Resonance or integration? Self-sustained dynamics and excitability of neural microcircuits.

We investigated spontaneous activity and excitability in large networks of artificial spiking neurons. We compared three different spiking neuron models: integrate-and-fire (IF), regular-spiking (RS), and resonator (RES). First, we show that different models have different frequency-dependent response properties, yielding large differences in excitability. Then, we investigate the responsiveness of these models to a single afferent inhibitory/excitatory spike and calibrate the total synaptic drive such that they would exhibit similar peaks of the postsynaptic potentials (PSP). Based on the synaptic calibration, we build large microcircuits of IF, RS, and RES neurons and show that the resonance property favors homeostasis and self-sustainability of the network activity. On the other hand, integration produces instability while it endows the network with other useful properties, such as responsiveness to external inputs. We also investigate other potential sources of stable self-sustained activity and their relation to the membrane properties of neurons. We conclude that resonance and integration at the neuron level might interact in the brain to promote stability as well as flexibility and responsiveness to external input and that membrane properties, in general, are essential for determining the behavior of large networks of neurons.

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

[2]  E. Evarts.  TEMPORAL PATTERNS OF DISCHARGE OF PYRAMIDAL TRACT NEURONS DURING SLEEP AND WAKING IN THE MONKEY. , 1964, Journal of neurophysiology.

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

[4]  D. Hansel,et al.  Existence and stability of persistent states in large neuronal networks. , 2001, Physical review letters.

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

[6]  R. Miura,et al.  Quantification of membrane properties of trigeminal root ganglion neurons in guinea pigs. , 1986, Journal of neurophysiology.

[7]  Marla B. Feller,et al.  Spontaneous patterned retinal activity and the refinement of retinal projections , 2005, Progress in Neurobiology.

[8]  A. Destexhe,et al.  The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.

[9]  Rajesh P. N. Rao,et al.  Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. , 2001, Journal of neurophysiology.

[10]  U. Heinemann,et al.  Dynamics of rat entorhinal cortex layer II and III cells: characteristics of membrane potential resonance at rest predict oscillation properties near threshold , 2004, The Journal of physiology.

[11]  Andreas V. M. Herz,et al.  A Universal Model for Spike-Frequency Adaptation , 2003, Neural Computation.

[12]  B. Richmond,et al.  Intrinsic dynamics in neuronal networks. I. Theory. , 2000, Journal of neurophysiology.

[13]  Fabrizio Gabbiani,et al.  Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron. , 2006, Journal of neurophysiology.

[14]  A. Arieli.  Ongoing activity and the state of mind: the role of spontaneously emerging cortical states in visual perception and motor action , 2004 .

[15]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[16]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[17]  Alain Destexhe,et al.  Event-based simulation strategy for conductance-based synaptic interactions and plasticity , 2006, Neurocomputing.

[18]  A. Grinvald,et al.  Linking spontaneous activity of single cortical neurons and the underlying functional architecture. , 1999, Science.

[19]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

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

[21]  C. Garc.  Resonance and selective communication via bursts in neurons having subthreshold oscillations , 2009 .

[22]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[23]  N. Brunel,et al.  From subthreshold to firing-rate resonance. , 2003, Journal of neurophysiology.

[24]  Henry C. Tuckwell,et al.  Stochastic processes in the neurosciences , 1989 .

[25]  Gordon Pipa,et al.  Coherence, Memory and Conditioning , 2005 .

[26]  Robert A. Frazor,et al.  Visual cortex neurons of monkeys and cats: temporal dynamics of the spatial frequency response function. , 2004, Journal of neurophysiology.

[27]  D. Snodderly,et al.  High response reliability of neurons in primary visual cortex (V1) of alert, trained monkeys. , 2006, Cerebral cortex.

[28]  Peter Wenner,et al.  Spontaneous Network Activity in the Embryonic Spinal Cord Regulates AMPAergic and GABAergic Synaptic Strength , 2006, Neuron.

[29]  D. W. Wheeler,et al.  Coherence, Memory and Conditioning : A Modern Viewpoint , 2005 .

[30]  Wulfram Gerstner,et al.  Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates , 2000, Neural Computation.

[31]  G. Buzsáki.  Rhythms of the brain , 2006 .

[32]  Y. Miyashita,et al.  Neuronal correlate of pictorial short-term memory in the primate temporal cortexYasushi Miyashita , 1988, Nature.

[33]  I. Lampl,et al.  Subthreshold oscillations and resonant behavior: two manifestations of the same mechanism , 1997, Neuroscience.

[34]  T. Sejnowski,et al.  Origin of slow cortical oscillations in deafferented cortical slabs. , 2000, Cerebral cortex.

[35]  D. Ulrich,et al.  Dendritic resonance in rat neocortical pyramidal cells. , 2002, Journal of neurophysiology.

[36]  D. Paré,et al.  Differential impact of miniature synaptic potentials on the soma and dendrites of pyramidal neurons in vivo. , 1997, Journal of neurophysiology.

[37]  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.

[38]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[39]  D. Durstewitz,et al.  Beyond bistability: Biophysics and temporal dynamics of working memory , 2006, Neuroscience.

[40]  Gina Turrigiano,et al.  Maintaining Your Youthful Spontaneity: Microcircuit Homeostasis in the Embryonic Spinal Cord , 2006, Neuron.

[41]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[42]  Nicolas Brunel,et al.  Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates , 1999, Neural Computation.

[43]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[44]  Xiao-Jing Wang,et al.  Persistent neural activity: experiments and theory. , 2003, Cerebral cortex.

[45]  M Steriade,et al.  Intracellular analysis of relations between the slow (< 1 Hz) neocortical oscillation and other sleep rhythms of the electroencephalogram , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[46]  Irina Erchova,et al.  Subthreshold resonance explains the frequency-dependent integration of periodic as well as random stimuli in the entorhinal cortex. , 2004, Journal of neurophysiology.

[47]  Raul C. Mureşan,et al.  The "Neocortex" Neural Simulator. A Modern Design. , 2004 .

[48]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[49]  G. Davis.  Homeostatic control of neural activity: from phenomenology to molecular design. , 2006, Annual review of neuroscience.

[50]  S. Solla,et al.  Self-sustained activity in a small-world network of excitable neurons. , 2003, Physical review letters.

[51]  Maxim Bazhenov,et al.  Role of network dynamics in shaping spike timing reliability. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[52]  D. Linden,et al.  The other side of the engram: experience-driven changes in neuronal intrinsic excitability , 2003, Nature Reviews Neuroscience.

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

[54]  C. Sherrington.  Integrative Action of the Nervous System , 1907 .

[55]  L. Abbott,et al.  Neural network dynamics. , 2005, Annual review of neuroscience.

[56]  A Grinvald,et al.  Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. , 1995, Journal of neurophysiology.

[57]  R. Miura,et al.  Subthreshold membrane resonance in neocortical neurons. , 1996, Journal of neurophysiology.

[58]  Y. Yarom,et al.  Resonant behavior and frequency preferences of thalamic neurons. , 1994, Journal of neurophysiology.

[59]  L. S. Leung,et al.  Theta-frequency resonance in hippocampal CA1 neurons in vitro demonstrated by sinusoidal current injection. , 1998, Journal of neurophysiology.

[60]  A. Destexhe,et al.  Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons In vivo. , 1998, Journal of neurophysiology.

[61]  W. Smith.  The Integrative Action of the Nervous System , 1907, Nature.

[62]  Fiona E. N. LeBeau,et al.  Microcircuits in action – from CPGs to neocortex , 2005, Trends in Neurosciences.

[63]  Nicolas Brunel,et al.  Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges. , 2005, Journal of neurophysiology.

[64]  Y. Yarom,et al.  Resonance, oscillation and the intrinsic frequency preferences of neurons , 2000, Trends in Neurosciences.

[65]  M. Steriade.  Grouping of brain rhythms in corticothalamic systems , 2006, Neuroscience.

[66]  D. Feldman,et al.  Modulation of spike timing by sensory deprivation during induction of cortical map plasticity , 2004, Nature Neuroscience.

[67]  R. Douglas,et al.  Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.

[68]  E. Dupont,et al.  A 14-day period of hindpaw sensory deprivation enhances the responsiveness of rat cortical neurons , 2003, Neuroscience.

[69]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[70]  F. G. Pike,et al.  Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents , 2000, The Journal of physiology.