Estimation of synaptic conductances.
In order to identify and understand mechanistically the cortical circuitry of sensory information processing estimates are needed of synaptic input fields that drive neurons. From intracellular in vivo recordings one would like to estimate net synaptic conductance time courses for excitation and inhibition, g(E)(t) and g(I)(t), during time-varying stimulus presentations. However, the intrinsic conductance transients associated with neuronal spiking can confound such estimates, and thereby jeopardize functional interpretations. Here, using a conductance-based pyramidal neuron model we illustrate errors in estimates when the influence of spike-generating conductances are not reduced or avoided. A typical estimation procedure involves approximating the current-voltage relation at each time point during repeated stimuli. The repeated presentations are done in a few sets, each with a different steady bias current. From the trial-averaged smoothed membrane potential one estimates total membrane conductance and then dissects out estimates for g(E)(t) and g(I)(t). Simulations show that estimates obtained during phases without spikes are good but those obtained from phases with spiking should be viewed with skeptism. For the simulations, we consider two different synaptic input scenarios, each corresponding to computational network models of orientation tuning in visual cortex. One input scenario mimics a push-pull arrangement for g(E)(t) and g(I)(t) and idealized as specified smooth time courses. The other is taken directly from a large-scale network simulation of stochastically spiking neurons in a slab of cortex with recurrent excitation and inhibition. For both, we show that spike-generating conductances cause serious errors in the estimates of g(E) and g(I). In some phases for the push-pull examples even the polarity of g(I) is mis-estimated, indicating significant increase when g(I) is actually decreased. Our primary message is to be cautious about forming interpretations based on estimates developed during spiking phases.
Guillamon
Antoni
A
Department de Matemàtica Aplicada I, Universitat Politècnica de Catalunya, Dr. Marañón n. 44-50, 08028 Barcelona, Catalonia, Spain. antoni.guillamon@upc.edu
McLaughlin
David W
DW
Rinzel
John
J
D000222:Adaptation, Physiological; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D009949:Orientation; D017966:Pyramidal Cells / Q000166:cytology / Q000502:physiology*; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors; D014793:Visual Cortex / Q000166:cytology