Graded persisting activity of heterogeneous neuron ensembles subject to white noises 1

Effects of distractions such as noises and parameter heterogeneity have been studied on the firing activity of ensemble neurons, each of which is described by the extended Morris-Lecar model showing the graded persisting firings with the aid of an included Ca 2+ -dependent cation current. Although the sustained activity of single neurons is rather robust in a sense that the activity is realized even in the presence of the distractions, the graded frequency of sustained firings is vulnerable to them. It has been shown, however, that the graded persisting activity of ensemble neurons becomes much robust to the distractions by the pooling (ensemble) effect. When the coupling is introduced, the synchronization of firings in ensemble neurons is enhanced, which is beneficial to firings of target neurons.

[1]  R. Romo,et al.  A recurrent network model of somatosensory parametric working memory in the prefrontal cortex. , 2003, Cerebral cortex.

[2]  M J Rosen,et al.  A theoretical neural integrator. , 1972, IEEE transactions on bio-medical engineering.

[3]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[4]  H. Sompolinsky,et al.  Temporal integration by calcium dynamics in a model neuron , 2003, Nature Neuroscience.

[5]  Bijan Pesaran,et al.  Temporal structure in neuronal activity during working memory in macaque parietal cortex , 2000, Nature Neuroscience.

[6]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[7]  R. Baker,et al.  Eye position and eye velocity integrators reside in separate brainstem nuclei. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[8]  H. Sebastian Seung,et al.  The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback , 2004, Journal of Computational Neuroscience.

[9]  R. Romo,et al.  Neuronal correlates of decision-making in secondary somatosensory cortex , 2002, Nature Neuroscience.

[10]  J. Fellous,et al.  A role for NMDA-receptor channels in working memory , 1998, Nature Neuroscience.

[11]  Stephen C. Cannon,et al.  A proposed neural network for the integrator of the oculomotor system , 1983, Biological Cybernetics.

[12]  Hideo Hasegawa,et al.  Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  R. Desimone,et al.  Neural Mechanisms of Visual Working Memory in Prefrontal Cortex of the Macaque , 1996, The Journal of Neuroscience.

[14]  Ranulfo Romo,et al.  Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations , 2003, Current Opinion in Neurobiology.

[15]  Xiao-Jing Wang,et al.  Robust Spatial Working Memory through Homeostatic Synaptic Scaling in Heterogeneous Cortical Networks , 2003, Neuron.

[16]  E Guigon,et al.  Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model. , 1995, Cerebral cortex.

[17]  M. Hasselmo,et al.  Graded persistent activity in entorhinal cortex neurons , 2002, Nature.

[18]  Xiao-Jing Wang,et al.  A Model of Visuospatial Working Memory in Prefrontal Cortex: Recurrent Network and Cellular Bistability , 1998, Journal of Computational Neuroscience.

[19]  H. Eichenbaum,et al.  Memory Representation within the Parahippocampal Region , 1997, The Journal of Neuroscience.

[20]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[21]  L. Frank,et al.  Persistent activity and memory in the entorhinal cortex , 2003, Trends in Neurosciences.

[22]  A. Koulakov,et al.  Model for a robust neural integrator , 2002, Nature Neuroscience.

[23]  H. Seung,et al.  Anatomy and discharge properties of pre-motor neurons in the goldfish medulla that have eye-position signals during fixations. , 2000, Journal of neurophysiology.

[24]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[25]  Oren Shriki,et al.  Rate Models for Conductance-Based Cortical Neuronal Networks , 2003, Neural Computation.

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

[27]  R. Desimone,et al.  Object and place memory in the macaque entorhinal cortex. , 1997, Journal of neurophysiology.

[28]  Hideo Hasegawa.  Dynamical mean-field theory of noisy spiking neuron ensembles: application to the Hodgkin-Huxley model. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  H. Seung,et al.  Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. , 2003, Cerebral cortex.

[30]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[31]  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.

[32]  E. Niebur,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2022 .