Presynaptic Learning and Memory with a Persistent Firing Neuron and a Habituating Synapse: a Model of Short Term Persistent Habituation

Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

[1]  L. Abbott.  Cortical Remapping through Spike Timing-Dependent Plasticity , 2005 .

[2]  Stephen Marsland,et al.  Using habituation in machine learning , 2009, Neurobiology of Learning and Memory.

[3]  C. Balkenius.  Attention, habituation and conditioning: toward a computational model , 2000 .

[4]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[5]  R. F. Thompson,et al.  Habituation: a model phenomenon for the study of neuronal substrates of behavior. , 1966, Psychological review.

[6]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[7]  Douglas A. Baxter,et al.  Short-term plasticity in a computational model of the tail-withdrawal circuit in Aplysia , 2007, Neurocomputing.

[8]  Donald A. Wilson,et al.  Habituation revisited: An updated and revised description of the behavioral characteristics of habituation , 2009, Neurobiology of Learning and Memory.

[9]  N. Spruston,et al.  Slow integration leads to persistent action potential firing in distal axons of coupled interneurons , 2010, Nature Neuroscience.

[10]  T. Martin McGinnity,et al.  An STDP Training Algorithm for a Spiking Neural Network with Dynamic Threshold Neurons , 2010, Int. J. Neural Syst..

[11]  E. De Schutter,et al.  Resonant Synchronization in Heterogeneous Networks of Inhibitory Neurons , 2003, The Journal of Neuroscience.

[12]  B. Raman,et al.  Olfactory learning and spike timing dependent plasticity , 2008, Communicative & integrative biology.

[13]  T. Martin McGinnity,et al.  An FPGA Hardware/Software Co-Design towards Evolvable Spiking Neural Networks for Robotics Application , 2010, Int. J. Neural Syst..

[14]  Iren Valova,et al.  Learning and data clustering with an RBF-based spiking neuron network , 2006, J. Exp. Theor. Artif. Intell..

[15]  C. Rankin,et al.  Factors affecting habituation and recovery from habituation in the nematode Caenorhabditis elegans. , 1992, Behavioral neuroscience.

[16]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[17]  Andrew Philippides,et al.  Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus , 2010, PLoS Comput. Biol..

[18]  Timothy E Holy,et al.  Pavlov's moth: olfactory learning and spike timing–dependent plasticity , 2008, Nature Neuroscience.

[19]  R. Kempter,et al.  Hebbian learning and spiking neurons , 1999 .

[20]  Patrick D. Roberts,et al.  Spike timing dependent synaptic plasticity in biological systems , 2002, Biological Cybernetics.

[21]  Snjezana Soltic,et al.  Knowledge Extraction from Evolving Spiking Neural Networks with Rank Order Population Coding , 2010, Int. J. Neural Syst..

[22]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[23]  I R Bell,et al.  Increased limbic system symptomatology and sensitizability of young adults with chemical and noise sensitivities. , 1995, Environmental research.

[24]  Fred H. Gage,et al.  A role for adult TLX-positive neural stem cells in learning and behaviour , 2008, Nature.

[25]  Alexander K. Vidybida,et al.  Testing of information condensation in a model reverberating spiking neural network , 2010, Int. J. Neural Syst..

[26]  H. Robinson,et al.  Threshold firing frequency-current relationships of neurons in rat somatosensory cortex: type 1 and type 2 dynamics. , 2004, Journal of neurophysiology.

[27]  Eric R. Kandel,et al.  Long-Term Habituation of a Defensive Withdrawal Reflex in Aplysia , 1972, Science.

[28]  Giacomo Indiveri,et al.  Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[29]  Wulfram Gerstner,et al.  A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.

[30]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[31]  T Natschläger,et al.  Spatial and temporal pattern analysis via spiking neurons. , 1998, Network.

[32]  Luping Shi,et al.  Axonal Slow Integration Induced Persistent Firing Neuron Model , 2011, ICONIP.

[33]  D. Tank,et al.  Persistent neural activity: prevalence and mechanisms , 2004, Current Opinion in Neurobiology.

[34]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[35]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[36]  E. Kandel.  The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses , 2001, Science.

[37]  Prahlad Vadakkepat,et al.  Stimulus dependent habituation in a bottom - up brain inspired model of learning and memory , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[38]  C. Gallistel,et al.  Memory and the Computational Brain , 2009 .

[39]  Irving Kupfermann,et al.  Neuronal Correlates of Habituation and Dishabituation of the Gill-Withdrawal Reflex in Aplysia , 1970, Science.

[40]  J ValdésJulio,et al.  2006 Special issue , 2006 .

[41]  Eric Nichols,et al.  Case Study on a Self-Organizing Spiking Neural Network for Robot Navigation , 2010, Int. J. Neural Syst..

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

[43]  E R Kandel,et al.  Neuronal Mechanisms of Habituation and Dishabituation of the Gill-Withdrawal Reflex in Aplysia , 1970, Science.

[44]  J. C. Stanley.  Computer simulation of a model of habituation , 1976, Nature.

[45]  Luping Shi,et al.  Artificial cognitive memory—changing from density driven to functionality driven , 2011 .

[46]  L. Abbott,et al.  Extending the effects of spike-timing-dependent plasticity to behavioral timescales. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[47]  DeLiang Wang,et al.  A Neural Model of Synaptic Plasticity Underlying Short-term and Long-term Habituation , 1993, Adapt. Behav..

[48]  P. Groves,et al.  Habituation: a dual-process theory. , 1970, Psychological review.

[49]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.