Coefficient of variation vs. mean interspike interval curves: What do they tell us about the brain?

Abstract A number of models have been produced recently to explain the high variability of natural spike trains (Softky and Koch, J. Neurosci. 13 (1) (1993) 334). These models use a range of different biological mechanisms including partial somatic reset, concurrent inhibition and excitation, correlated inputs and network dynamics effects. In this paper we examine which model is more likely to reflect the mechanisms used in the brain and we evaluate the ability of each model to reproduce the experimental coefficient of variation (CV) vs. mean interspike interval (ISI) curves (CV=standard deviation/mean ISI). The results show that the partial somatic reset mechanism is the most likely candidate to reflect the mechanism used in the brain for reproducing irregular firing.

[1]  Kenneth D. Miller,et al.  Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell , 1997, Neural Computation.

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

[3]  Guido Bugmann,et al.  Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset , 1997, Neural Computation.

[4]  P. Cariani.  Temporal Coding of Periodicity Pitch in the Auditory System: An Overview , 1999, Neural plasticity.

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

[6]  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.

[7]  W. Singer,et al.  Integrator or coincidence detector? The role of the cortical neuron revisited , 1996, Trends in Neurosciences.

[8]  Jianfeng Feng,et al.  Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model , 2000, Neural Computation.

[9]  D. R. Smith,et al.  A STATISTICAL ANALYSIS OF THE CONTINUAL ACTIVITY OF SINGLE CORTICAL NEURONES IN THE CAT UNANAESTHETIZED ISOLATED FOREBRAIN. , 1965, Biophysical journal.

[10]  A. C. Webb,et al.  The spontaneous activity of neurones in the cat’s cerebral cortex , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[11]  Jianfeng Feng,et al.  Coefficient of variation of interspike intervals greater than 0.5. How and when? , 1999, Biological Cybernetics.

[12]  Marius Usher,et al.  Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials , 1994, Neural Computation.

[13]  C. Stevens,et al.  Input synchrony and the irregular firing of cortical neurons , 1998, Nature Neuroscience.

[14]  W. Shofner,et al.  Responses of cochlear nucleus units in the chinchilla to iterated rippled noises: analysis of neural autocorrelograms. , 1999, Journal of neurophysiology.

[15]  Michael N. Shadlen,et al.  Noise, neural codes and cortical organization , 1994, Current Opinion in Neurobiology.

[16]  G Bugmann,et al.  Near Poisson-type firing produced by concurrent excitation and inhibition. , 2000, Bio Systems.

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

[18]  Jianfeng Feng,et al.  Impact of temporal variation and the balance between excitation and inhibition on the output of the perfect integrate-and-fire model , 1998, Biological Cybernetics.

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

[20]  T J Sejnowski,et al.  Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons. , 1998, Network.

[21]  Yutaka Sakai,et al.  Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons , 1999, Neural Networks.