Synaptic Delay Learning in Pulse-Coupled Neurons

We present rules for the unsupervised learning of coincidence between excitatory postsynaptic potentials (EPSPs) by the adjustment of post-synaptic delays between the transmitter binding and the opening of ion channels. Starting from a gradient descent scheme, we develop a robust and more biological threshold rule by which EPSPs from different synapses can be gradually pulled into coincidence. The synaptic delay changes are determined from the summed potentialat the site where the coincidence is to be establishedand from postulated synaptic learning functions that accompany the individual EPSPs. According to our scheme, templates for the detection of spatiotemporal patterns of synaptic activation can be learned, which is demonstrated by computer simulation. Finally, we discuss possible relations to biological mechanisms.

[1]  W. Singer,et al.  Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation , 1993, Trends in Neurosciences.

[2]  J. J. Hopfield,et al.  Pattern recognition computation using action potential timing for stimulus representation , 1995, Nature.

[3]  Amir F. Atiya,et al.  How delays affect neural dynamics and learning , 1994, IEEE Trans. Neural Networks.

[4]  J J Hopfield,et al.  Neural computation by concentrating information in time. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[5]  R. Eckmiller,et al.  Information processing in biology-inspired pulse coded neural networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[6]  J. Eggermont The Correlative Brain: Theory and Experiment in Neural Interaction , 1990 .

[7]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  D. Hebb Textbook of psychology , 1958 .

[9]  Bertil Hille,et al.  Modulation of ion-channel function by G-protein-coupled receptors , 1994, Trends in Neurosciences.

[10]  Gèunther Palm,et al.  Neural Assemblies: An Alternative Approach to Artificial Intelligence , 1982 .

[11]  Jack D. Cowan,et al.  DYNAMICS OF SELF-ORGANIZED DELAY ADAPTATION , 1999 .

[12]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[13]  DeLiang Wang,et al.  Temporal pattern processing , 1998 .

[14]  M. Abeles Role of the cortical neuron: integrator or coincidence detector? , 1982, Israel journal of medical sciences.

[15]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[16]  Alexander H. Waibel,et al.  The Tempo 2 Algorithm: Adjusting Time-Delays By Supervised Learning , 1990, NIPS.

[17]  E. W. Kairiss,et al.  Hebbian synapses: biophysical mechanisms and algorithms. , 1990, Annual review of neuroscience.

[18]  C E Carr,et al.  Processing of temporal information in the brain. , 1993, Annual review of neuroscience.

[19]  Robert Miller,et al.  Cortico-hippocampal interplay: Self-organizing phase-locked loops for indexing memory , 1989, Psychobiology.

[20]  D. Clapham,et al.  G-protein regulation of ion channels , 1995, Current Opinion in Neurobiology.

[21]  B. Sakmann,et al.  Active propagation of somatic action potentials into neocortical pyramidal cell dendrites , 1994, Nature.

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

[23]  Terrence J. Sejnowski,et al.  Synaptic currents, neuromodulation, and kinetic models , 1998 .