7 research outputs found
Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type
Despite their significant functional roles, beta-band oscillations are least
understood. Synchronization in neuronal networks have attracted much attention
in recent years with the main focus on transition type. Whether one obtains
explosive transition or a continuous transition is an important feature of the
neuronal network which can depend on network structure as well as synaptic
types. In this study we consider the effect of synaptic interaction (electrical
and chemical) as well as structural connectivity on synchronization transition
in network models of Izhikevich neurons which spike regularly with beta
rhythms. We find a wide range of behavior including continuous transition,
explosive transition, as well as lack of global order. The stronger electrical
synapses are more conducive to synchronization and can even lead to explosive
synchronization. The key network element which determines the order of
transition is found to be the clustering coefficient and not the small world
effect, or the existence of hubs in a network. These results are in contrast to
previous results which use phase oscillator models such as the Kuramoto model.
Furthermore, we show that the patterns of synchronization changes when one goes
to the gamma band. We attribute such a change to the change in the refractory
period of Izhikevich neurons which changes significantly with frequency.Comment: 7 figures, 1 tabl
Phase synchronization in a network of Izhikevich neurons with electrical synapses
<p>This code simulates a complex network of Izhikevich neurons
with electrical synapses. The dependence of phase synchronization order
parameter on coupling strength and also the network activity is calculated. The
differential equations of Izhikevich neuron’s dynamics are integrated using
Rung-Kutta method. Many features such as network topology can be changed
easily.</p
Izhikevich neurons with diffusive coupling.f90
<p>This code simulates a complex network of Izhikevich neurons
with electrical synapses. The dependence of phase synchronization order
parameter on coupling strength and also the network activity is calculated. The
differential equations of Izhikevich neuron’s dynamics are integrated using
Rung-Kutta method.</p
Optimal reinforcement learning near the edge of synchronization transition
Recent experimental and theoretical studies have indicated that the putative
criticality of cortical dynamics may corresponds to a synchronization phase
transition. The critical dynamics near such a critical point needs further
investigation specifically when compared to the critical behavior near the
standard absorbing state phase transition. Since the phenomena of learning and
self-organized criticality (SOC) at the edge of synchronization transition can
emerge jointly in spiking neural networks due to the presence of spike-timing
dependent plasticity (STDP), it is tempting to ask: What is the relationship
between synchronization and learning in neural networks? Further, does learning
benefit from SOC at the edge of synchronization transition? In this paper, we
intend to address these important issues. Accordingly, we construct a
biologically inspired model of an autonomous cognitive system which learns to
perform stimulus-response tasks. We train this system using a reinforcement
learning rule implemented through dopamine-modulated STDP. We find that the
system exhibits a continuous transition from synchronous to asynchronous neural
oscillations upon increasing the average axonal time delay. We characterize the
learning performance of the system and observe that it is optimized near the
synchronization transition. We also study neuronal avalanches in the system and
provide evidence that optimized learning is achieved in a slightly
supercritical state.Comment: 9 pages, 5 figures. To appear in Physical Review