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Physiologically realistic formation of autoassociative memory in networks with theta/gamma oscillations: role of fast NMDA channels.

Recordings from brain regions involved in memory function show dual oscillations in which each cycle of a low-frequency theta oscillation (5-8 Hz) is subdivided into about seven subcycles by high frequency gamma oscillations (20-60 Hz). It has been proposed (Lisman and Idiart 1995) that such networks are a multiplexed short-term memory (STM) buffer that can actively maintain about seven memories, a capability of human STM. A memory is encoded by a subset of principal neurons that fire synchronously in a particular gamma subcycle. Firing is maintained by a membrane process intrinsic to each cell. We now extend this model by incorporating recurrent connections with modifiable synapses to store long-term memory (LTM). The repetition provided by STM gradually modifies synapses in a physiologically realistic way. Because different memories are active in different gamma subcycles, the formation of autoassociative LTM requires that synaptic modification depend on N-methyl-D-aspartate (NMDA) channels having a time constant of deactivation that is of the same order as the duration of a gamma subcycle (15-50 msec). Many types of NMDA channels have longer time constants (150 msec), as for instance those found in the hippocampus, but both fast and slow NMDA channels are present in cortex. This is the first proposal for the special role of these fast NMDA channels. The STM for novel items must depend on activity-dependent changes intrinsic to neurons rather than recurrent connections, which have not developed the required selectivity. Because these intrinsic mechanisms are not error-correcting, STM will become slowly corrupted by noise. This limits the accuracy with which LTM can become encoded after a single presentation. Accurate encoding of items in LTM can be achieved by multiple presentations, provided different memory items are presented in a varied interleaved order. Our results indicate that a limited memory-capacity STM model can be integrated in the same network with a high-capacity LTM model.