Membrane potential and spike train statistics depend distinctly on input statistics.

A description of how the activity of a population of neurons reflects the structure of its inputs is essential for understanding neural coding. Many studies have examined how inputs determine spiking statistics, while comparatively little is known about membrane potentials. We examine how membrane potential statistics are related to input and spiking statistics. Surprisingly, firing rates and membrane potentials are sensitive to input current modulations in distinct regimes. Additionally, the correlation between the membrane potentials of two uncoupled cells and the correlation between their spike trains reflect input correlations in distinct regimes. Our predictions are experimentally testable, provide insight into the filtering properties of neurons, and indicate that care needs to be taken when interpreting neuronal recordings that reflect a combination of subthreshold and spiking activity.

[1]  Robert Rosenbaum,et al.  Mechanisms That Modulate the Transfer of Spiking Correlations , 2011, Neural Computation.

[2]  Moritz Helias,et al.  Finite Post Synaptic Potentials Cause a Fast Neuronal Response , 2010, Front. Neurosci..

[3]  Fred Wolf,et al.  Spike onset dynamics and response speed in neuronal populations. , 2011, Physical review letters.

[4]  Robert Rosenbaum,et al.  The Effects of Pooling on Spike Train Correlations , 2011, Front. Neurosci..

[5]  Rupert Swarbrick,et al.  Firing-rate response of a neuron receiving excitatory and inhibitory synaptic shot noise. , 2010, Physical review letters.

[6]  Robert Rosenbaum,et al.  Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .

[7]  Benjamin Lindner,et al.  Comparative study of different integrate-and-fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[9]  Magnus J. E. Richardson,et al.  Spike-train spectra and network response functions for non-linear integrate-and-fire neurons , 2008, Biological Cybernetics.

[10]  Amir Globerson,et al.  Correlations between Groups of Premotor Neurons Carry Information about Prehension , 2008, The Journal of Neuroscience.

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

[12]  Michael Okun,et al.  Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities , 2008, Nature Neuroscience.

[13]  Eric Shea-Brown,et al.  Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding. , 2008, Physical review letters.

[14]  M. J. Richardson,et al.  Firing-rate response of linear and nonlinear integrate-and-fire neurons to modulated current-based and conductance-based synaptic drive. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[16]  E. Seidemann,et al.  Optimal decoding of correlated neural population responses in the primate visual cortex , 2006, Nature Neuroscience.

[17]  Anthony N. Burkitt,et al.  A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.

[18]  S. Schiff,et al.  Interneuron and pyramidal cell interplay during in vitro seizure-like events. , 2006, Journal of neurophysiology.

[19]  Brent Doiron,et al.  Theory of oscillatory firing induced by spatially correlated noise and delayed inhibitory feedback. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Development, dynamics and pathology of neuronal networks: from molecules to functional circuits. Proceedings of the 23rd International Summer School of Brain Research. August 25-29, 2003. Amsterdam, The Netherlands. , 2005, Progress in brain research.

[21]  D. Hansel,et al.  How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs , 2003, The Journal of Neuroscience.

[22]  W. Senn,et al.  Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. , 2003, Journal of neurophysiology.

[23]  Jessy D. Dorn,et al.  Estimating membrane voltage correlations from extracellular spike trains. , 2003, Journal of neurophysiology.

[24]  Frances S. Chance,et al.  Effects of synaptic noise and filtering on the frequency response of spiking neurons. , 2001, Physical review letters.

[25]  D. Ferster,et al.  Synchronous Membrane Potential Fluctuations in Neurons of the Cat Visual Cortex , 1999, Neuron.

[26]  Charles J. Wilson,et al.  Membrane potential synchrony of simultaneously recorded striatal spiny neurons in vivo , 1998, Nature.

[27]  Purvis Bedenbaugh,et al.  Multiunit Normalized Cross Correlation Differs from the Average Single-Unit Normalized Correlation , 1997, Neural Computation.

[28]  ASTIN Bulletin , 1997, ASTIN Bulletin.

[29]  A. Mccarthy Development , 1996, Current Opinion in Neurobiology.

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

[31]  J. Elgin The Fokker-Planck Equation: Methods of Solution and Applications , 1984 .

[32]  H. Risken Fokker-Planck Equation , 1984 .

[33]  J. Hammersley,et al.  Diffusion Processes and Related Topics in Biology , 1977 .

[34]  H. Kalmus Biological Cybernetics , 1972, Nature.

[35]  Physical Review , 1965, Nature.

[36]  A. Yaglom,et al.  An Introduction to the Theory of Stationary Random Functions , 1963 .

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