Large developing axonal arbors using a distributed and locally-reprogrammable address-event receiver

We have designed a distributed and locally reprogrammable address event receiver. Incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change input address, allowing neurons to implement a biologically realistic learning rule locally, with both synapse formation and elimination.

[1]  R. Sperry.  CHEMOAFFINITY IN THE ORDERLY GROWTH OF NERVE FIBER PATTERNS AND CONNECTIONS. , 1963, Proceedings of the National Academy of Sciences of the United States of America.

[2]  J. Szentágothai,et al.  Quantitative histological analysis of the cerebellar cortex in the cat. I. Number and arrangement in space of the Purkinje cells. , 1971, Brain research.

[3]  J. Szentágothai,et al.  Quantitative histological analysis of the cerebellar cortex in the cat. IV. Mossy fiber-Purkinje cell numerical transfer. , 1972, Brain research.

[4]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[5]  Robert B. Allen,et al.  Stochastic Learning Networks and their Electronic Implementation , 1987, NIPS.

[6]  S. Tam,et al.  An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.

[7]  Misha Anne Mahowald,et al.  VLSI analogs of neuronal visual processing: a synthesis of form and function , 1992 .

[8]  Massimo A. Sivilotti,et al.  Wiring considerations in analog VLSI systems, with application to field-programmable networks , 1992 .

[9]  P. Rakić,et al.  Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[10]  B. Finlay,et al.  Regulation of retinal ganglion cell axon arbor size by target availability: Mechanisms of compression and expansion of the retinotectal projection , 1994, The Journal of comparative neurology.

[11]  Kenneth D. Miller,et al.  Equivalence of a Sprouting-and-Retraction Model and Correlation-Based Plasticity Models of Neural Development , 1998, Neural Computation.

[12]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[13]  U. Cilingiroglu,et al.  A 4-transistor Euclidean distance cell for analog classifiers , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[14]  Andreas G. Andreou,et al.  AER image filtering architecture for vision-processing systems , 1999 .

[15]  Rodney J. Douglas,et al.  A pulse-coded communications infrastructure for neuromorphic systems , 1999 .

[16]  Kwabena Boahen,et al.  Point-to-point connectivity between neuromorphic chips using address events , 2000 .

[17]  L. Abbott,et al.  Cortical Development and Remapping through Spike Timing-Dependent Plasticity , 2001, Neuron.

[18]  M. Aminoff.  Principles of Neural Science. 4th edition , 2001 .

[19]  K. Svoboda,et al.  Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex , 2002, Nature.

[20]  Kwabena Boahen,et al.  Topographic Map Formation by Silicon Growth Cones , 2002, NIPS.

[21]  H. Cline.  Sperry and Hebb: oil and vinegar? , 2003, Trends in Neurosciences.

[22]  Bartlett W. Mel,et al.  Cortical rewiring and information storage , 2004, Nature.

[23]  Kwabena Boahen,et al.  A burst-mode word-serial address-event link-I: transmitter design , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[24]  Giacomo Indiveri,et al.  A VLSI spike-driven dynamic synapse which learns only when necessary , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[25]  R. Sarpeshkar,et al.  Brain power - borrowing from biology makes for low power computing [bionic ear] , 2006, IEEE Spectrum.

[26]  Gert Cauwenberghs,et al.  Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses , 2007, IEEE Transactions on Neural Networks.