Long-lasting memory from evanescent networks.

Current models of memory typically require a protein synthetic step leading to a more or less permanent structural change in synapses of the network that represent the stored information. This instructive role of protein synthesis has recently been called into question [Routtenberg, A., Rekart, J.L. 2005. Post-translational modification of synaptic proteins as the substrate for long-lasting memory. Trends Neurosci. 28, 12-19]. In its place a new theory is proposed in which post-translational modifications (PTMs) of proteins already synthesized and present within the synapse calibrate synaptic strength. PTM is thus the only mechanism required to sustain long-lasting memories. Activity-induced, PTM-dependent structural modifications within brain synapses then define network formation which is thus a product of the concatenation of cascaded PTMs. This leads to a formulation different from current protein synthesis models in which neural networks initially formed from these individual synaptic PTM-dependent changes is maintained by regulated positive feedback maintains. One such positive feedback mechanism is 'cryptic rehearsal' typically referred to as 'noise' or 'spontaneous' activity. This activity is in fact not random or spontaneous but determined in a stochastic sense by the past history of activation of the nerve cell. To prevent promiscuous network formation, the regulated positive feedback maintains the altered state given specific decay kinetics for the PTM. The up or down state of individual synapses actually exists in an infinite number of intermediate states, never fully 'up', nor fully 'down.' The networks formed from these uncertain synapses are therefore metastable. A particular memory is also multiply represented by a 'degenerate code' so that should loss of a subset of representations occur, erasure can be protected against. This mechanism also solves the flexibility-stability problem by positing that the brain eschews synaptic stability having its own uncertainty principle that allows retrieval from a probabilistic network, so that a retrieved memory can be represented by a selection of components from an essentially infinite number of networks. The network so formed, that is the retrieval, thus emerges from a hierarchy of connectionistic probabilities. The relation of this new theory of memory network formation to current and potential computational implementations will benefit by its unusual point of initiation: deep concerns about the molecular substrates of information storage.