Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

[1]  Claude E. Shannon,et al.  A Mathematical Theory of Communications , 1948 .

[2]  D. Mackay.  The Epistemological Problem for Automata , 1956 .

[3]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[4]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

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

[6]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[7]  W. Reichardt,et al.  Autocorrelation, a principle for the evaluation of sensory information by the central nervous system , 1961 .

[8]  R. W. Rodieck.  Quantitative analysis of cat retinal ganglion cell response to visual stimuli. , 1965, Vision research.

[9]  J. Konorski.  Integrative activity of the brain , 1967 .

[10]  W. Hays.  Statistical theory. , 1968, Annual review of psychology.

[11]  J. Dowling,et al.  Organization of the retina of the mudpuppy, Necturus maculosus. II. Intracellular recording. , 1969, Journal of neurophysiology.

[12]  D. Marr.  A theory for cerebral neocortex , 1970, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[13]  R. Rescorla.  A theory of pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement , 1972 .

[14]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[15]  P. O. Bishop,et al.  Orientation specificity of cells in cat striate cortex. , 1974, Journal of neurophysiology.

[16]  R. Solomon,et al.  An opponent-process theory of motivation. I. Temporal dynamics of affect. , 1974, Psychological review.

[17]  J. Jack,et al.  Electric current flow in excitable cells , 1975 .

[18]  W. Precht.  The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.

[19]  T. Sejnowski,et al.  Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.

[20]  J. Movshon,et al.  Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat's visual cortex. , 1978, The Journal of physiology.

[21]  J. Movshon,et al.  Spatial summation in the receptive fields of simple cells in the cat's striate cortex. , 1978, The Journal of physiology.

[22]  A. M. Uttley,et al.  Information transmission in the nervous system , 1979 .

[23]  Geoffrey E. Hinton.  Shape Representation in Parallel Systems , 1981, IJCAI.

[24]  S. Laughlin.  A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[25]  G. Poggio,et al.  Mechanisms of static and dynamic stereopsis in foveal cortex of the rhesus monkey , 1981, The Journal of physiology.

[26]  A. Dickinson.  Conditioning and associative learning. , 1981, British medical bulletin.

[27]  E. Switkes,et al.  Deoxyglucose analysis of retinotopic organization in primate striate cortex. , 1982, Science.

[28]  D. Pollen,et al.  Spatial computation performed by simple and complex cells in the visual cortex of the cat , 1982, Vision Research.

[29]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[30]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[31]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[33]  B. Sakmann,et al.  Single-Channel Recording , 1983, Springer US.

[34]  B. Hille.  Ionic channels of excitable membranes , 2001 .

[35]  J. Rovamo,et al.  Isotropy of cortical magnification and topography of striate cortex , 1984, Vision Research.

[36]  Professor Dr. Guy A. Orban.  Neuronal Operations in the Visual Cortex , 1983, Studies of Brain Function.

[37]  John H. R. Maunsell,et al.  The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies, and individual variability , 1984, Vision Research.

[38]  J. Hopfield.  Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .

[39]  J. van Santen,et al.  Temporal covariance model of human motion perception. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[40]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[41]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[42]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[43]  W. Siebert.  Circuits, Signals and Systems , 1985 .

[44]  R. Linsker,et al.  From basic network principles to neural architecture , 1986 .

[45]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[46]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[47]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[48]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

[49]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.

[50]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[51]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[52]  D. Sparks,et al.  Population coding of saccadic eye movements by neurons in the superior colliculus , 1988, Nature.

[53]  Richard A. Andersen,et al.  A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.

[54]  William Bialek,et al.  Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[55]  G. Ermentrout,et al.  Analysis of neural excitability and oscillations , 1989 .

[56]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[57]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[58]  K. Miller,et al.  Ocular dominance column development: analysis and simulation. , 1989, Science.

[59]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[60]  D. Robinson,et al.  Integrating with neurons. , 1989, Annual review of neuroscience.

[61]  K. H. Britten,et al.  Neuronal correlates of a perceptual decision , 1989, Nature.

[62]  M. Mascagni,et al.  Numerical Methods for Neuronal Modeling 14.1 Introduction , 1989 .

[63]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[64]  Todd K. Leen.  Dynamics of Learning in Recurrent Feature-Discovery Networks , 1990, NIPS.

[65]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[66]  A. L. Humphrey,et al.  Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. , 1990, Journal of neurophysiology.

[67]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[68]  A. Turing,et al.  The chemical basis of morphogenesis. 1953. , 1990, Bulletin of mathematical biology.

[69]  David J C MacKaytS,et al.  Analysis of Linsker's application of Hebbian rules to linear networks , 1990 .

[70]  T. Poggio.  A theory of how the brain might work. , 1990, Cold Spring Harbor symposia on quantitative biology.

[71]  D. Amit,et al.  Quantitative study of attractor neural networks retrieving at low spike rates: II. Low-rate retrieval in symmetric networks , 1991 .

[72]  Steven J. Nowlan,et al.  Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures , 1991 .

[73]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[74]  J. Patlak.  Molecular kinetics of voltage-dependent Na+ channels. , 1991, Physiological reviews.

[75]  C. Koch,et al.  A detailed model of the primary visual pathway in the cat: comparison of afferent excitatory and intracortical inhibitory connection schemes for orientation selectivity , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[76]  J P Miller,et al.  Representation of sensory information in the cricket cercal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons. , 1991, Journal of neurophysiology.

[77]  W. J. Nowack.  Methods in Neuronal Modeling , 1991, Neurology.

[78]  L A Real,et al.  Animal choice behavior and the evolution of cognitive architecture , 1991, Science.

[79]  Christof Koch,et al.  Cortical Cells Should Fire Regularly, But Do Not , 1999, Neural Computation.

[80]  K. Obermayer,et al.  Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[81]  W. Newsome,et al.  Microstimulation in visual area MT: effects on direction discrimination performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[82]  S. W. Kuffler,et al.  From Neuron to Brain: A Cellular and Molecular Approach to the Function of the Nervous System , 1992 .

[83]  Zhaoping Li,et al.  Understanding Retinal Color Coding from First Principles , 1992, Neural Computation.

[84]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[85]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[86]  A. Pece.  Redundancy reduction of a Gabor representation: a possible computational role for feedback from primary visual cortex to lateral geniculate nucleus , 1992 .

[87]  Xiao-Jing Wang,et al.  Alternating and Synchronous Rhythms in Reciprocally Inhibitory Model Neurons , 1992, Neural Computation.

[88]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[89]  Nathan Intrator,et al.  Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions , 1992, Neural Networks.

[90]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[91]  L. Abbott.  Simple diagrammatic rules for solving dendritic cable problems , 1992 .

[92]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[93]  David S. Touretzky,et al.  Neural Representation of Space Using Sinusoidal Arrays , 1993, Neural Computation.

[94]  Mitsuo Kawato,et al.  A forward-inverse optics model of reciprocal connections between visual cortical areas , 1993 .

[95]  J. Wickens.  A Theory of the Striatum , 1993 .

[96]  Joseph J. Atick,et al.  Convergent Algorithm for Sensory Receptive Field Development , 1993, Neural Computation.

[97]  Donald B. Percival,et al.  Spectral Analysis for Physical Applications , 1993 .

[98]  E. Marder,et al.  Activity-dependent regulation of conductances in model neurons. , 1993, Science.

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

[100]  D. Heeger.  Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. , 1993, Journal of neurophysiology.

[101]  K. Obermayer,et al.  Geometry of orientation and ocular dominance columns in monkey striate cortex , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[102]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[103]  Zhaoping Li,et al.  Modeling the Sensory Computations of the Olfactory Bulb , 1994 .

[104]  Zhaoping Li,et al.  Toward a Theory of the Striate Cortex , 1994, Neural Computation.

[105]  Kenneth D. Miller,et al.  The Role of Constraints in Hebbian Learning , 1994, Neural Computation.

[106]  L. Abbott,et al.  Modeling state-dependent inactivation of membrane currents. , 1994, Biophysical journal.

[107]  X. Wang.  Multiple dynamical modes of thalamic relay neurons: Rhythmic bursting and intermittent phase-locking , 1994, Neuroscience.

[108]  T. H. Brown,et al.  Efficient mapping from neuroanatomical to electrotonic space , 1994 .

[109]  Zhaoping Li,et al.  Efficient stereo coding in the multiscale representation , 1994 .

[110]  W. Schultz,et al.  Importance of unpredictability for reward responses in primate dopamine neurons. , 1994, Journal of neurophysiology.

[111]  L F Abbott,et al.  Decoding neuronal firing and modelling neural networks , 1994, Quarterly Reviews of Biophysics.

[112]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[113]  KD Miller.  A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[114]  R. Zemel.  A minimum description length framework for unsupervised learning , 1994 .

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

[116]  Terence D. Sanger,et al.  Theoretical Considerations for the Analysis of Population Coding in Motor Cortex , 1994, Neural Computation.

[117]  Terrence J. Sejnowski,et al.  Spatial Representations in the Parietal Cortex May Use Basis Functions , 1994, NIPS.

[118]  W. Bialek,et al.  Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[119]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[120]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[121]  S. Nelson,et al.  An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[122]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[123]  E. Marder,et al.  Selective regulation of current densities underlies spontaneous changes in the activity of cultured neurons , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[124]  L F Abbott,et al.  Transfer of coded information from sensory to motor networks , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[125]  David R. Shanks,et al.  The Psychology of Associative Learning , 1995 .

[126]  Peter Dayan,et al.  Bee foraging in uncertain environments using predictive hebbian learning , 1995, Nature.

[127]  A. Dickinson,et al.  Reward-related signals carried by dopamine neurons. , 1995 .

[128]  Idan Segev,et al.  The morphoelectrotonic transform: a graphical approach to dendritic function , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[129]  David Mumford,et al.  Neuronal Architectures for Pattern-theoretic Problems , 1995 .

[130]  K. Miller,et al.  Synaptic Economics: Competition and Cooperation in Synaptic Plasticity , 1996, Neuron.

[131]  Yoram Baram,et al.  Multidimensional density shaping by sigmoids , 1996, IEEE Trans. Neural Networks.

[132]  L. Abbott,et al.  A model of multiplicative neural responses in parietal cortex. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[133]  E. Marder,et al.  Principles of rhythmic motor pattern generation. , 1996, Physiological reviews.

[134]  P. Dayan,et al.  A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[135]  Bernd Girod,et al.  Subband Image Coding , 1996 .

[136]  William R. Softky,et al.  Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. , 1996, Journal of neurophysiology.

[137]  K. Miller.  Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns* , 1996 .

[138]  Zhaoping Li,et al.  A Theory of the Visual Motion Coding in the Primary Visual Cortex , 1996, Neural Computation.

[139]  H. Markram,et al.  Redistribution of synaptic efficacy between neocortical pyramidal neurons , 1996, Nature.

[140]  B. Olshausen.  Learning linear, sparse, factorial codes , 1996 .

[141]  Barak A. Pearlmutter,et al.  A Context-Sensitive Generalization of ICA , 1996 .

[142]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[143]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[144]  S. Lisberger,et al.  The Cerebellum: A Neuronal Learning Machine? , 1996, Science.

[145]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[146]  K. Zhang,et al.  Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[147]  H S Seung,et al.  How the brain keeps the eyes still. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[148]  T. Sanger,et al.  Probability density estimation for the interpretation of neural population codes. , 1996, Journal of neurophysiology.

[149]  Eero P. Simoncelli,et al.  A filter design technique for steerable pyramid image transforms , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

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

[151]  William Bialek,et al.  Entropy and Information in Neural Spike Trains , 1996, cond-mat/9603127.

[152]  K. I. Blum,et al.  Functional significance of long-term potentiation for sequence learning and prediction. , 1996, Cerebral cortex.

[153]  N. Swindale.  The development of topography in the visual cortex: a review of models. , 1996, Network.

[154]  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.

[155]  Roland Baddeley,et al.  Optimal, Unsupervised Learning in Invariant Object Recognition , 1997, Neural Computation.

[156]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[157]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[158]  R. Shapley,et al.  New perspectives on the mechanisms for orientation selectivity , 1997, Current Opinion in Neurobiology.

[159]  Eero P. Simoncelli.  Statistical models for images: compression, restoration and synthesis , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[160]  N. Donegan,et al.  A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum. , 1997, Learning & memory.

[161]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[162]  Geoffrey E. Hinton,et al.  Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[163]  B L McNaughton,et al.  Path Integration and Cognitive Mapping in a Continuous Attractor Neural Network Model , 1997, The Journal of Neuroscience.

[164]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[165]  J. Lisman.  Bursts as a unit of neural information: making unreliable synapses reliable , 1997, Trends in Neurosciences.

[166]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.

[167]  T. Sejnowski,et al.  Spatial Transformations in the Parietal Cortex Using Basis Functions , 1997, Journal of Cognitive Neuroscience.

[168]  L. Abbott,et al.  A Quantitative Description of Short-Term Plasticity at Excitatory Synapses in Layer 2/3 of Rat Primary Visual Cortex , 1997, The Journal of Neuroscience.

[169]  B. McNaughton,et al.  Experience-dependent, asymmetric expansion of hippocampal place fields. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[170]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[171]  E. Marder,et al.  A Model Neuron with Activity-Dependent Conductances Regulated by Multiple Calcium Sensors , 1998, The Journal of Neuroscience.

[172]  Peter E. Latham,et al.  Statistically Efficient Estimation Using Population Coding , 1998, Neural Computation.

[173]  D. Perrett,et al.  The `Ideal Homunculus': decoding neural population signals , 1998, Trends in Neurosciences.

[174]  E. Rolls,et al.  Neural networks and brain function , 1998 .

[175]  Klaus Obermayer,et al.  The Role of Lateral Cortical Competition in Ocular Dominance Development , 1998, NIPS.

[176]  Peter Dayan,et al.  Computational Differences between Asymmetrical and Symmetrical Networks , 1998, NIPS.

[177]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[178]  X J Wang,et al.  Calcium coding and adaptive temporal computation in cortical pyramidal neurons. , 1998, Journal of neurophysiology.

[179]  A. Parker,et al.  Sense and the single neuron: probing the physiology of perception. , 1998, Annual review of neuroscience.

[180]  Anthony M. Zador,et al.  Novel Integrate-and-re-like Model of Repetitive Firing in Cortical Neurons , 1998 .

[181]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[182]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[183]  W. Kristan,et al.  A neuronal network for computing population vectors in the leech , 1998, Nature.

[184]  Li I. Zhang,et al.  A critical window for cooperation and competition among developing retinotectal synapses , 1998, Nature.

[185]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[186]  Zhaoping Li,et al.  A Neural Model of Contour Integration in the Primary Visual Cortex , 1998, Neural Computation.

[187]  N. Spruston,et al.  Determinants of Voltage Attenuation in Neocortical Pyramidal Neuron Dendrites , 1998, The Journal of Neuroscience.

[188]  J. C. Anderson,et al.  Estimates of the net excitatory currents evoked by visual stimulation of identified neurons in cat visual cortex. , 1998, Cerebral cortex.

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

[190]  Terrence J. Sejnowski,et al.  Neuronal Tuning: To Sharpen or Broaden? , 1999, Neural Computation.

[191]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[192]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[193]  T. Sejnowski,et al.  The Book of Hebb , 1999, Neuron.

[194]  Z Li,et al.  Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex. , 1999, Network.

[195]  Vivien A. Casagrande,et al.  Biophysics of Computation: Information Processing in Single Neurons , 1999 .

[196]  Christof Koch,et al.  How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate , 1999, Nature Neuroscience.

[197]  M. Sur,et al.  Development and plasticity of the cerebral cortex: from molecules to maps. , 1999, Journal of neurobiology.

[198]  Michael I. Jordan,et al.  Attractor Dynamics in Feedforward Neural Networks , 2000, Neural Computation.

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

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

[201]  Daniel D. Lee,et al.  Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons , 2000, Neuron.

[202]  M. Weliky.  Correlated Neuronal Activity Minireview and Visual Cortical Development , 2000 .

[203]  Emilio Salinas,et al.  Do Simple Cells in Primary Visual Cortex Form a Tight Frame? , 2000, Neural Computation.

[204]  M. Häusser,et al.  Neurobiology , 2001, Current Opinion in Neurobiology.

[205]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[206]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.