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 in | Proceedings of the National Academy of Sciences - PNAS Vol. 106; no. 42; pp. 18004 - 18009 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
20.10.2009
National Acad Sciences |
Subjects | |
Online Access | Get full text |
ISSN | 0027-8424 1091-6490 1091-6490 |
DOI | 10.1073/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. |
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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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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 |
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