Computational principles of biological memory

Memories are stored, retained, and recollected through complex, coupled processes operating on multiple timescales. To understand the computational principles behind these intricate networks of interactions we construct a broad class of synaptic models that efficiently harnesses biological complexity to preserve numerous memories. The memory capacity scales almost linearly with the number of synapses, which is a substantial improvement over the square root scaling of previous models. This was achieved by combining multiple dynamical processes that initially store memories in fast variables and then progressively transfer them to slower variables. Importantly, the interactions between fast and slow variables are bidirectional. The proposed models are robust to parameter perturbations and can explain several properties of biological memory, including delayed expression of synaptic modifications, metaplasticity, and spacing effects.

[1]  Xiao-Jing Wang,et al.  The Stability of a Stochastic CaMKII Switch: Dependence on the Number of Enzyme Molecules and Protein Turnover , 2005, PLoS biology.

[2]  Marc Mézard,et al.  Basins of Attraction in a Perception-like Neural Network , 1988, Complex Syst..

[3]  L. Abbott,et al.  Limits on the memory storage capacity of bounded synapses , 2007, Nature Neuroscience.

[4]  Stefano Fusi,et al.  Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation , 2013, PLoS Comput. Biol..

[5]  Peter Dayan,et al.  Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3 , 2014, PLoS Comput. Biol..

[6]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .

[7]  Gerald Sommer,et al.  Pattern Recognition by Self-Organizing Neural Networks , 1994 .

[8]  T. SHALLICE,et al.  Learning and Memory , 1970, Nature.

[9]  Daniel J. Amit,et al.  Learning in Neural Networks with Material Synapses , 1994, Neural Computation.

[10]  A. Treves,et al.  Why the simplest notion of neocortex as an autoassociative memory would not work , 1992 .

[11]  James L. McClelland,et al.  Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.

[12]  Marc Mézard,et al.  Solvable models of working memories , 1986 .

[13]  M. Poo,et al.  Reversal and Stabilization of Synaptic Modifications in a Developing Visual System , 2003, Science.

[14]  Peter E. Latham,et al.  A Balanced Memory Network , 2007, PLoS Comput. Biol..

[15]  W. Abraham.  Metaplasticity: tuning synapses and networks for plasticity , 2008, Nature Reviews Neuroscience.

[16]  Sompolinsky,et al.  Neural networks with nonlinear synapses and a static noise. , 1986, Physical review. A, General physics.

[17]  Surya Ganguli,et al.  A memory frontier for complex synapses , 2013, NIPS.

[18]  Bartlett W. Mel,et al.  Capacity-Enhancing Synaptic Learning Rules in a Medial Temporal Lobe Online Learning Model , 2009, Neuron.

[19]  T. Sejnowski,et al.  Associative long-term depression in the hippocampus induced by hebbian covariance , 1989, Nature.

[20]  Shaomin Li,et al.  Amyloid-β protein dimers isolated directly from Alzheimer's brains impair synaptic plasticity and memory , 2008, Nature Medicine.

[21]  Stefano Fusi,et al.  Long Memory Lifetimes Require Complex Synapses and Limited Sparseness , 2007, Frontiers Comput. Neurosci..

[22]  Stefano Fusi,et al.  The Sparseness of Mixed Selectivity Neurons Controls the Generalization–Discrimination Trade-Off , 2013, The Journal of Neuroscience.

[23]  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.

[24]  H. Shouval.  Clusters of interacting receptors can stabilize synaptic efficacies. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  W. Krauth,et al.  Learning algorithms with optimal stability in neural networks , 1987 .

[26]  U. Bhalla.  Molecular computation in neurons: a modeling perspective , 2014, Current Opinion in Neurobiology.

[27]  J. Wixted,et al.  Genuine power curves in forgetting: A quantitative analysis of individual subject forgetting functions , 1997, Memory & cognition.

[28]  L. Abbott,et al.  Cascade Models of Synaptically Stored Memories , 2005, Neuron.

[29]  W. Gerstner,et al.  Synaptic Consolidation: From Synapses to Behavioral Modeling , 2015, The Journal of Neuroscience.

[30]  Stefano Fusi,et al.  Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates , 2002, Biological Cybernetics.

[31]  Aude Oliva,et al.  Visual long-term memory has a massive storage capacity for object details , 2008, Proceedings of the National Academy of Sciences.

[32]  S. Wang,et al.  Graded bidirectional synaptic plasticity is composed of switch-like unitary events. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[33]  S. Schultz.  Principles of Neural Science, 4th ed. , 2001 .

[34]  M. Tsodyks,et al.  The Enhanced Storage Capacity in Neural Networks with Low Activity Level , 1988 .

[35]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[36]  E. Kandel,et al.  Long-term habituation of a defensive withdrawal reflex in aplysia. , 1972, Science.

[37]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[38]  F. Crick.  Memory and molecular turnover. , 1984, Nature.

[39]  E. Kandel,et al.  A Neuronal Isoform of the Aplysia CPEB Has Prion-Like Properties , 2003, Cell.

[40]  J. Wixted,et al.  On the Form of Forgetting , 1991 .

[41]  G. Parisi.  A memory which forgets , 1986 .

[42]  F. Crick.  Neurobiology: Memory and molecular turnover , 1984, Nature.