Impact of intrinsic biophysical diversity on the activity of spiking neurons

We study the effect of intrinsic heterogeneity on the activity of a population of leaky integrate-and-fire neurons. By rescaling the dynamical equation, we derive mathematical relations between multiple neuronal parameters and a fluctuating input noise. To this end, common input to heterogeneous neurons is conceived as an identical noise with neuron-specific mean and variance. As a consequence, the neuronal output rates can differ considerably, and their relative spike timing becomes desynchronized. This theory can quantitatively explain some recent experimental findings.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  A. Siegert.  On the First Passage Time Probability Problem , 1951 .

[3]  Sompolinsky,et al.  Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions. , 1993, Physical review letters.

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

[5]  G. Buzsáki,et al.  Gamma Oscillation by Synaptic Inhibition in a Hippocampal Interneuronal Network Model , 1996, The Journal of Neuroscience.

[6]  著者なし.  16 , 1871, Animals at the End of the World.

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

[8]  W. Bair,et al.  Correlated Firing in Macaque Visual Area MT: Time Scales and Relationship to Behavior , 2001, The Journal of Neuroscience.

[9]  A. Aertsen,et al.  Neuronal Integration of Synaptic Input in the Fluctuation-Driven Regime , 2004, The Journal of Neuroscience.

[10]  L. Ricciardi,et al.  The Ornstein-Uhlenbeck process as a model for neuronal activity , 1979, Biological Cybernetics.

[11]  E. Marder,et al.  Variability, compensation and homeostasis in neuron and network function , 2006, Nature Reviews Neuroscience.

[12]  Jaime de la Rocha,et al.  Supplementary Information for the article ‘ Correlation between neural spike trains increases with firing rate ’ , 2007 .

[13]  Valentin Dragoi,et al.  Efficient coding in heterogeneous neuronal populations , 2008, Proceedings of the National Academy of Sciences.

[14]  Stefan Rotter,et al.  Dependence of Neuronal Correlations on Filter Characteristics and Marginal Spike Train Statistics , 2008, Neural Computation.

[15]  Tomoki Fukai,et al.  Temporal precision of spike response to fluctuating input in pulse-coupled networks of oscillating neurons. , 2008, Physical review letters.

[16]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[17]  Nicolas Brunel,et al.  How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains , 2009, The Journal of Neuroscience.

[18]  Hermann Riecke,et al.  Mechanisms of pattern decorrelation by recurrent neuronal circuits , 2010, Nature Neuroscience.

[19]  N. Urban,et al.  Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content , 2010, Nature Neuroscience.

[20]  P. Dayan,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits , 2022 .

[21]  Fred Wolf,et al.  Correlations and synchrony in threshold neuron models. , 2008, Physical review letters.

[22]  J F Mejias,et al.  Optimal heterogeneity for coding in spiking neural networks. , 2012, Physical review letters.

[23]  Romain Brette,et al.  Computing with Neural Synchrony , 2012, PLoS Comput. Biol..