From: IN%"unni@neuro.cs.gmr.com" "K.P.Unnikrishnan" 22-FEB-1993 00:53:27.44 To: IN%"Connectionists@CS.CMU.EDU" CC: Subj: A NEURAL COMPUTATION course reading list Folks: Here is the reading list for a course I offered last semester at Univ. of Michigan. Unnikrishnan --------------------------------------------------------------- READING LIST FOR THE COURSE "NEURAL COMPUTATION" EECS-598-6 (FALL 1992), UNIVERSITY OF MICHIGAN INSTRUCTOR: K. P. UNNIKRISHNAN ----------------------------------------------- A. COMPUTATION AND CODING IN THE NERVOUS SYSTEM 1. Hodgkin, A.L., and Huxley, A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500-544 (1952). 2a. Del Castillo, J., and Katz, B. Quantal components of the end-plate potential. J. Physiol. 124, 560-573 (1954). 2b. Del Castillo, J., and Katz, B. Statistical factors involved in neuromuscular facilitation and depression. J. Physiol. 124, 574-585 (1954). 3. Rall, W. Cable theory for dendritic neurons. In: Methods in neural modeling (Koch and Segev, eds.) pp. 9-62 (1989). 4. Koch, C., and Poggio, T. Biophysics of computation: neurons, synapses and membranes. In: Synaptic function (Edelman, Gall, and Cowan, eds.) pp. 637-698 (1987). B. SENSORY PROCESSING IN VISUAL AND AUDITORY SYSTEMS 1. Werblin, F.S., and Dowling, J.E. Organization of the retina of the mudpuppy, Necturus maculosus: II. Intracellular recording. J. Neurophysiol. 32, 339-355 (1969). 2a. Barlow H.B., and Levick, W.R. The mechanism of directionally selective units in rabbit's retina. J. Physiol. 178, 477-504 (1965). 2b. Lettvin, J.Y., Maturana, H.R., McCulloch, W.S., and Pitts, W.H. What the frog's eye tells the frogs's brain. Proc. IRE 47, 1940-1951 (1959). 3. Hubel, D.H., and Wiesel, T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160, 106-154 (1962). 4a. Suga, N. Cortical computational maps for auditory imaging. Neural Networks, 3, 3-21 (1990). 4b. Simmons, J.A. A view of the world through the bat's ear: the formation of acoustic images in echolocation. Cognition, 33 155-199 (1989). C. MODELS OF SENSORY SYSTEMS 1. Hect,S., Shlaer, S., and Pirenne, M.H. Energy, quanta, and vision. J. Gen. Physiol. 25, 819-840 (1942). 2. Julesz, B., and Bergen, J.R. Textons, the fundamental elements in preattentive vision and perception of textures. Bell Sys. Tech. J. 62, 1619-1645 (1983). 3a. Harth, E., Unnikrishnan, K.P., and Pandya, A.S. The inversion of sensory processing by feedback pathways: a model of visual cognitive functions. science 237, 184-187 (1987). 3b. Harth, E., Pandya, A.S., and Unnikrishnan, K.P. Optimization of cortical responses by feedback modification and synthesis of sensory afferents. A model of perception and rem sleep. Concepts Neurosci. 1, 53-68 (1990). 3c. Koch, C. The action of the corticofugal pathway on sensory thalamic nuclei: A hypothesis. Neurosci. 23, 399-406 (1987). 4a. Singer, W. et al., Formation of cortical cell assemblies. In: CSH Symposia on Quant. Biol. 55, pp. 939-952 (1990). 4b. Eckhorn, R., Reitboeck, H.J., Arndt, M., and Dicke, P. Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Comp. 293-307 (1990). 5. Reichardt, W., and Poggio, T. Visual control of orientation behavior in the fly. Part I. A quantitative analysis. Q. Rev. Biophys. 9, 311-375 (1976). D. ARTIFICIAL NEURAL NETWORKS 1a. Block, H.D. The perceptron: a model for brain functioning. Rev. Mod. Phy. 34, 123-135 (1962). 1b. Minsky, M.L., and Papert, S.A. Perceptrons. pp. 62-68 (1988). 2a. Hornik, K., Stinchcombe, M., and White, H. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366 (1989). 2b. Lapedes, A., and Farber, R. How neural nets work. In: Neural Info. Proc. Sys. (Anderson, ed.) pp. 442-456 (1987). 3a. Ackley, D.H., Hinton, G.E., and Sejnowski, T.J. A learning algorithm for boltzmann machines. Cog. Sci. 9, 147-169 (1985). 3b. Hopfield, J.J. Learning algorithms and probability distributions in feed-forward and feed-back networks. PNAS, USA. 84, 8429-8433 (1987). 4. Tank, D.W., and Hopfield, J.J. Simple neural optimization networks: An A/D converter, signal decision circuit, and linear programming circuit. IEEE Tr. Cir. Sys. 33, 533-541 (1986). E. NEURAL NETWOK APPLICATIONS 1. LeCun, Y., et al., Backpropagation applied to handwritten zip code recognition. Neural Comp. 1, 541-551 (1990). 2. Lapedes, A., and Farber, R. Nonlinear signal processing using neural networks. LA-UR-87-2662, Los Alamos Natl. Lab. (1987). 3. Unnikrishnan, K.P., Hopfield, J.J., and Tank, D.W. Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections. IEEE Tr. ASSP. 39, 698-713 (1991). 4a. De Vries, B., and Principe, J.C. The gamma model - a new neural model for temporal processing. Neural Networks 5, 565-576 (1992). 4b. Poddar, P., and Unnikrishnan, K.P. Memory neuron networks: a prolegomenon. GMR-7493, GM Res. Labs. (1991). 5. Narendra, K.S., and Parthasarathy, K. Gradient methods for the optimization of dynamical systems containing neural networks. IEEE Tr. NN 2, 252-262 (1991). F. HARDWARE IMPLEMENTATIONS 1a. Mahowald, M.A., and Mead, C. Silicon retina. In: Analog VLSI and neural systems (Mead). pp. 257-278 (1989). 1b. Mahowald, M.A., and Douglas, R. A silicon neuron. Nature 354, 515-518 (1991). 2. Mueller, P. et al. Design and fabrication of VLSI components for a general purpose analog computer. In: Proc. IEEE workshop VLSI neural sys. (Mead, ed.) pp. xx-xx (1989). 3. Graf, H.P., Jackel, L.D., and Hubbard, W.E. VLSI implementation of a neural network model. Computer 2, 41-49 (1988). G. ISSUES ON LEARNING 1. Geman, S., Bienenstock, E., and Doursat, R. Neural networks and the bias/variance dilema. Neural Comp. 4, 1-58 (1992). 2. Brown, T.H., Kairiss, E.W., and Keenan, C.L. Hebbian synapses: Biophysical mechanisms and algorithms. Ann. Rev. Neurosci. 13, 475-511 (1990). 3. Haussler, D. Quantifying inductive bias: AI learning algorithms and valiant's learning framework. AI 36, 177-221 (1988). 4. Reeke, G.N. Jr., and Edelman, G.M. Real brains and artificial intelligence. Daedalus 117, 143-173 (1988). 5. White, H. Learning in artificial neural networks: a statistical perspective. Neural Comp. 1, 425-464 (1989). ---------------------------------------------------------------------- SUPPLEMENTAL READING Nehr, E., and Sakmann, B. Single channel currents recorded from membrane of denervated frog muscle fibers. Nature 260, 779-781 (1976). Rall, W. Core conductor theory and cable properties of neurons. In: Handbook Physiol. (Brrokhart, Mountcastle, and Kandel eds.) pp. 39-97 (1977). Shepherd, G.M., and Koch, C. Introduction to synaptic circuits. In: The synaptic organization of the brain (Shepherd, ed.) pp. 3-31 (1990). Junge, D. Synaptic transmission. In: nerve and muscle excitation (Junge) pp. 149-178 (1981). Scott, A.C. The electrophysics of a nerve fiber. Rev. Mod. Phy. 47, 487-533 (1975). Enroth-Cugell, C., and Robson, J.G. The contrast sensitivity of retinal ganglion cells of the cat. J. Physiol. 187, 517-552 (1966). Felleman, D.J., and Van Essen, D.C. Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47 (1991). Julesz, B. Early vision and focal attention. Rev. Mod. Phy.63, 735-772 (1991). Sejnowski, T.J., Koch, C., and Churchland, P.S. Computational neuroscience. Science 241, 1299-1302 (1988). Churchland, P.S., and Sejnowski, T.J. Perspectives on Cognitive Neuroscience. Science 242, 741-745 (1988). McCulloch, W.S., and Pitts, W. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophy. 5, 115-133 (1943). Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities. PNAS, USA. 79, 2554-2558 (1982). Hopfield, J.J. Neurons with graded responses have collective computational properties like those of two-state neurons. PNAS, USA. 81, 3088-3092 (1984). Hinton, G.E., and Sejnowski, T.J. Optimal perceptual inference. Proc. IEEE CVPR. 448-453 (1983). Rumelhart, D.E., Hinton, G.E., and Williams, R.J. Learning representations by back-propagating errors. Nature 323, 533-536 (1986). Unnikrishnan, K.P., and Venugopal, K.P. Learning in connectionist networks using the Alopex algorithm. Proc. IEEE IJCNN. I-926 - I-931 (1992). Cowan, J.D., and Sharp, D.H. Neural nets. Quart. Rev. Biophys. 21, 365-427 (1988). Lippmann, R.P. An introduction to computing with neural nets. IEEE ASSP Mag. 4, 4-22 (1987). Sompolinsky, H. Statistical mechanics of neural networks. Phy. Today 41, 70-80 (1988). Hinton, G.E. Connectionist learning procedures. Art. Intel. 40, 185-234 (1989). Return-path: Received: from ccsun.unicamp.br by ccvax.unicamp.br (PMDF #2801 ) id <01GUZWTC3IIO8WVZSB@ccvax.unicamp.br>; Sun, 21 Feb 1993 20:57:51 BSC (-0300 C) Received: from dynamo.ecn.purdue.edu by ccsun.unicamp.br (4.1/SMI-4.0) id AA11727; Sun, 21 Feb 93 20:54:22 BSC Received: from jovian.ecn.purdue.edu by dynamo.ecn.purdue.edu (5.65/1.32jrs) id AA13217; Sun, 21 Feb 93 18:52:13 -0500 Received: from bank.ecn.purdue.edu by jovian.ecn.purdue.edu (5.65/1.32jrs) id AA10321; Sun, 21 Feb 93 18:52:10 -0500 Received: from Q.CS.CMU.EDU by bank.ecn.purdue.edu (5.65/1.32jrs) id AA17895; Sun, 21 Feb 93 18:52:04 -0500 Received: from Q.CS.CMU.EDU by Q.CS.CMU.EDU id aa14198; 21 Feb 93 2:17:00 EST Received: from DST.BOLTZ.CS.CMU.EDU by Q.CS.CMU.EDU id aa14196; 21 Feb 93 1:50:47 EST Received: from dst.boltz.cs.cmu.edu by DST.BOLTZ.CS.CMU.EDU id aa03333; 21 Feb 93 1:48:31 EST Received: from cs.cmu.edu by B.GP.CS.CMU.EDU id aa24455; 20 Feb 93 15:20:25 EST Received: from neuro.cs.gmr.com by CS.CMU.EDU id aa18322; 20 Feb 93 15:19:59 EST Received: by neuro.cs.gmr.com (4.1/GMR-1.2) id AA22392; Sat, 20 Feb 93 14:57:13 EST Date: 20 Feb 1993 14:57:13 -0500 (EST) From: "K.P.Unnikrishnan" Subject: A NEURAL COMPUTATION course reading list To: Connectionists@CS.CMU.EDU Message-id: <9302201957.AA22392@neuro.cs.gmr.com> X-Envelope-to: infomed, sabbatini Content-transfer-encoding: 7BIT