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Intrinsic neuronal properties switch the mode of information transmission in networks.

Gjorgjieva J, Mease RA, Moody WJ, Fairhall AL - PLoS Comput. Biol. (2014)

Bottom Line: Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission.The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity.This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT
Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.

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Stimulus encoding varies with the intrinsic properties of neurons.A. Noise fluctuations (black) superimposed on a short ramping input stimulus (red) with rise time of 50 ms were presented to two separate populations of 100 independent conductance-based model neurons with different gain-scaling properties. B,C. Voltage responses of (B) 100 NGS ( pS/µm2 and  pS/µm2) and (C) 100 GS neurons ( pS/µm2 and  pS/µm2) to the ramp input in A. The different colors indicate voltage responses of different neurons. D. Noise fluctuations with a correlation time constant of 1 ms (black) superimposed on a Gaussian input stimulus low-pass filtered at 500 ms (red) for a duration of 10 seconds were also presented to the two neuron populations. E,F. Population response (PSTH) of NGS (E) and GS (F) neurons to the input in D.
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pcbi-1003962-g003: Stimulus encoding varies with the intrinsic properties of neurons.A. Noise fluctuations (black) superimposed on a short ramping input stimulus (red) with rise time of 50 ms were presented to two separate populations of 100 independent conductance-based model neurons with different gain-scaling properties. B,C. Voltage responses of (B) 100 NGS ( pS/µm2 and pS/µm2) and (C) 100 GS neurons ( pS/µm2 and pS/µm2) to the ramp input in A. The different colors indicate voltage responses of different neurons. D. Noise fluctuations with a correlation time constant of 1 ms (black) superimposed on a Gaussian input stimulus low-pass filtered at 500 ms (red) for a duration of 10 seconds were also presented to the two neuron populations. E,F. Population response (PSTH) of NGS (E) and GS (F) neurons to the input in D.

Mentions: Upon characterizing single neuron responses of the two neuron types to fast-varying information via the LN models and to slow-varying information via the – curves, we compared their population responses to stimuli with fast and slow timescales. A population of uncoupled neurons of each type was stimulated with a common slow ramp of input current, and superimposed fast-varying noise inputs, generated independently for each neuron (Figure 3A). The population of NGS neurons fired synchronously with respect to the ramp input and only during the peak of the ramp (Figure 3B), while the GS neurons were more sensitive to the background noise and fired asynchronously during the ramp (Figure 3C) with a firing rate that was continuously modulated by the ramp input. This suggests that the sensitivity to noise fluctuations of the GS neurons at the single neuron level allows them to better encode slower variations in the common signal at the population level [25]–[27], in contrast to the NGS population which only responds to events of large amplitude independent of the background noise.


Intrinsic neuronal properties switch the mode of information transmission in networks.

Gjorgjieva J, Mease RA, Moody WJ, Fairhall AL - PLoS Comput. Biol. (2014)

Stimulus encoding varies with the intrinsic properties of neurons.A. Noise fluctuations (black) superimposed on a short ramping input stimulus (red) with rise time of 50 ms were presented to two separate populations of 100 independent conductance-based model neurons with different gain-scaling properties. B,C. Voltage responses of (B) 100 NGS ( pS/µm2 and  pS/µm2) and (C) 100 GS neurons ( pS/µm2 and  pS/µm2) to the ramp input in A. The different colors indicate voltage responses of different neurons. D. Noise fluctuations with a correlation time constant of 1 ms (black) superimposed on a Gaussian input stimulus low-pass filtered at 500 ms (red) for a duration of 10 seconds were also presented to the two neuron populations. E,F. Population response (PSTH) of NGS (E) and GS (F) neurons to the input in D.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4256072&req=5

pcbi-1003962-g003: Stimulus encoding varies with the intrinsic properties of neurons.A. Noise fluctuations (black) superimposed on a short ramping input stimulus (red) with rise time of 50 ms were presented to two separate populations of 100 independent conductance-based model neurons with different gain-scaling properties. B,C. Voltage responses of (B) 100 NGS ( pS/µm2 and pS/µm2) and (C) 100 GS neurons ( pS/µm2 and pS/µm2) to the ramp input in A. The different colors indicate voltage responses of different neurons. D. Noise fluctuations with a correlation time constant of 1 ms (black) superimposed on a Gaussian input stimulus low-pass filtered at 500 ms (red) for a duration of 10 seconds were also presented to the two neuron populations. E,F. Population response (PSTH) of NGS (E) and GS (F) neurons to the input in D.
Mentions: Upon characterizing single neuron responses of the two neuron types to fast-varying information via the LN models and to slow-varying information via the – curves, we compared their population responses to stimuli with fast and slow timescales. A population of uncoupled neurons of each type was stimulated with a common slow ramp of input current, and superimposed fast-varying noise inputs, generated independently for each neuron (Figure 3A). The population of NGS neurons fired synchronously with respect to the ramp input and only during the peak of the ramp (Figure 3B), while the GS neurons were more sensitive to the background noise and fired asynchronously during the ramp (Figure 3C) with a firing rate that was continuously modulated by the ramp input. This suggests that the sensitivity to noise fluctuations of the GS neurons at the single neuron level allows them to better encode slower variations in the common signal at the population level [25]–[27], in contrast to the NGS population which only responds to events of large amplitude independent of the background noise.

Bottom Line: Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission.The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity.This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT
Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.

Show MeSH
Related in: MedlinePlus