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Robust sequential working memory recall in heterogeneous cognitive networks.

Rabinovich MI, Sokolov Y, Kozma R - Front Syst Neurosci (2014)

Bottom Line: As a result, competitive network dynamics is qualitatively altered.The results are interpreted in the context of the winnerless competition principle.We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

View Article: PubMed Central - PubMed

Affiliation: BioCircuits Institute, University of California San Diego La Jolla, CA, USA.

ABSTRACT
Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon-clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain-the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

No MeSH data available.


Related in: MedlinePlus

Examples of time series illustrating heteroclinic chimera in a GLV network with degraded inhibitor weights; the GLV network has 6 units, and two units are shown here. (A) Variable R1(t) exhibits SHC regime with stable values over extended time periods; (B) Variable R2(t) has frequent irregular oscillations; the determined positive Lyapunov exponent indicates the existence of chaos.
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Figure 7: Examples of time series illustrating heteroclinic chimera in a GLV network with degraded inhibitor weights; the GLV network has 6 units, and two units are shown here. (A) Variable R1(t) exhibits SHC regime with stable values over extended time periods; (B) Variable R2(t) has frequent irregular oscillations; the determined positive Lyapunov exponent indicates the existence of chaos.

Mentions: Figure 7 illustrates heteroclinic chimera in a GLV network with degraded inhibitor weights. This GLV network has 6 units, from which two units are shown here, R1 and R2, respectively. Figure 7A depicts variable R1(t), which exhibits SHC regime with stable values over extended time periods. Figure 7B shows the temporal evolution of variable R2(t), which has frequent irregular oscillations. The determined positive Lyapunov exponent indicates the existence of chaos. The dynamics of all 6 variables Ri, i = 1, …, 6 of the abnormal system is summarized in Figure 8 using a raster plot. A given variable is shown in the plot if its amplitude exceeds a threshold value (of 0.1). Note the preservation of sequential switching in the boxes marked by dashed lines. While some of the variables apparently maintain stable heteroclinic trajectories, others exhibit intermittent oscillations and chaos, as a manifestation of heteroclinic chimera dynamics.


Robust sequential working memory recall in heterogeneous cognitive networks.

Rabinovich MI, Sokolov Y, Kozma R - Front Syst Neurosci (2014)

Examples of time series illustrating heteroclinic chimera in a GLV network with degraded inhibitor weights; the GLV network has 6 units, and two units are shown here. (A) Variable R1(t) exhibits SHC regime with stable values over extended time periods; (B) Variable R2(t) has frequent irregular oscillations; the determined positive Lyapunov exponent indicates the existence of chaos.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231877&req=5

Figure 7: Examples of time series illustrating heteroclinic chimera in a GLV network with degraded inhibitor weights; the GLV network has 6 units, and two units are shown here. (A) Variable R1(t) exhibits SHC regime with stable values over extended time periods; (B) Variable R2(t) has frequent irregular oscillations; the determined positive Lyapunov exponent indicates the existence of chaos.
Mentions: Figure 7 illustrates heteroclinic chimera in a GLV network with degraded inhibitor weights. This GLV network has 6 units, from which two units are shown here, R1 and R2, respectively. Figure 7A depicts variable R1(t), which exhibits SHC regime with stable values over extended time periods. Figure 7B shows the temporal evolution of variable R2(t), which has frequent irregular oscillations. The determined positive Lyapunov exponent indicates the existence of chaos. The dynamics of all 6 variables Ri, i = 1, …, 6 of the abnormal system is summarized in Figure 8 using a raster plot. A given variable is shown in the plot if its amplitude exceeds a threshold value (of 0.1). Note the preservation of sequential switching in the boxes marked by dashed lines. While some of the variables apparently maintain stable heteroclinic trajectories, others exhibit intermittent oscillations and chaos, as a manifestation of heteroclinic chimera dynamics.

Bottom Line: As a result, competitive network dynamics is qualitatively altered.The results are interpreted in the context of the winnerless competition principle.We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

View Article: PubMed Central - PubMed

Affiliation: BioCircuits Institute, University of California San Diego La Jolla, CA, USA.

ABSTRACT
Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon-clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain-the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

No MeSH data available.


Related in: MedlinePlus