Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.
Abstract
GABA is generally known as the principal inhibitory neurotransmitter in the nervous
system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents
sometimes exhibit non-inhibitory effects, depending on the brain region, developmental
stage or pathological condition. Here, we investigate the diverse effects of GABA
on the firing rate of several single neuron models, using both analytical calculations
and numerical simulations. We find that GABAergic synaptic conductance and output
firing rate exhibit three qualitatively different regimes as a function of GABA reversal
potential, EGABA: monotonically decreasing for sufficiently low EGABA (inhibitory),
monotonically increasing for EGABA above firing threshold (excitatory); and a non-monotonic
region for intermediate values of EGABA. In the non-monotonic regime, small GABA conductances
have an excitatory effect while large GABA conductances show an inhibitory effect.
We provide a phase diagram of different GABAergic effects as a function of GABA reversal
potential and glutamate conductance. We find that noisy inputs increase the range
of EGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit
model of striatum to explain observed effects of GABAergic fast spiking interneurons
on spiny projection neurons, including non-monotonicity, as well as the heterogeneity
of the effects. Our work provides a mechanistic explanation of paradoxical effects
of GABAergic synaptic inputs, with implications for understanding the effects of GABA
in neural computation and development.
Type
Journal articleSubject
Corpus StriatumNeurons
Interneurons
gamma-Aminobutyric Acid
Synaptic Transmission
Membrane Potentials
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https://hdl.handle.net/10161/25453Published Version (Please cite this version)
10.1371/journal.pcbi.1010226Publication Info
Abed Zadeh, Aghil; Turner, Brandon D; Calakos, Nicole; & Brunel, Nicolas (2022). Non-monotonic effects of GABAergic synaptic inputs on neuronal firing. PLoS computational biology, 18(6). pp. e1010226. 10.1371/journal.pcbi.1010226. Retrieved from https://hdl.handle.net/10161/25453.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Nicolas Brunel
Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn
information from their inputs. Our current work focuses on the mechanisms of learning
and memory, from the synapse to the network level, in collaboration with various experimental
groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity
of a network that maximizes storage capacity reproducestwo key experimentally observed
features: low connection proba
Nicole Calakos
Lincoln Financial Group Distinguished Professor of Neurobiology
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