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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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Analysis of the reduced Mainen model.A. Top: Fixed points and their stability for the dynamics of a NGS neuron with  pS/µm2 and  pS/µm2 () as a function of the input current DC. Bottom: The phase planes showing the clines (black) and their intersection points (fixed points) together with the flow lines indicated by the arrows. A single trajectory is shown in red. The inset shows a zoomed portion of the phase plane near the fixed point. Below we show trajectories for two values of  and two DC values. B. The fixed points for different ratios , while keeping  pS/µm2 and varying , as a function of the DC. C. Same as A but for a GS neuron with  pS/µm2 and  pS/µm2 (). Note that the abscissa has been scaled from A and B.
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pcbi-1003962-g008: Analysis of the reduced Mainen model.A. Top: Fixed points and their stability for the dynamics of a NGS neuron with pS/µm2 and pS/µm2 () as a function of the input current DC. Bottom: The phase planes showing the clines (black) and their intersection points (fixed points) together with the flow lines indicated by the arrows. A single trajectory is shown in red. The inset shows a zoomed portion of the phase plane near the fixed point. Below we show trajectories for two values of and two DC values. B. The fixed points for different ratios , while keeping pS/µm2 and varying , as a function of the DC. C. Same as A but for a GS neuron with pS/µm2 and pS/µm2 (). Note that the abscissa has been scaled from A and B.

Mentions: The differential ability of GS and NGS networks to reliably propagate mean input signals is predicted by the modulability of the – curves by the network noise . To understand the dynamical origins of this difference, we analytically reduced the neuron model (Eq. 2) to a system of two first order differential equations describing the dynamics of the membrane potential and an auxiliary slower-varying potential variable (Methods) [35]. We analyzed the dynamics in the phase plane by plotting vs. . The clines, curves along which the change in either or is 0, organize the flows of and (Figure 8); these lines intersect at the fixed points of the neuron's dynamics. We studied the fixed points at different ratios of and , with a particular focus on the values discussed above ( and ). These exhibit substantial differences in the type and stability of the fixed points, as well as the emergent bifurcations where the fixed points change stability as one varies the mean DC input current into the neuron (Figure 8).


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

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

Analysis of the reduced Mainen model.A. Top: Fixed points and their stability for the dynamics of a NGS neuron with  pS/µm2 and  pS/µm2 () as a function of the input current DC. Bottom: The phase planes showing the clines (black) and their intersection points (fixed points) together with the flow lines indicated by the arrows. A single trajectory is shown in red. The inset shows a zoomed portion of the phase plane near the fixed point. Below we show trajectories for two values of  and two DC values. B. The fixed points for different ratios , while keeping  pS/µm2 and varying , as a function of the DC. C. Same as A but for a GS neuron with  pS/µm2 and  pS/µm2 (). Note that the abscissa has been scaled from A and B.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003962-g008: Analysis of the reduced Mainen model.A. Top: Fixed points and their stability for the dynamics of a NGS neuron with pS/µm2 and pS/µm2 () as a function of the input current DC. Bottom: The phase planes showing the clines (black) and their intersection points (fixed points) together with the flow lines indicated by the arrows. A single trajectory is shown in red. The inset shows a zoomed portion of the phase plane near the fixed point. Below we show trajectories for two values of and two DC values. B. The fixed points for different ratios , while keeping pS/µm2 and varying , as a function of the DC. C. Same as A but for a GS neuron with pS/µm2 and pS/µm2 (). Note that the abscissa has been scaled from A and B.
Mentions: The differential ability of GS and NGS networks to reliably propagate mean input signals is predicted by the modulability of the – curves by the network noise . To understand the dynamical origins of this difference, we analytically reduced the neuron model (Eq. 2) to a system of two first order differential equations describing the dynamics of the membrane potential and an auxiliary slower-varying potential variable (Methods) [35]. We analyzed the dynamics in the phase plane by plotting vs. . The clines, curves along which the change in either or is 0, organize the flows of and (Figure 8); these lines intersect at the fixed points of the neuron's dynamics. We studied the fixed points at different ratios of and , with a particular focus on the values discussed above ( and ). These exhibit substantial differences in the type and stability of the fixed points, as well as the emergent bifurcations where the fixed points change stability as one varies the mean DC input current into the neuron (Figure 8).

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