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The role of inhibition in a computational model of an auditory cortical neuron during the encoding of temporal information.

Bendor D - PLoS Comput. Biol. (2015)

Bottom Line: Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition.In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition.Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex.

View Article: PubMed Central - PubMed

Affiliation: Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, United Kingdom.

ABSTRACT
In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex.

No MeSH data available.


Related in: MedlinePlus

Computational model of an auditory cortical neuron.Error bars indicate SEM. a. The acoustic stimulus (top) used in our neurophysiological experiments was a narrowband acoustic pulse train. Each pulse was converted into an excitatory and inhibitory conductance in our computational model, using an alpha function with a time constant of 5 ms (middle). Three parameters could be altered (I-E delay, E input, and I/E ratio). Above threshold changes in the membrane voltage generated spikes (bottom), which could be further analyzed to measure the response properties of the simulated neuron. b. Classification of neural coding regime based on the two criteria (dashed lines)- y axis: Rayleigh statistic at an IPI of 75 ms>13.8, x axis: Discharge rate ratio>1. Neurons were classified as having a non-synchronized (o), synchronized (x), or mixed (+) response. c. Comparison of stimulus synchronization in real (gray) and simulated (red) synchronized neurons across different IPIs (3–75 ms). d. Comparison of normalized discharge rate in real (gray) and simulated (blue) non-synchronized neurons across different IPIs (3–75 ms).
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pcbi.1004197.g002: Computational model of an auditory cortical neuron.Error bars indicate SEM. a. The acoustic stimulus (top) used in our neurophysiological experiments was a narrowband acoustic pulse train. Each pulse was converted into an excitatory and inhibitory conductance in our computational model, using an alpha function with a time constant of 5 ms (middle). Three parameters could be altered (I-E delay, E input, and I/E ratio). Above threshold changes in the membrane voltage generated spikes (bottom), which could be further analyzed to measure the response properties of the simulated neuron. b. Classification of neural coding regime based on the two criteria (dashed lines)- y axis: Rayleigh statistic at an IPI of 75 ms>13.8, x axis: Discharge rate ratio>1. Neurons were classified as having a non-synchronized (o), synchronized (x), or mixed (+) response. c. Comparison of stimulus synchronization in real (gray) and simulated (red) synchronized neurons across different IPIs (3–75 ms). d. Comparison of normalized discharge rate in real (gray) and simulated (blue) non-synchronized neurons across different IPIs (3–75 ms).

Mentions: We developed an integrate-and-fire computational model of an auditory cortical neuron [23], based on previously reported data obtained using in-vivo, whole-cell recordings from rodent primary auditory cortex [24] (see Methods). We tested our model with acoustic pulse trains spanning the perceptual range of flutter/fusion perception, with interpulse intervals (IPIs) ranging between 3–75 ms. Each acoustic pulse was modeled as a change in the excitatory and inhibitory conductance, governed by an alpha function with a 5 ms time constant (Fig. 2a, Wehr and Zador 2003). Our model consisted of three parameters: 1) I-E delay- the temporal delay between inhibitory and excitatory inputs, 2) I/E ratio- the ratio between the magnitude of inhibitory and excitatory inputs, and 3) Excitatory input- the magnitude of the excitatory input (Fig. 2a). To ensure that the parameters of our model were physiologically realistic, we used a previously reported range of I/E ratio and I-E delay values obtained using intracellular recordings from auditory cortical neurons [24]. While our model does not explicitly simulate a specific cell-type or lamina of auditory cortex, the pattern of excitation combined with feedforward inhibition is consistent with the canonical circuitry of layer 2/3 auditory cortex, where synchronized and non-synchronized neurons have been previously identified in marmoset auditory cortex [14].


The role of inhibition in a computational model of an auditory cortical neuron during the encoding of temporal information.

Bendor D - PLoS Comput. Biol. (2015)

Computational model of an auditory cortical neuron.Error bars indicate SEM. a. The acoustic stimulus (top) used in our neurophysiological experiments was a narrowband acoustic pulse train. Each pulse was converted into an excitatory and inhibitory conductance in our computational model, using an alpha function with a time constant of 5 ms (middle). Three parameters could be altered (I-E delay, E input, and I/E ratio). Above threshold changes in the membrane voltage generated spikes (bottom), which could be further analyzed to measure the response properties of the simulated neuron. b. Classification of neural coding regime based on the two criteria (dashed lines)- y axis: Rayleigh statistic at an IPI of 75 ms>13.8, x axis: Discharge rate ratio>1. Neurons were classified as having a non-synchronized (o), synchronized (x), or mixed (+) response. c. Comparison of stimulus synchronization in real (gray) and simulated (red) synchronized neurons across different IPIs (3–75 ms). d. Comparison of normalized discharge rate in real (gray) and simulated (blue) non-synchronized neurons across different IPIs (3–75 ms).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4400160&req=5

pcbi.1004197.g002: Computational model of an auditory cortical neuron.Error bars indicate SEM. a. The acoustic stimulus (top) used in our neurophysiological experiments was a narrowband acoustic pulse train. Each pulse was converted into an excitatory and inhibitory conductance in our computational model, using an alpha function with a time constant of 5 ms (middle). Three parameters could be altered (I-E delay, E input, and I/E ratio). Above threshold changes in the membrane voltage generated spikes (bottom), which could be further analyzed to measure the response properties of the simulated neuron. b. Classification of neural coding regime based on the two criteria (dashed lines)- y axis: Rayleigh statistic at an IPI of 75 ms>13.8, x axis: Discharge rate ratio>1. Neurons were classified as having a non-synchronized (o), synchronized (x), or mixed (+) response. c. Comparison of stimulus synchronization in real (gray) and simulated (red) synchronized neurons across different IPIs (3–75 ms). d. Comparison of normalized discharge rate in real (gray) and simulated (blue) non-synchronized neurons across different IPIs (3–75 ms).
Mentions: We developed an integrate-and-fire computational model of an auditory cortical neuron [23], based on previously reported data obtained using in-vivo, whole-cell recordings from rodent primary auditory cortex [24] (see Methods). We tested our model with acoustic pulse trains spanning the perceptual range of flutter/fusion perception, with interpulse intervals (IPIs) ranging between 3–75 ms. Each acoustic pulse was modeled as a change in the excitatory and inhibitory conductance, governed by an alpha function with a 5 ms time constant (Fig. 2a, Wehr and Zador 2003). Our model consisted of three parameters: 1) I-E delay- the temporal delay between inhibitory and excitatory inputs, 2) I/E ratio- the ratio between the magnitude of inhibitory and excitatory inputs, and 3) Excitatory input- the magnitude of the excitatory input (Fig. 2a). To ensure that the parameters of our model were physiologically realistic, we used a previously reported range of I/E ratio and I-E delay values obtained using intracellular recordings from auditory cortical neurons [24]. While our model does not explicitly simulate a specific cell-type or lamina of auditory cortex, the pattern of excitation combined with feedforward inhibition is consistent with the canonical circuitry of layer 2/3 auditory cortex, where synchronized and non-synchronized neurons have been previously identified in marmoset auditory cortex [14].

Bottom Line: Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition.In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition.Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex.

View Article: PubMed Central - PubMed

Affiliation: Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, United Kingdom.

ABSTRACT
In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex.

No MeSH data available.


Related in: MedlinePlus