Phase Precession and Recession with STDP and Anti-STDP

We show that standard, Hebbian spike-timing dependent plasticity (STDP) induces the precession of the firing phase of neurons in oscillatory networks, while anti-Hebbian STDP induces phase recession. In networks that are subject to oscillatory inhibition, the intensity of excitatory input relative to the inhibitory one determines whether the phase can precess due to STDP or whether the phase is fixed. This phenomenon can give a very simple explanation to the experimentally-observed hippocampal phase precession. Modulation of STDP can lead, through precession and recession, to the synchronization of the firing of a trained neuron to a target phase.

[1]  Kenneth D. Miller,et al.  Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell , 1997, Neural Computation.

[2]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[3]  J. O’Keefe,et al.  Phase relationship between hippocampal place units and the EEG theta rhythm , 1993, Hippocampus.

[4]  Robert A. Legenstein,et al.  What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? , 2005, Neural Computation.

[5]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[6]  L. Abbott,et al.  Homeostasis and Learning through Spike-Timing Dependent Plasticity , 2003 .

[7]  Wulfram Gerstner,et al.  Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning , 2001, Neural Computation.

[8]  Rajesh P. N. Rao,et al.  Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning , 2001, Neural Computation.

[9]  M. R. Mehta,et al.  Role of experience and oscillations in transforming a rate code into a temporal code , 2002, Nature.

[10]  Patrick D. Roberts,et al.  Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning , 1999, Journal of Computational Neuroscience.

[11]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[12]  Lucas C. Parra,et al.  Maximising Sensitivity in a Spiking Network , 2004, NIPS.

[13]  J J Hopfield,et al.  Learning rules and network repair in spike-timing-based computation networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  B. McNaughton,et al.  Population dynamics and theta rhythm phase precession of hippocampal place cell firing: A spiking neuron model , 1998, Hippocampus.

[15]  Y. Dan,et al.  Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.

[16]  D. Johnston,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .

[17]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[18]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[19]  Sander M. Bohte,et al.  Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity , 2004, BNAIC.

[20]  Y. Dan,et al.  Hebbian depression of isolated neuromuscular synapses in vitro. , 1992, Science.

[21]  G. Buzsáki.  Theta Oscillations in the Hippocampus , 2002, Neuron.

[22]  Wulfram Gerstner,et al.  Spiking Neuron Models: An Introduction , 2002 .

[23]  B. Kosco.  Differential Hebbian learning , 1987 .

[24]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[25]  Jean-Pascal Pfister,et al.  Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model , 2004, NIPS.

[26]  V. Han,et al.  Synaptic plasticity in a cerebellum-like structure depends on temporal order , 1997, Nature.

[27]  M. Quirk,et al.  Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields , 2000, Neuron.

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

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

[30]  Gal Chechik,et al.  Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization , 2003, Neural Computation.

[31]  Maria Marinaro,et al.  A learning rule for place fields in a cortical model: Theta phase precession as a network effect , 2005, Hippocampus.

[32]  Razvan V. Florian,et al.  Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity , 2007, Neural Computation.

[33]  Razvan V. Florian.  A reinforcement learning algorithm for spiking neural networks , 2005, Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05).

[34]  B. Sakmann,et al.  Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex , 1999, Nature Neuroscience.