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A Samsonovich

Publications and source records attributed to A Samsonovich.

2 recordsLinked to original sources

Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results.

Hippocampal-neocortical interactions in memory have typically been characterized within the "standard model" of memory consolidation. In this view, memory storage initially requires hippocampal linking of dispersed neocortical storage sites, but over time this need dissipates, and the hippocampal component is rendered unnecessary. This change in function over time is held to account for the retrograde amnesia (RA) gradients often seen in patients with hippocampal damage. Recent evidence, however, calls this standard model into question, and we have recently proposed a new approach, the "multiple memory trace" (MMT) theory. In this view, hippocampal ensembles are always involved in storage and retrieval of episodic information, but semantic (gist) information can be established in neocortex, and will survive damage to the hippocampal system if enough time has elapsed. This approach accounts more readily for the very long RA gradients often observed in amnesia. We report the results of analytic and connectionist simulations that demonstrate the feasibility of MMT. We also report a neuroimaging study showing that retrieval of very remote (25-year-old) memories elicits as much activation in hippocampus as retrieval of quite recent memories. Finally, we report new data from the study of patients with temporal lobe damage, using more sensitive measures than previously the case, showing that deficits in both episodic and spatial detail can be observed even for very remote memories. Overall, these findings indicate that the standard model of memory consolidation, which views the hippocampus as having only a temporary role in memory, is wrong. Instead, the data support the view that for episodic and spatial detail the hippocampal system is always necessary.

Epilepsy, Temporal Lobe↗

Path integration and cognitive mapping in a continuous attractor neural network model.

A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a "chart" contains a two-dimensional attractor set called an "attractor map" that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its neighbors on the currently active chart. Therefore, it is not particularly useful to think of place cells as encoding any particular external object or event. Because of its recurrent connections, hippocampal field CA3 is proposed as a possible location for this "multichart" architecture; however, other implementations in anatomy would not invalidate the main concepts. The model is implemented numerically both as a network of integrate-and-fire units and as a "macroscopic" (with respect to the space of states) description of the system, based on a continuous approximation defined by a system of stochastic differential equations. It provides an explanation for a number of hitherto perplexing observations on hippocampal place fields, including doubling, vanishing, reshaping in distorted environments, acquiring directionality in a two-goal shuttling task, rapid formation in a novel environment, and slow rotation after disorientation. The model makes several new predictions about the expected properties of hippocampal place cells and other cells of the proposed network.

Animals↗