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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

Network activity on the parameter plane of low synaptic strengths: a typical distribution of network activity patterns for 210 neurons. Network parameters and the coloring scheme as in the top panel of Figure 3.
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Figure 5: Network activity on the parameter plane of low synaptic strengths: a typical distribution of network activity patterns for 210 neurons. Network parameters and the coloring scheme as in the top panel of Figure 3.

Mentions: 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).


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)

Network activity on the parameter plane of low synaptic strengths: a typical distribution of network activity patterns for 210 neurons. Network parameters and the coloring scheme as in the top panel of Figure 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Network activity on the parameter plane of low synaptic strengths: a typical distribution of network activity patterns for 210 neurons. Network parameters and the coloring scheme as in the top panel of Figure 3.
Mentions: 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).

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