TY - JOUR
T1 - Estimation of synaptic conductances
AU - Guillamon, Antoni
AU - McLaughlin, David W.
AU - Rinzel, John
N1 - Funding Information:
The authors want to thank Louis Tao for providing data for some simulations in this paper and also to Robert Shapley for stimulating discussions. An important part of the work was done while A.G. was visiting the New York University under the MECD grant number PR2000-0292 0046670968; he has been partially supported by the DGES grant number MTM2005-06098-C02-1 and CONACIT grant number 2005SGR-986. J.R. has been partially supported by NIH grant MH62595-01. D.M. was supported by National Science Foundation (NSF) Grant DMS-0506396 and by NSF Grant DMS-0211655.
PY - 2006/7
Y1 - 2006/7
N2 - 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, gE(t) and gI(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 gE(t) and gI(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 gE(t) and gI(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 gE and gI. In some phases for the push-pull examples even the polarity of gI is mis-estimated, indicating significant increase when gI is actually decreased. Our primary message is to be cautious about forming interpretations based on estimates developed during spiking phases.
AB - 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, gE(t) and gI(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 gE(t) and gI(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 gE(t) and gI(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 gE and gI. In some phases for the push-pull examples even the polarity of gI is mis-estimated, indicating significant increase when gI is actually decreased. Our primary message is to be cautious about forming interpretations based on estimates developed during spiking phases.
KW - Conductance-based models
KW - Estimation of conductances
KW - Primary visual cortex
KW - Spiking neurons
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U2 - 10.1016/j.jphysparis.2006.09.010
DO - 10.1016/j.jphysparis.2006.09.010
M3 - Article
C2 - 17084599
AN - SCOPUS:37849186763
SN - 0928-4257
VL - 100
SP - 31
EP - 42
JO - Journal of Physiology Paris
JF - Journal of Physiology Paris
IS - 1-3
ER -