What is the physiological basis of long term memory? The current dogma in theoretical neuroscience attributes changes in synaptic efficacy to memory acquisition. This view implies that in the absence of learning, synaptic efficacies should be constant; in other words, stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent dynamics in synaptic strengths. These fluctuations seem to be occurring continuously, without specific reference to a learning process, but with similar magnitude. Motivated by these observations, we explore the possibility of memory storage within a specific component of network connectivity, while individual connections fluctuate in time . We find a family of neural network models in which memory is acquired by means of biologically-inspired learning rules; the properties of these rules, in turn, determine the storing component in which memory can persist indefinitely. Memory representations manifest as time-varying attractors in neural state-space and support associative retrieval of learned information. Our results suggest a link between the properties of learning rules and those of network-level memory representations, which generates experimentally-testable predictions .
What is the physiological basis of long-term memory? The prevailing view in neuroscience attributes changes in synaptic efficacy to memory acquisition. This view implies that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent dynamics in synaptic strengths. Motivated by these observations, we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate . We find a simple and general principle, stemming from stability arguments, that links eigenvalues in the complex plane to memories. Specifically, imaginary-coded memories are more resilient to noise and homeostatic plasticity than their real-coded counterparts. Memory representations are stored as time-varying attractors in neural state-space and support associative retrieval of learned information. Our results suggest a link between the properties of learning rules and those of network-level memory representations, and point at measurable signatures to be sought in experimental data .
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long term
long-term
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current dogma in theoretical
prevailing view in
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in the absence of learning, synaptic efficacies should be constant; in other words,
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These fluctuations seem to be occurring continuously, without specific reference to a learning process, but with similar magnitude.
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specific
global
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in time
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family of neural network models in which memory is acquired by means of biologically-inspired learning rules; the properties of these rules, in turn, determine the storing component in which memory can persist indefinitely. Memory representations manifest
simple and general principle, stemming from stability arguments, that links eigenvalues in the complex plane to memories. Specifically, imaginary-coded memories are more resilient to noise and homeostatic plasticity than their real-coded counterparts. Memory representations are stored
R
which generates experimentally-testable predictions
and point at measurable signatures to be sought in experimental data