Feed-forward inhibition as a buffer of the neuronal input-output relation

Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal b...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 106; no. 42; pp. 18004 - 18009
Main Authors Ferrante, Michele, Migliore, Michele, Ascoli, Giorgio A
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 20.10.2009
National Acad Sciences
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Online AccessGet full text
ISSN0027-8424
1091-6490
1091-6490
DOI10.1073/pnas.0904784106

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Abstract Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.
AbstractList Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.
Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration. [PUBLICATION ABSTRACT]
Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.
Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultantaxonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/Orelation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuationswithin the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forwardinhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatoryinput, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediateplateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisysynaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulatedby biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providinga soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramaticallyincrease the computational power of neuronal integration.
Author Migliore, Michele
Ascoli, Giorgio A
Ferrante, Michele
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/19815518$$D View this record in MEDLINE/PubMed
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Snippet Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The...
Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultantaxonal firing rate. The...
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StartPage 18004
SubjectTerms Action Potentials - physiology
Animals
Biological Sciences
Brain
Cells
Dendrites
Dentate gyrus
Dentate Gyrus - cytology
Dentate Gyrus - physiology
Electrophysiological Phenomena
Feedback, Physiological - physiology
Frequency ranges
gamma-Aminobutyric Acid - physiology
Hippocampus
Input output
Interneurons
Interneurons - physiology
Membranes
Models, Neurological
Nerve Net - cytology
Nerve Net - physiology
Neurons
neurophysiology
Neuroscience
Pyramidal cells
Rats
Signal transduction
Synapses
Synapses - physiology
Title Feed-forward inhibition as a buffer of the neuronal input-output relation
URI https://www.jstor.org/stable/25592945
http://www.pnas.org/content/106/42/18004.abstract
https://www.ncbi.nlm.nih.gov/pubmed/19815518
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https://pubmed.ncbi.nlm.nih.gov/PMC2764942
Volume 106
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