Computational principles of synaptic memory consolidation

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

The synaptic tagging and capture hypothesis of protein synthesis-dependent long-term potentiation asserts that the induction of synaptic potentiation creates only the potential for a lasting change in synaptic efficacy, but not the commitment to such a change. Other neural activity, before or after induction, can also determine whether persistent change occurs. Recent findings, leading us to revise the original hypothesis, indicate that the induction of a local, synapse-specific 'tagged' state and the expression of long-term potentiation are dissociable. Additional observations suggest that there are major differences in the mechanisms of functional and structural plasticity. These advances call for a revised theory that incorporates the specific molecular and structural processes involved. Addressing the physiological relevance of previous in vitro findings, new behavioural studies have experimentally translated the hypothesis to learning and the consolidation of newly formed memories.

Slow-wave sleep (SWS) is important for memory consolidation. During sleep, neural patterns reflecting previously acquired information are replayed. One possible reason for this is that such replay exchanges information between hippocampus and neocortex, supporting consolidation. We recorded neuron ensembles in the rat medial prefrontal cortex (mPFC) to study memory trace reactivation during SWS following learning and execution of cross-modal strategy shifts. In general, reactivation of learning-related patterns occurred in distinct, highly synchronized transient bouts, mostly simultaneous with hippocampal sharp wave/ripple complexes (SPWRs), when hippocampal ensemble reactivation and cortico-hippocampal interaction is enhanced. During sleep following learning of a new rule, mPFC neural patterns that appeared during response selection replayed prominently, coincident with hippocampal SPWRs. This was learning dependent, as the patterns appeared only after rule acquisition. Therefore, learning, or the resulting reliable reward, influenced which patterns were most strongly encoded and successively reactivated in the hippocampal/prefrontal network.