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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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Information transmission through GS and NGS networks.A. Feedforward network with a slowly modulated time-varying input (magenta) presented to all neurons in the first layer, each neuron receiving in addition an independent noisy signal (black). B. Spike rasters for GS neurons ( pS/µm2 and  pS/µm2) show the rapid signal degradation in deeper layers, while NGS neurons ( pS/µm2 and  pS/µm2) exhibit reliable signal transmission of large-amplitude events. The spiking responses synchronize in deeper layers. C. PSTHs from each layer in the two networks showing the propagation of a slow-varying input in the presence of background fast fluctuations. PSTHs were normalized to mean 0 and variance 1 to illustrate fluctuations (in spite of different firing rates) so that the dashed lines next to each PSTH denote 0 and the scalebar 2 normalized units. D. Information about the slow stimulus fluctuations conveyed by the population mean responses shown in C.
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pcbi-1003962-g004: Information transmission through GS and NGS networks.A. Feedforward network with a slowly modulated time-varying input (magenta) presented to all neurons in the first layer, each neuron receiving in addition an independent noisy signal (black). B. Spike rasters for GS neurons ( pS/µm2 and pS/µm2) show the rapid signal degradation in deeper layers, while NGS neurons ( pS/µm2 and pS/µm2) exhibit reliable signal transmission of large-amplitude events. The spiking responses synchronize in deeper layers. C. PSTHs from each layer in the two networks showing the propagation of a slow-varying input in the presence of background fast fluctuations. PSTHs were normalized to mean 0 and variance 1 to illustrate fluctuations (in spite of different firing rates) so that the dashed lines next to each PSTH denote 0 and the scalebar 2 normalized units. D. Information about the slow stimulus fluctuations conveyed by the population mean responses shown in C.

Mentions: The observed difference between the population responses of the GS and NGS neurons to the slow-varying stimulus in the presence of fast background fluctuations (Figure 3D–F) suggested that the two neuron types differ in their ability to transmit information at slow timescales. Therefore, we next examined how the identified single neuron properties affect information transmission across multiple layers in feedforward networks. Networks consisted of 10 layers of 2000 identical neurons of the two different types (Figure 4A). The neurons in the first layer receive a common temporally fluctuating stimulus with a long correlation time constant (1 s, see Methods); neurons in deeper layers receive synaptic input from neurons in the previous layer via conductance-based synapses. Each neuron in the network also receives a rapidly varying independent noise input (with a correlation time constant of 1 ms) to simulate fast-varying synaptic fluctuations. The noise input here is a rapidly-varying input that sets the statistical context for the slow-varying information; it does not transmit specific stimulus information itself. The GS and NGS networks have strikingly different spiking dynamics (Figure 4B). The GS network responds with higher mean firing rates in each layer, as would be expected from the – curves characterizing intrinsic neuronal properties (Figure 1C,D). While the GS neurons have a baseline firing rate even at zero input current, the NGS neurons only fire for large input currents, with a threshold dependent on the level of intrinsic noise; thus, the two neuron types have different firing rates. To evaluate how the networks transmit fluctuations of the slow-varying common input signal, independent of the overall firing rates, we evaluated the averaged population (PSTH) response of each layer, normalized to have a mean equal to 0 and a variance equal to 1 (Figure 4C).


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

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

Information transmission through GS and NGS networks.A. Feedforward network with a slowly modulated time-varying input (magenta) presented to all neurons in the first layer, each neuron receiving in addition an independent noisy signal (black). B. Spike rasters for GS neurons ( pS/µm2 and  pS/µm2) show the rapid signal degradation in deeper layers, while NGS neurons ( pS/µm2 and  pS/µm2) exhibit reliable signal transmission of large-amplitude events. The spiking responses synchronize in deeper layers. C. PSTHs from each layer in the two networks showing the propagation of a slow-varying input in the presence of background fast fluctuations. PSTHs were normalized to mean 0 and variance 1 to illustrate fluctuations (in spite of different firing rates) so that the dashed lines next to each PSTH denote 0 and the scalebar 2 normalized units. D. Information about the slow stimulus fluctuations conveyed by the population mean responses shown in C.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4256072&req=5

pcbi-1003962-g004: Information transmission through GS and NGS networks.A. Feedforward network with a slowly modulated time-varying input (magenta) presented to all neurons in the first layer, each neuron receiving in addition an independent noisy signal (black). B. Spike rasters for GS neurons ( pS/µm2 and pS/µm2) show the rapid signal degradation in deeper layers, while NGS neurons ( pS/µm2 and pS/µm2) exhibit reliable signal transmission of large-amplitude events. The spiking responses synchronize in deeper layers. C. PSTHs from each layer in the two networks showing the propagation of a slow-varying input in the presence of background fast fluctuations. PSTHs were normalized to mean 0 and variance 1 to illustrate fluctuations (in spite of different firing rates) so that the dashed lines next to each PSTH denote 0 and the scalebar 2 normalized units. D. Information about the slow stimulus fluctuations conveyed by the population mean responses shown in C.
Mentions: The observed difference between the population responses of the GS and NGS neurons to the slow-varying stimulus in the presence of fast background fluctuations (Figure 3D–F) suggested that the two neuron types differ in their ability to transmit information at slow timescales. Therefore, we next examined how the identified single neuron properties affect information transmission across multiple layers in feedforward networks. Networks consisted of 10 layers of 2000 identical neurons of the two different types (Figure 4A). The neurons in the first layer receive a common temporally fluctuating stimulus with a long correlation time constant (1 s, see Methods); neurons in deeper layers receive synaptic input from neurons in the previous layer via conductance-based synapses. Each neuron in the network also receives a rapidly varying independent noise input (with a correlation time constant of 1 ms) to simulate fast-varying synaptic fluctuations. The noise input here is a rapidly-varying input that sets the statistical context for the slow-varying information; it does not transmit specific stimulus information itself. The GS and NGS networks have strikingly different spiking dynamics (Figure 4B). The GS network responds with higher mean firing rates in each layer, as would be expected from the – curves characterizing intrinsic neuronal properties (Figure 1C,D). While the GS neurons have a baseline firing rate even at zero input current, the NGS neurons only fire for large input currents, with a threshold dependent on the level of intrinsic noise; thus, the two neuron types have different firing rates. To evaluate how the networks transmit fluctuations of the slow-varying common input signal, independent of the overall firing rates, we evaluated the averaged population (PSTH) response of each layer, normalized to have a mean equal to 0 and a variance equal to 1 (Figure 4C).

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