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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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– curves and information as a function of individual maximal Na and K conductances.A. The – curves for different maximal Na and K conductances,  and , in pS/µm2 (compare to Figure 1C,D). B. The information for the different models as a function of  (compare to Figure 1F).
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pcbi-1003962-g002: – curves and information as a function of individual maximal Na and K conductances.A. The – curves for different maximal Na and K conductances, and , in pS/µm2 (compare to Figure 1C,D). B. The information for the different models as a function of (compare to Figure 1F).

Mentions: To characterize the spectrum of intrinsic properties that might arise as a result of different maximal conductances, and , we determined the – curves for a range of maximal conductances in the conductance-based model neurons (Figure 2). Mease et al. [8] previously classified neurons as spontaneously active, excitable or silent, and based on the neurons' LN models determined gain-scaling ability as a function of the individual and for excitable neurons. Models with low had nonlinear input-output relations that did not scale completely with , while models with high had almost identical nonlinear input-output relations for all [8]. Therefore, gain scaling ability increased with increasing ratio, independent of each individual conductance.


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

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

– curves and information as a function of individual maximal Na and K conductances.A. The – curves for different maximal Na and K conductances,  and , in pS/µm2 (compare to Figure 1C,D). B. The information for the different models as a function of  (compare to Figure 1F).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003962-g002: – curves and information as a function of individual maximal Na and K conductances.A. The – curves for different maximal Na and K conductances, and , in pS/µm2 (compare to Figure 1C,D). B. The information for the different models as a function of (compare to Figure 1F).
Mentions: To characterize the spectrum of intrinsic properties that might arise as a result of different maximal conductances, and , we determined the – curves for a range of maximal conductances in the conductance-based model neurons (Figure 2). Mease et al. [8] previously classified neurons as spontaneously active, excitable or silent, and based on the neurons' LN models determined gain-scaling ability as a function of the individual and for excitable neurons. Models with low had nonlinear input-output relations that did not scale completely with , while models with high had almost identical nonlinear input-output relations for all [8]. Therefore, gain scaling ability increased with increasing ratio, independent of each individual conductance.

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