Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses.
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.
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.
Birgit
B
Kriener
Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences Ås, Norway ; Simula Research Laboratory, Kalkulo AS Fornebu, Norway.
Håkon
H
Enger
Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience and Institute for Advanced Simulation (IAS-6), Theoretical Neuroscience, Jülich Research Centre and JARA Jülich, Germany.
Tom
T
Tetzlaff
Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences Ås, Norway.
Hans E
HE
Plesser
Blue Brain Project, In-Silico Neuroscience - Cognitive Architectures, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland.
Marc-Oliver
MO
Gewaltig
Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences Ås, Norway ; Department of Physics, University of Oslo Oslo, Norway.
Gaute T
GT
Einevoll
Self-sustained activity; balanced random networks; leaky integrate-and-fire neurons; linear stability; spike train irregularity