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The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input

Figure 3

Evolution of the recoding and retrieval errors over environments for the fixed, reinitialising and plastic networks.

Left panel: The recoding error (lower solid line) and retrieval error after adaptation to the final environment (dashed line) of the reinitialising network are a measure of how well a completely specialised network deals with the same kind of statistics. This is the best possible average recoding performance, and correspondingly the worst possible retrieval performance we can expect for a network with DG units. The recoding error of the fixed network (upper solid line) is a measure of how well a completely generic network deals with the statistics of the spatially driven input we have used. We expect that any adaptation strategy would produce at least this level of recoding accuracy. Right panel: Evolution of the recoding error (solid line) and the retrieval error (dashed line) as a function of environment number for a network that uses a neural gas-like plasticity algorithm with a recoding error threshold of . In all subsequent plots we conform to the convention of plotting recoding errors with a solid line and retrieval errors with a dashed line. The errors lie in the range to which we also adopt as our standard vertical scale. Conventional plasticity successfully reduces the recoding error in each environment to the target value but only at the expense of increasing the retrieval error for previously stored memory patterns.

doi: https://doi.org/10.1371/journal.pcbi.1001063.g003