Dynamic functional tuning of nonlinear cortical networks.

The mammalian neocortex is a highly complex and nonlinear dynamic system. One of its most prominent features is an omnipresent spontaneous neuronal activity. Here the possible functional role of this global background for cognitive flexibility is studied in a prototypic mean-field model area. It is demonstrated that the level of global background current efficiently controls the stimulus-response threshold and the stability and properties of short-term memory states. Moreover, it can dynamically gate arbitrary cortical subnetworks, when applied to parts of the area as a weak bias signal. These results suggest a central functional role of the level of background activation: the dynamic functional tuning of neocortical circuits.

[1]  Emilio Salinas,et al.  Background Synaptic Activity as a Switch Between Dynamical States in a Network , 2003, Neural Computation.

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

[3]  P S Goldman-Rakic,et al.  Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[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]  M. Mattia,et al.  Population dynamics of interacting spiking neurons. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[7]  Gustavo Deco,et al.  Modular biased‐competition and cooperation: a candidate mechanism for selective working memory , 2004, The European journal of neuroscience.

[8]  Mark C. W. van Rossum,et al.  Fast Propagation of Firing Rates through Layered Networks of Noisy Neurons , 2002, The Journal of Neuroscience.

[9]  C. Gray,et al.  Physiological properties of inhibitory interneurons in cat striate cortex. , 1997, Cerebral cortex.

[10]  H. Tuckwell.  Introduction to Theoretical Neurobiology: Linear Cable Theory and Dendritic Structure , 1988 .

[11]  X. Wang,et al.  Synaptic Basis of Cortical Persistent Activity: the Importance of NMDA Receptors to Working Memory , 1999, The Journal of Neuroscience.

[12]  S. Kosslyn,et al.  Brain areas underlying visual mental imagery and visual perception: an fMRI study. , 2004, Brain research. Cognitive brain research.

[13]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[14]  P. D. Giudice,et al.  Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses , 2003, Journal of Physiology-Paris.

[15]  D. Amit,et al.  Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network , 2003, The European journal of neuroscience.

[16]  Gustavo Deco,et al.  A neuronal model for the shaping of feature selectivity in IT by visual categorization , 2005, Neurocomputing.

[17]  J. Fuster,et al.  Unit activity in monkey parietal cortex related to haptic perception and temporary memory , 2004, Experimental Brain Research.

[18]  S. Kosslyn,et al.  Neural foundations of imagery , 2001, Nature Reviews Neuroscience.

[19]  Daniel J. Amit,et al.  E ective neurons and attractor neural networks in cortical environment , 1992 .

[20]  N. Brunel,et al.  Firing frequency of leaky intergrate-and-fire neurons with synaptic current dynamics. , 1998, Journal of theoretical biology.

[21]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

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

[23]  E. Rolls,et al.  What and Where in Visual Working Memory: A Computational Neurodynamical Perspective for Integrating fMRI and Single-Neuron Data , 2004, Journal of Cognitive Neuroscience.

[24]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[25]  Wulfram Gerstner,et al.  Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking , 2000, Neural Computation.

[26]  Martin Stetter,et al.  Exploration of Cortical Function , 2002, Springer Netherlands.

[27]  G. E. Alexander,et al.  Neuron Activity Related to Short-Term Memory , 1971, Science.

[28]  Walter Senn,et al.  Minimal Models of Adapted Neuronal Response to In VivoLike Input Currents , 2004, Neural Computation.

[29]  Nicolas Brunel,et al.  Dynamics and plasticity of stimulus-selective persistent activity in cortical network models. , 2003, Cerebral cortex.

[30]  E. Rolls,et al.  Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex , 2003, The European journal of neuroscience.

[31]  G. Deco,et al.  Cooperation and biased competition model can explain attentional filtering in the prefrontal cortex , 2004, The European journal of neuroscience.