Neuronal regulation versus synaptic unlearning in memory maintenance mechanisms
Abstract
Hebbian learning, the paradigm of memory formation, needs further mechanisms to guarantee creation and maintenance of a viable memory system. One such proposed mechanism is Hebbian unlearning, a process hypothesized to occur during sleep. It can remove spurious states and eliminate global correlations in the memory system. However, the problem of spurious states is unimportant in the biologically interesting case of memories that are sparsely coded on excitatory neurons. Moreover, if some memories are anomalously strong and have to be weakened to guarantee proper functioning of the network, we show that it is advantageous to do that by neuronal regulation (NR) rather than synaptic unlearning. Neuronal regulation can account for dynamical maintenance of memory systems that undergo continuous synaptic turnover. This neuronal-based mechanism, regulating all excitatory synapses according to neuronal average activity, has recently gained strong experimental support. NR achieves synaptic maintenance over short time scales by preserving the average neuronal input field. On longer time scales it acts to maintain memories by letting the stronger synapses grow to their upper bounds. In ageing, these bounds are increased to allow stronger values of remaining synapses to overcome the loss of synapses that have perished Note: This article was submitted to Network: Computation in Neural Systems as a response to the recent Viewpoint by van Hemmen (van Hemmen J L 1997 Hebbian learning, its correlation catastrophe, and unlearning Network: Comput. Neural Syst. 8 V1–V17). In their Comment, Horn, Levy and Ruppin argue for the mechanism of neuronal regulation in associative memory models as an alternative to the mechanism of unlearning discussed by van Hemmen. To obtain a complete picture of the advantages and disadvantages of both types of mechanism, readers should also consult van Hemmen's original article. Editor-in-Chief .