Detection and estimation of neural connectivity based on crosscorrelation analysis

Crosscorrelation analysis of simultaneously recorded activity of pairs of neurons is a common tool to infer functional neural connectivity. The adequacy of crosscorrelation procedures to detect and estimate neural connectivity has been investigated by means of computer simulations of small networks composed of fairly realistic modelneurons. If the mean interval of neural firings is much larger than the duration of postsynaptic potentials, which will be the case in many central brain areas excitatory connections are easier to detect than inhibitory ones. On the other hand, inhibitory connections are revealed better if the mean firing interval is much smaller than post-synaptic potential duration. In general the effects of external stimuli and the effects of neural connectivity do not add linearly. Furthermore, neurons may exhibit a certain degree of timelock to the stimulus. For these reasons the commonly applied “shift predictor” procedure to separate stimulus and neural effects appears to be of limited value. In case of parallel direct and indirect neural pathways between two neurons crosscorrelation analysis does not estimate the direct connection but instead an effective connectivity, which reflects the combined influences of the parallel pathways.

[1]  G L Gerstein,et al.  Interactions between neurons in auditory cortex of the cat. , 1974, Journal of neurophysiology.

[2]  D. Brillinger.  The Identification of Point Process Systems , 1975 .

[3]  A. Aertsen,et al.  A comparison of the Spectro-Temporal sensitivity of auditory neurons to tonal and natural stimuli , 1981, Biological Cybernetics.

[4]  C. Knox,et al.  Cross-correlation functions for a neuronal model. , 1974, Biophysical journal.

[5]  K. Tanaka,et al.  Cross-Correlation Analysis of Interneuronal Connectivity in cat visual cortex. , 1981, Journal of neurophysiology.

[6]  A. Aertsen,et al.  Neural representation of the acoustic biotope: On the existence of stimulus-event relations for sensory neurons , 1979, Biological Cybernetics.

[7]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[8]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

[9]  A. Aertsen,et al.  Representation of cooperative firing activity among simultaneously recorded neurons. , 1985, Journal of neurophysiology.

[10]  Ron D. Frostig,et al.  The effects of stimuli on the activity and functional connectivity of local neuronal groups in the cat auditory cortex , 1983, Brain Research.

[11]  George L. Gerstein,et al.  Design of a laboratory for multineuron studies , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  G. P. Moore,et al.  Statistical signs of synaptic interaction in neurons. , 1970, Biophysical journal.

[13]  G L Gerstein,et al.  Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. , 1972, Biophysical journal.

[14]  M. Goldstein,et al.  Intracellular study of the cat's primary auditory cortex. , 1972, Brain research.

[15]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[16]  G. L. Gerstein,et al.  Interactions between cat striate cortex neurons , 2004, Experimental Brain Research.

[17]  P. I. Kuznetsov,et al.  A NOTE ON THE MATHEMATICAL THEORY OF CORRELATED RANDOM POINTS , 1965 .

[18]  D. James Surmeier,et al.  The relationship between cross-correlation measures and underlying synaptic events , 1985, Brain Research.

[19]  Jos J. Eggermont,et al.  Sensitivity of neurons in the auditory midbrain of the grassfrog to temporal characteristics of sound. I. Stimulation with acoustic clicks , 1986, Hearing Research.

[20]  Donald L. Snyder,et al.  Random point processes , 1975 .

[21]  H. I. Krausz,et al.  Identification of nonlinear systems using random impulse train inputs , 1975, Biological Cybernetics.

[22]  J. J. Eggermont,et al.  Stimulus dependent neural correlations in the auditory midbrain of the grassfrog (Rana temporaria L.) , 1983, Biological Cybernetics.

[23]  A. Aertsen,et al.  Evaluation of neuronal connectivity: Sensitivity of cross-correlation , 1985, Brain Research.

[24]  J J Eggermont,et al.  Coherent neural activity in the auditory midbrain of the grassfrog. , 1987, Journal of neurophysiology.

[25]  O. Creutzfeldt,et al.  An intracellular analysis of visual cortical neurones to moving stimuli: Responses in a co-operative neuronal network , 2004, Experimental Brain Research.

[26]  H. Boogaard,et al.  System identification based on point processes and correlation densities. I: The nonrefractory neuron model , 1986 .

[27]  H. F. Voigt,et al.  Stimulus dependent neural correlation: an example from the cochlear nucleus , 2004, Experimental Brain Research.

[28]  D. Perkel,et al.  Cooperative firing activity in simultaneously recorded populations of neurons: detection and measurement , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[29]  Moshe Abeles,et al.  Detection of single unit responses which are loosely time-locked to a stimulus , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  H. Boogaard,et al.  Stochastic formulation of neural interaction , 1985 .

[31]  Albert S. Feng,et al.  Frequency selectivity in the anuran auditory midbrain: Single unit responses to single and multiple tone stimulation , 1982, Journal of comparative physiology.