A method for decoding the neurophysiological spike-response transform.
Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.
Stern
Estee
E
Fishberg Department of Neuroscience, Mount Sinai School of Medicine, Box 1065, One Gustave L. Levy Place, New York, NY 10029, United States. estee.stern@mssm.edu
García-Crescioni
Keyla
K
Miller
Mark W
MW
Peskin
Charles S
CS
Brezina
Vladimir
V
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000502:physiology*; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008432:Mathematical Computing; D009469:Neuromuscular Junction / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology
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