Limits...
Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses.

Kriener B, Enger H, Tetzlaff T, Plesser HE, Gewaltig MO, Einevoll GT - Front Comput Neurosci (2014)

Bottom Line: We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network.During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states.We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

View Article: PubMed Central - PubMed

Affiliation: Neural Coding and Dynamics, Center for Learning and Memory, University of Texas at Austin Austin, TX, USA ; Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences Ås, Norway.

ABSTRACT
Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

No MeSH data available.


Related in: MedlinePlus

Spike-train irregularity (A) and pairwise correlations (B) in the SSAI-state. Dependence of the mean coefficient of variation CV, see Equation (9), of the inter-spike intervals (A) and the mean spike-train correlation coefficient Equation (11) (B) on the synaptic weight J and the relative strength g of inhibition. The gray-shaded area marks regions where activity was not sufficient for analysis (see Figure 3A). Other parameters as in Figure 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4214205&req=5

Figure 4: Spike-train irregularity (A) and pairwise correlations (B) in the SSAI-state. Dependence of the mean coefficient of variation CV, see Equation (9), of the inter-spike intervals (A) and the mean spike-train correlation coefficient Equation (11) (B) on the synaptic weight J and the relative strength g of inhibition. The gray-shaded area marks regions where activity was not sufficient for analysis (see Figure 3A). Other parameters as in Figure 1.

Mentions: Figure 4A, moreover, demonstrates that during SSAI the coefficient of variation (CV) of inter-spike interval (ISI) are typically substantially higher than unity, meaning that spike trains are more irregular than a Poisson process, while Figure 4B shows that pairwise spike-train correlations—indicating residual synchrony—decrease for longer lifetimes, especially in the region of large g and J.


Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses.

Kriener B, Enger H, Tetzlaff T, Plesser HE, Gewaltig MO, Einevoll GT - Front Comput Neurosci (2014)

Spike-train irregularity (A) and pairwise correlations (B) in the SSAI-state. Dependence of the mean coefficient of variation CV, see Equation (9), of the inter-spike intervals (A) and the mean spike-train correlation coefficient Equation (11) (B) on the synaptic weight J and the relative strength g of inhibition. The gray-shaded area marks regions where activity was not sufficient for analysis (see Figure 3A). Other parameters as in Figure 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214205&req=5

Figure 4: Spike-train irregularity (A) and pairwise correlations (B) in the SSAI-state. Dependence of the mean coefficient of variation CV, see Equation (9), of the inter-spike intervals (A) and the mean spike-train correlation coefficient Equation (11) (B) on the synaptic weight J and the relative strength g of inhibition. The gray-shaded area marks regions where activity was not sufficient for analysis (see Figure 3A). Other parameters as in Figure 1.
Mentions: Figure 4A, moreover, demonstrates that during SSAI the coefficient of variation (CV) of inter-spike interval (ISI) are typically substantially higher than unity, meaning that spike trains are more irregular than a Poisson process, while Figure 4B shows that pairwise spike-train correlations—indicating residual synchrony—decrease for longer lifetimes, especially in the region of large g and J.

Bottom Line: We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network.During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states.We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

View Article: PubMed Central - PubMed

Affiliation: Neural Coding and Dynamics, Center for Learning and Memory, University of Texas at Austin Austin, TX, USA ; Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences Ås, Norway.

ABSTRACT
Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

No MeSH data available.


Related in: MedlinePlus