Memory recall and spike-frequency adaptation.

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

[1]  P. Morse.  Annals of Physics , 1957, Nature.

[2]  E. G. Jones.  Cerebral Cortex , 1987, Cerebral Cortex.

[3]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[4]  D. Amit,et al.  Statistical mechanics of neural networks near saturation , 1987 .

[5]  G. Tononi,et al.  Sleep and synaptic homeostasis: a hypothesis , 2003, Brain Research Bulletin.

[6]  John J Hopfield,et al.  Neurodynamics of mental exploration , 2009, Proceedings of the National Academy of Sciences.

[7]  October I.  Physical Review Letters , 2022 .

[8]  M. Hasselmo,et al.  In vivo cholinergic modulation of the cellular properties of medial entorhinal cortex neurons , 2013, The Journal of physiology.

[9]  Brain research bulletin , 1984, Pharmacology Biochemistry and Behavior.

[10]  Boris S. Gutkin,et al.  The effects of cholinergic neuromodulation on neuronal phase-response curves of modeled cortical neurons , 2009, Journal of Computational Neuroscience.

[11]  R. Monasson,et al.  Transitions between Spatial Attractors in Place-Cell Models. , 2015, Physical review letters.

[12]  HighWire Press,et al.  American journal of physiology. Cell physiology , 1977 .

[13]  John Mann.  The Frontiers of Psychology , 1935, Nature.

[14]  M. Hasselmo,et al.  Modes and Models of Forebrain Cholinergic Neuromodulation of Cognition , 2011, Neuropsychopharmacology.

[15]  M. Hasselmo,et al.  Cholinergic modulation of cortical associative memory function. , 1992, Journal of neurophysiology.

[16]  D. Fayuk,et al.  The Journal of Physiology , 1978, Medical History.

[17]  Nowak,et al.  Fully connected neural networks with self-control of noise levels. , 1989, Physical review letters.

[18]  D. Wilkin,et al.  Neuron , 2001, Brain Research.

[19]  Eshel Ben-Jacob,et al.  Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation , 2015, PLoS Comput. Biol..

[21]  T. Sejnowski,et al.  Effects of cholinergic modulation on responses of neocortical neurons to fluctuating input. , 1997, Cerebral cortex.

[22]  Physical Review , 1965, Nature.

[23]  Pontus B Persson,et al.  American Journal of Physiology-regulatory, Integrative, and Comparative Physiology , 2022 .

[24]  Athena Akrami,et al.  Lateral thinking, from the Hopfield model to cortical dynamics , 2012, Brain Research.

[25]  H E M.  Journal of Neurophysiology , 1938, Nature.

[26]  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.

[27]  J. Herskowitz,et al.  Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.

[28]  Itamar Lerner,et al.  Internally- and externally-driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation , 2014, Front. Psychol..

[29]  David E. Breen,et al.  Calculating Center of Mass in an Unbounded 2D Environment , 2008, J. Graph. Tools.

[30]  Sandro Romani,et al.  Neural Network Model of Memory Retrieval , 2015, Front. Comput. Neurosci..

[31]  B. S. Brown,et al.  Reduction of spike frequency adaptation and blockade of M‐current in rat CA1 pyramidal neurones by linopirdine (DuP 996), a neurotransmitter release enhancer , 1995, British journal of pharmacology.

[32]  Xiao-Jing Wang,et al.  Robust Spatial Working Memory through Homeostatic Synaptic Scaling in Heterogeneous Cortical Networks , 2003, Neuron.