Neural Systems Engineering

The quest to build an electronic computer based on the operational principles of biological brains has attracted attention over many years. The hope is that, by emulating the brain, it will be possible to capture some of its capabilities and thereby bridge the very large gulf that separates mankind from machines. At present, however, knowledge about the operational principles of the brain is far from complete, so attempts at emulation must employ a great deal of assumption and guesswork to fill the gaps in the experimental evidence. The sheer scale and complexity of the human brain still defies attempts to model it in its entirety at the neuronal level, but Moore's Law is closing this gap and machines with the potential to emulate the brain (so far as we can estimate the computing power required) are no more than a decade or so away. Do computer engineers have something to contribute, alongside neuroscientists, psychologists, mathematicians and others, to the understanding of brain and mind, which remains as one of the great frontiers of science?

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

[2]  Jim Austin,et al.  THE ADVANCED UNCERTAIN REASONING ARCHITECTURE, AURA , 1998 .

[3]  Pentti Kanerva,et al.  Sparse Distributed Memory , 1988 .

[4]  Florentin Wörgötter,et al.  How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model , 2004, Neural Computation.

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

[6]  B. Katz,et al.  Spontaneous subthreshold activity at motor nerve endings , 1952, The Journal of physiology.

[7]  V. Mountcastle,et al.  An organizing principle for cerebral function : the unit module and the distributed system , 1978 .

[8]  J. White.  Neural engineering , 2007, Annals of Biomedical Engineering.

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

[10]  Steve Furber,et al.  High-performance computing for systems of spiking neurons , 2006 .

[11]  E. D. Adrian,et al.  The Basis of Sensation , 1928, The Indian Medical Gazette.

[12]  Florentin Wörgötter,et al.  A neuromorphic depth-from-motion vision model with STDP adaptation , 2006, IEEE Transactions on Neural Networks.

[13]  Rufin van Rullen,et al.  Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex , 2001, Neural Computation.

[14]  W. Rall.  Branching dendritic trees and motoneuron membrane resistivity. , 1959, Experimental neurology.

[15]  Joy Bose,et al.  Sparse Distributed Memory Using Rank-Order Neural Codes , 2007, IEEE Transactions on Neural Networks.

[16]  H. Klar,et al.  Cascadable digital emulator IC for 16 biological neurons , 1993, 1993 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[17]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

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

[19]  Andrew D. Brown,et al.  On-chip and inter-chip networks for modeling large-scale neural systems , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[20]  Sharon Begley,et al.  The Mind and the Brain: Neuroplasticity and the Power of Mental Force , 2002 .

[21]  Terrence J Sejnowski,et al.  Communication in Neuronal Networks , 2003, Science.

[22]  M. Geike,et al.  Emulation engine for spiking neurons and adaptive synaptic weights , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

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

[24]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[25]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[26]  Tobi Delbrück,et al.  A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change , 2006, 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers.

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

[28]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.

[29]  T. Lømo,et al.  The discovery of long-term potentiation. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[30]  T. Schoenauer,et al.  MASPINN: novel concepts for a neuroaccelerator for spiking neural networks , 1999, Other Conferences.

[31]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[32]  James M. Bower,et al.  The Book of GENESIS , 1994, Springer New York.

[33]  Jihan Zhu,et al.  FPGA Implementations of Neural Networks - A Survey of a Decade of Progress , 2003, FPL.

[34]  Heinrich Klar,et al.  A SIMD/dataflow architecture for a neurocomputer for spike-processing neural networks (NESPINN) , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[35]  W. Gerstner,et al.  Time structure of the activity in neural network models. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[36]  Stephen B. Furber,et al.  Chain: A Delay-Insensitive Chip Area Interconnect , 2002, IEEE Micro.

[37]  Heik Heinrich Hellmich,et al.  Synaptic plasticity in spiking neural networks (SP2INN): a system approach , 2003, IEEE Trans. Neural Networks.

[38]  R. Douglas,et al.  A Quantitative Map of the Circuit of Cat Primary Visual Cortex , 2004, The Journal of Neuroscience.

[39]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[40]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[41]  Eugene M. Izhikevich,et al.  Polychronization: Computation with Spikes , 2006, Neural Computation.

[42]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[43]  Randall C. O'Reilly,et al.  Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.

[44]  H. C. LONGUET-HIGGINS,et al.  Non-Holographic Associative Memory , 1969, Nature.

[45]  Steve Temple,et al.  Sparse distributed memory using N-of-M codes , 2004, Neural Networks.

[46]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[47]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.