Limits...
Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types.

Tomov P, Pena RF, Zaks MA, Roque AC - Front Comput Neurosci (2014)

Bottom Line: The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime.Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class.These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

View Article: PubMed Central - PubMed

Affiliation: Institute of Mathematics, Humboldt University of Berlin Berlin, Germany.

ABSTRACT
The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

No MeSH data available.


Related in: MedlinePlus

Four types of network activity patterns. Each panel shows the raster plot of the spiking activity for a sample of 100 network neurons (Top), and the firing rate f(t) of all neurons (Bottom). Constant SSA: point A in Figure 3 (gex = 0.6, gin = 1). Persistent oscillatory SSA: point B in Figure 5 (gex = 0.12, gin = 0.6). Temporary oscillations: point C in Figure 5 (gex = 0.09, gin = 0.5). Decay: point D in Figure 5 (gex = 0.06, gin = 0.2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4151042&req=5

Figure 4: Four types of network activity patterns. Each panel shows the raster plot of the spiking activity for a sample of 100 network neurons (Top), and the firing rate f(t) of all neurons (Bottom). Constant SSA: point A in Figure 3 (gex = 0.6, gin = 1). Persistent oscillatory SSA: point B in Figure 5 (gex = 0.12, gin = 0.6). Temporary oscillations: point C in Figure 5 (gex = 0.09, gin = 0.5). Decay: point D in Figure 5 (gex = 0.06, gin = 0.2).

Mentions: The top panel of Figure 3 shows the duration and type of network activity. The blue region corresponds to fast decay of activity after termination of the external input with network activity lasting not longer than 50 ms. We call this type of behavior “rapid decay.” The yellow region indicates large-scale network activity oscillations, when, for a certain time after activation, different groups of neurons fire synchronously, and decay afterwards. We call this behavior “temporary oscillatory activity.” The red region corresponds to the same type of network behavior as in the yellow one, but lasting until the end of the simulation, and we call it “persistent oscillatory SSA.” The green region indicates SSA with strongly irregular individual neuronal firing and more or less constant overall network activity; this behavior is referred to as “constant SSA.” Examples of these four behavioral patterns are visualized in Figure 4.


Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types.

Tomov P, Pena RF, Zaks MA, Roque AC - Front Comput Neurosci (2014)

Four types of network activity patterns. Each panel shows the raster plot of the spiking activity for a sample of 100 network neurons (Top), and the firing rate f(t) of all neurons (Bottom). Constant SSA: point A in Figure 3 (gex = 0.6, gin = 1). Persistent oscillatory SSA: point B in Figure 5 (gex = 0.12, gin = 0.6). Temporary oscillations: point C in Figure 5 (gex = 0.09, gin = 0.5). Decay: point D in Figure 5 (gex = 0.06, gin = 0.2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4151042&req=5

Figure 4: Four types of network activity patterns. Each panel shows the raster plot of the spiking activity for a sample of 100 network neurons (Top), and the firing rate f(t) of all neurons (Bottom). Constant SSA: point A in Figure 3 (gex = 0.6, gin = 1). Persistent oscillatory SSA: point B in Figure 5 (gex = 0.12, gin = 0.6). Temporary oscillations: point C in Figure 5 (gex = 0.09, gin = 0.5). Decay: point D in Figure 5 (gex = 0.06, gin = 0.2).
Mentions: The top panel of Figure 3 shows the duration and type of network activity. The blue region corresponds to fast decay of activity after termination of the external input with network activity lasting not longer than 50 ms. We call this type of behavior “rapid decay.” The yellow region indicates large-scale network activity oscillations, when, for a certain time after activation, different groups of neurons fire synchronously, and decay afterwards. We call this behavior “temporary oscillatory activity.” The red region corresponds to the same type of network behavior as in the yellow one, but lasting until the end of the simulation, and we call it “persistent oscillatory SSA.” The green region indicates SSA with strongly irregular individual neuronal firing and more or less constant overall network activity; this behavior is referred to as “constant SSA.” Examples of these four behavioral patterns are visualized in Figure 4.

Bottom Line: The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime.Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class.These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

View Article: PubMed Central - PubMed

Affiliation: Institute of Mathematics, Humboldt University of Berlin Berlin, Germany.

ABSTRACT
The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

No MeSH data available.


Related in: MedlinePlus