Continuous memory stabilization based on different adaptive processes in neural circuits

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The ability to learn and to create memories enables humans and animals to survive in complex and changing environments. Sensing our environment causes neural activity resulting in synaptic adaptation by various plasticity processes. Little is known about how neuronal and synaptic processes interact to select some specific input signals to remember while disregarding others. In particular, it is unclear how the neuronal network stabilizes a memory and maintains it for longer periods of time, especially given that the neuronal substrate does not remain constant but changes substantially during the lifetime of a memory. Memory formation, consolidation, and long-term stabilization are dynamic mechanisms based on neuronal and synaptic processes, which operate across many time scales. These processes are only partially understood and their interactions are still widely unknown. Thus, the goal of this work is to develop a neuro-theoretical framework to investigate formation, consolidation, and long-term stabilization. We will specifically focus on the underlying neural processes and their interactions across different time scales, which should lead to a better understanding of the dynamics of memory. Our central hypothesis is that memory consolidation or stabilization is a multi-step process: We propose that, by the input-dependent interaction of the processes of synaptic and sleep-induced network consolidation, first, memory representations or cell assemblies are formed by strongly interconnecting groups of neurons and, then, some of these cell assemblies are stabilized for longer durations (days), while others disintegrate leading to forgetting (selection by consolidation). Next, we suggest that the days-lasting stabilization of cell assemblies provides enough time to stabilize them for longer times by restructuring their internal connectivity through the slow processes of structural plasticity (long-term stabilization). The final outcome of this approach should be a dynamic memory model across different time scales, which allows investigating the continuous stabilization of memories.