Computational Neuroscience: Cortical Dynamics

A major challenge to understanding cortical function is the complexity found both at the single cell and microcircuit levels. This review covers theoretical studies aimed at elucidating dynamic signal processing within hippocampal pyramidal cells. This processing involves both the intrinsic pyramidal cell properties as well as the microcircuit of inhibitory interneurons that synapse onto the cell. These factors are considered within the framework of associative memory function in areas CA1 and CA3 of the mammalian hippocampus.

[1]  David McLaughlin,et al.  States of High Conductance in a Large-Scale Model of the Visual Cortex , 2002, Journal of Computational Neuroscience.

[2]  Ichiro Tsuda,et al.  Cantor coding in the hippocampus , 2000 .

[3]  Nicolas Brunel,et al.  Dynamics of a recurrent network of spiking neurons before and following learning , 1997 .

[4]  D. Amit,et al.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. , 1997, Cerebral cortex.

[5]  Barry J. Richmond,et al.  Anomalous response variability in a balanced cortical network model , 2002, Neurocomputing.

[6]  P. Heggelund,et al.  Response variability and orientation discrimination of single cells in striate cortex of cat , 1978, Experimental Brain Research.

[7]  Opper,et al.  New method for studying the dynamics of disordered spin systems without finite-size effects. , 1992, Physical review letters.

[8]  J. Szentágothai The modular architectonic principle of neural centers. , 1983, Reviews of physiology, biochemistry and pharmacology.

[9]  H. Sompolinsky,et al.  Relaxational dynamics of the Edwards-Anderson model and the mean-field theory of spin-glasses , 1982 .

[10]  J. A. Movshon,et al.  The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast , 1981, Experimental Brain Research.

[11]  Y. Kuramoto,et al.  Dephasing and bursting in coupled neural oscillators. , 1995, Physical review letters.

[12]  A. Declet,et al.  [Psychology of communication]. , 1969, Puerto Rico y su enfermera.

[13]  T. Albright,et al.  Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex , 1998, Neuron.

[14]  H. Sompolinsky,et al.  Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[15]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[16]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[17]  A. Dean The variability of discharge of simple cells in the cat striate cortex , 2004, Experimental Brain Research.

[18]  J. Movshon,et al.  The statistical reliability of signals in single neurons in cat and monkey visual cortex , 1983, Vision Research.

[19]  L.K.J. Vandamme,et al.  An explanation of 1/f noise in LDD MOSFETs from the ohmic region to saturation , 1993 .

[20]  Nicholas L. Port,et al.  Erratum: Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex (Journal of Neuroscience (February, 1998) (1161-1170)) , 1998 .

[21]  R. Reid,et al.  Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons , 2000, Neuron.

[22]  J. Nicolis,et al.  Chaos and information processing , 1991 .

[23]  D. Snodderly,et al.  Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys , 1997, The Journal of Neuroscience.

[24]  P. Davis,et al.  Chaotic wandering and search in a cycle-memory neural network , 1992 .

[25]  G. Orban,et al.  The response variability of striate cortical neurons in the behaving monkey , 2004, Experimental Brain Research.

[26]  I. Tsuda,et al.  Chaotic dynamics of information processing: the "magic number seven plus-minus two" revisited. , 1985, Bulletin of mathematical biology.

[27]  P. Holmes,et al.  Structurally stable heteroclinic cycles , 1988, Mathematical Proceedings of the Cambridge Philosophical Society.

[28]  J. Hindmarsh,et al.  The assembly of ionic currents in a thalamic neuron I. The three-dimensional model , 1989, Proceedings of the Royal Society of London. B. Biological Sciences.

[29]  S. Treue,et al.  The response of neurons in areas V1 and MT of the alert rhesus monkey to moving random dot patterns , 2005, Experimental Brain Research.

[30]  H. Nozawa,et al.  Solution of the optimization problem using the neural network model as a globally coupled map , 1994 .

[31]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

[32]  Ichiro Tsuda,et al.  Neocortical gap junction-coupled interneuron systems may induce chaotic behavior itinerant among quasi-attractors exhibiting transient synchrony , 2004, Neurocomputing.

[33]  J. Szentágothai The ‘module-concept’ in cerebral cortex architecture , 1975, Brain Research.

[34]  Ichiro Tsuda,et al.  Mathematical description of brain dynamics in perception and action , 1999 .

[35]  John S. Nicolis SHOULD A RELIABLE INFORMATION PROCESSOR BE CHAOTIC , 1982 .

[36]  Nicolas Brunel,et al.  Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.

[37]  Celso Grebogi,et al.  Dynamical properties of a simple mechanical system with a large number of coexisting periodic attractors , 1998 .

[38]  H. Sompolinsky,et al.  13 Modeling Feature Selectivity in Local Cortical Circuits , 2022 .

[39]  Jorge Buescu,et al.  Exotic Attractors: From Liapunov Stability to Riddled Basins , 1997 .

[40]  Ichiro Tsuda,et al.  Memory Dynamics in Asynchronous Neural Networks , 1987 .

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

[42]  M. DeWeese,et al.  Binary Spiking in Auditory Cortex , 2003, The Journal of Neuroscience.

[43]  Alexander Lerchner,et al.  High-conductance states in a mean-field cortical network model , 2004, Neurocomputing.

[44]  Manabu Nakano,et al.  Spatio-temporal Chaos in Gap Junction-Coupled Class I Neurons Exhibiting Saddle-Node Bifurcations , 2003 .

[45]  Eugene M. Izhikevich,et al.  Neural excitability, Spiking and bursting , 2000, Int. J. Bifurc. Chaos.

[46]  J. R. Kantor,et al.  A Functional Interpretation of Human Instincts , 1920 .