Neuronal regulation versus synaptic unlearning in memory maintenance mechanisms.

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.

[1]  F. Seil.  Neural plasticity and regeneration , 2000 .

[2]  Eytan Ruppin,et al.  Associative Memory in a Multimodular Network , 1999, Neural Computation.

[3]  E. Ruppin,et al.  Associative memory in a multi-modular network. , 1999, Neural Computation.

[4]  Isaac Meilijson,et al.  Synaptic pruning in development: a novel account in neural terms , 1998 .

[5]  Isaac Meilijson,et al.  Synaptic Pruning in Development: A Computational Account , 1998, Neural Computation.

[6]  C. Goodman,et al.  Synapse-specific control of synaptic efficacy at the terminals of a single neuron , 1998, Nature.

[7]  Niraj S. Desai,et al.  Activity-dependent scaling of quantal amplitude in neocortical neurons , 1998, Nature.

[8]  Eytan Ruppin,et al.  Memory Maintenance via Neuronal Regulation , 1998, Neural Computation.

[9]  J. Lichtman,et al.  Alterations in Synaptic Strength Preceding Axon Withdrawal , 1997, Science.

[10]  J. L. van Hemmen,et al.  Hebbian learning, its correlation catastrophe, and unlearning , 1997 .

[11]  Eytan Ruppin,et al.  Neuronal-Based Synaptic Compensation: A Computational Study in Alzheimer's Disease , 1996, Neural Computation.

[12]  J A Reggia,et al.  Pathogenesis of schizophrenic delusions and hallucinations: a neural model. , 1996, Schizophrenia bulletin.

[13]  Eytan Ruppin,et al.  Compensatory Mechanisms in an Attractor Neural Network Model of Schizophrenia , 1995, Neural Computation.

[14]  J. L. Kavanau,et al.  Sleep and dynamic stabilization of neural circuitry: A review and synthesis , 1994, Behavioural Brain Research.

[15]  E. Masliah,et al.  Synaptic and neuritic alterations during the progression of Alzheimer's disease , 1994, Neuroscience Letters.

[16]  A. V. Ooyen.  Activity-dependent neural network development , 1994 .

[17]  L. F. Abbott,et al.  Analysis of Neuron Models with Dynamically Regulated Conductances , 1993, Neural Computation.

[18]  Marius Usher,et al.  Neural Network Modeling of Memory Deterioration in Alzheimer's Disease , 1993, Neural Computation.

[19]  E. Marder,et al.  Activity-dependent regulation of conductances in model neurons. , 1993, Science.

[20]  D. Price,et al.  Synapse loss in the temporal lobe in Alzheimer's disease , 1993, Annals of neurology.

[21]  J. Stevens,et al.  Abnormal reinnervation as a basis for schizophrenia: a hypothesis. , 1992, Archives of general psychiatry.

[22]  D. Salmon,et al.  Physical basis of cognitive alterations in alzheimer's disease: Synapse loss is the major correlate of cognitive impairment , 1991, Annals of neurology.

[23]  W. Meier-Ruge,et al.  Morphological adaptive response of the synaptic junctional zones in the human dentate gyrus during aging and Alzheimer's disease , 1990, Brain Research.

[24]  S. DeKosky,et al.  Synapse loss in frontal cortex biopsies in Alzheimer's disease: Correlation with cognitive severity , 1990, Annals of neurology.

[25]  M. Tsodyks.  ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH THE HEBBIAN LEARNING RULE , 1989 .

[26]  W. Meier-Ruge,et al.  Quantitative morphology of synaptic plasticity in the aging brain. , 1988, Scanning microscopy.

[27]  J. Hobson.  The Dreaming Brain , 1988 .

[28]  C. Cotman,et al.  Synaptic plasticity and functional stabilization in the hippocampal formation: possible role in Alzheimer's disease. , 1988, Advances in neurology.

[29]  Haim Sompolinsky,et al.  The Theory of Neural Networks: The Hebb Rule and Beyond , 1987 .

[30]  Francis Crick,et al.  The function of dream sleep , 1983, Nature.

[31]  J. J. Hopfield,et al.  ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.

[32]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[33]  J. Hobson,et al.  The brain as a dream state generator: an activation-synthesis hypothesis of the dream process. , 1977, The American journal of psychiatry.