Correlations strike back (again): The case of associative memory retrieval

It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population. Less studied, though equally pernicious, is the need to take account of dependencies between synaptic weights when decoding patterns previously encoded in an auto-associative memory. We show that activity-dependent learning generically produces such correlations, and failing to take them into account in the dynamics of memory retrieval leads to catastrophically poor recall. We derive optimal network dynamics for recall in the face of synaptic correlations caused by a range of synaptic plasticity rules. These dynamics involve well-studied circuit motifs, such as forms of feedback inhibition and experimentally observed dendritic nonlinearities. We therefore show how addressing the problem of synaptic correlations leads to a novel functional account of key biophysical features of the neural substrate.

Context-dependent decay of motor memories during skill acquisition.

Current models of motor learning posit that skill acquisition involves both the formation and decay of multiple motor memories that can be engaged in different contexts. Memory formation is assumed to be context dependent, so that errors most strongly update motor memories associated with the current context. In contrast, memory decay is assumed to be context independent, so that movement in any context leads to uniform decay across all contexts. We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent. We show that the decay of memory associated with a given context is greatest for movements made in that context, with more distant contexts showing markedly reduced decay. Thus, both memory formation and decay are strongest for the current context. We propose that this apparently paradoxical organization provides a mechanism for optimizing performance. While memory decay tends to reduce force output, memory formation can correct for any errors that arise, allowing the motor system to regulate force output so as to both minimize errors and avoid unnecessary energy expenditure. The motor commands for any given context thus result from a balance between memory formation and decay, while memories for other contexts are preserved.