possible roles for the inputs and mechanisms involved in spatial memory
