Detection of weak synaptic interactions between single Ia afferent and motor‐unit spike trains in the decerebrate cat.

1. Spike trains from identified single Ia afferents from soleus and lateral gastrocnemius muscles were recorded (while ‘in continuity’ with the spinal cord) simultaneously with single‐motor‐unit EMG spike trains from the same muscles in decerebrate cats. 2. A total of 143 Ia afferent‐motor‐unit pairs were examined for the presence of correlated activity between the Ia afferent and motor‐unit and between the motor‐unit and Ia afferent. Four types of correlation were identified on the basis of the cross‐intensity function estimated for individual Ia afferent‐motor‐unit pairs. These correlations were attributed to the absence or presence of a central Ia afferent‐motoneurone interaction or a peripheral motor‐unit‐muscle spindle interaction. 3. In addition to the cross‐correlation‐based second‐order cross‐intensity function, third‐order cumulants were defined and used further to investigate Ia afferent‐motor‐unit interactions. A third‐order cumulant density‐based approach to signal processing offers improved signal‐to‐noise ratios, compared with the traditional product density approach, for parameters characterizing certain kinds of linear processes as well as a description of non‐linear interactions. Two classes of third‐order relations were described. One class was associated with a strong central connection and the other with a weak central connection. 4. Third‐order cumulants estimated for Ia afferent‐motor‐unit pairs with significant second‐order central correlations were able to detect a period of decreased motoneuronal excitability. In addition, temporal summation prior to spike initiation could be identified in cases where the afferent discharge was suitably high. 5. Third‐order cumulants estimated for Ia afferent‐motor‐unit pairs in which no significant second‐order central correlation existed identified the presence of weak synaptic interactions. It is argued that these interactions result from the summation from the recorded Ia afferent discharge and other spontaneous synaptic inputs to the motoneurone. 6. The results of the second‐order cross‐intensity analysis of Ia afferent‐motor‐unit interactions, combined with those from the third‐order cumulant density analysis, showed that 77% of the recorded afferents had a detectable influence on motor‐unit behaviour. 7. The results of this study suggest that the third‐order cumulant, based on the analysis of spike trains, will provide a useful tool for detecting synaptic interactions not found by the use of the second‐order cross‐correlation histogram alone, and may also be used to estimate the time course of post‐spike depression in motoneurones, as well as other non‐linear regions of motoneurone membrane trajectory.

[1]  Paul D. Mac Lean.  Nerve Impulse , 1954, The Yale Journal of Biology and Medicine.

[2]  J. Eccles,et al.  Excitatory synaptic action in motoneurones , 1955, The Journal of physiology.

[3]  D. Brillinger.  An Introduction to Polyspectra , 1965 .

[4]  Samuel Karlin,et al.  A First Course on Stochastic Processes , 1968 .

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

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

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

[8]  E Henneman,et al.  Terminals of single Ia fibers: location, density, and distribution within a pool of 300 homonymous motoneurons. , 1971, Journal of neurophysiology.

[9]  D. Brillinger.  The spectral analysis of stationary interval functions , 1972 .

[10]  S. Kidambi Srinivasan,et al.  Stochastic point processes and their applications , 1974 .

[11]  D. Perkel,et al.  Nerve-impulse patterns: A quantitative display technique for three neurons , 1975, Brain Research.

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

[13]  T. Sears,et al.  Short‐term synchronization of intercostal motoneurone activity. , 1976, The Journal of physiology.

[14]  L. Mendell,et al.  Individual EPSPs produced by single triceps surae Ia afferent fibers in homonymous and heteronymous motoneurons. , 1976, Journal of neurophysiology.

[15]  E K Stauffer,et al.  Analysis of muscle receptor connections by spike-triggered averaging. 1. Spindle primary and tendon organ afferents. , 1976, Journal of neurophysiology.

[16]  T. Sears,et al.  The synaptic connexions to intercostal motoneurones as revealed by the average common excitation potential. , 1978, The Journal of physiology.

[17]  D. Brillinger.  Comparative Aspects of the Study of Ordinary Time Series and of Point Processes , 1978 .

[18]  P. Kirkwood.  On the use and interpretation of cross-correlation measurements in the mammalian central nervous system , 1979, Journal of Neuroscience Methods.

[19]  Interactions among an ensemble of chordotonal organ receptors and motor neurons of the crayfish claw. , 1979, Journal of neurophysiology.

[20]  R. Reinking,et al.  Motor unit-muscle spindle interactions in active muscles of decerebrate cats , 1980, Neuroscience Letters.

[21]  David J. Murray-Smith,et al.  An introduction to the application of system identification techniques to elements of the neuromuscular system , 1982 .

[22]  M. Abeles.  Quantification, smoothing, and confidence limits for single-units' histograms , 1982, Journal of Neuroscience Methods.

[23]  M. Rosenblatt.  Cumulants and cumulant spectra , 1983 .

[24]  G L Gerstein,et al.  Favored patterns in spike trains. I. Detection. , 1983, Journal of neurophysiology.

[25]  G L Gerstein,et al.  Favored patterns in spike trains. II. Application. , 1983, Journal of neurophysiology.

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

[27]  E. Pierrot-Deseilligny,et al.  Evidence for interneuronally mediated Ia excitatory effects to human quadriceps motoneurones. , 1986, The Journal of physiology.

[28]  M.R. Raghuveer,et al.  Bispectrum estimation: A digital signal processing framework , 1987, Proceedings of the IEEE.

[29]  E E Fetz,et al.  Cross‐correlation assessment of synaptic strength of single Ia fibre connections with triceps surae motoneurones in cats. , 1987, The Journal of physiology.

[30]  A M Amjad,et al.  A framework for the analysis of neuronal networks. , 1989, Progress in brain research.

[31]  Abdul Majeed Amjad.  Identification of point process systems with application to complex neuronal networks , 1989 .

[32]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[33]  U. Windhorst,et al.  Activation of renshaw cells , 1990, Progress in Neurobiology.

[34]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[35]  P. Andersen,et al.  Synaptically triggered action potentials begin as a depolarizing ramp in rat hippocampal neurones in vitro. , 1992, The Journal of physiology.