Effective Suppression of Pathological Synchronization in Cortical Networks by Highly Heterogeneous Distribution of Inhibitory Connections

The CB1 cannabinoid receptors involvement in anti-epileptic effect of safranal on penicillin-induced epileptiform activity in rats

Neuroprotective effects for natural products are supported by several studies. In this regard, safranal, a constitute of saffron, has the potential to exert beneficial effects in neuro-logical disorders such as Parkinson's disease, epilepsy, stroke, multiple sclerosis and Alzheimer's disease. Here, we investigated the effect of safranal on penicillin-induced epileptiform activity. Also, the effects of intracerebroventricular (ICV) microinjection of AM251 as a CB1-cannabinoid receptors antagonist to clarify the possible mechanism of safranal were evaluated. Epileptiform activity was induced by intra-cortical administration of penicillin (300 IU, 1.50 μL) in urethane-anesthetized rats. Electrocorticographic recordings were used to analyze the frequency and amplitude of spike waves. Intraperitoneal injections of safranal at doses of 1.00 and 4.00 mg kg-1 significantly reduced both the number and amplitude of spike waves. The ICV microinjection of AM251 (0.50 μg 2.00 μL-1) significantly increased the frequency and amplitude of spike waves. In addition, the anti-epileptic effect induced by administration of safranal at a dose of 4.00 mg kg-1 was partially prevented by ICV microinjection of 0.50 μg 2.00 μL-1 of AM251. The results showed anti-epileptiform activities for safranal. Central CB1 cannabinergic receptors might be involved in the anti-epileptiform activity of safranal.

Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

Highly Heterogeneous Excitatory Connections Require Less Amount of Noise to Sustain Firing Activities in Cortical Networks

Cortical networks both in vivo and in vitro sustain asynchronous irregular firings with extremely low frequency. To realize such self-sustained activity in neural network models, balance between excitatory and inhibitory activities is known to be one of the keys. In addition, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., sparse but strong connections and dense weak connections, plays an essential role. The previous studies, however, have not thoroughly considered the cooperative dynamics between a network of such heterogeneous synaptic connections and intrinsic noise. The noise stimuli, representing inherent nature of the neuronal activities, e.g., variability of presynaptic discharges, should be also of significant importance for sustaining the irregular firings in cortical networks. Here, we numerically demonstrate that highly heterogeneous distribution, typically a lognormal type, of excitatory-to-excitatory connections, reduces the amount of noise required to sustain the network firing activities. In the sense that noise consumes an energy resource, the heterogeneous network receiving less amount of noise stimuli is considered to realize an efficient dynamics in cortex. A noise-driven network of bi-modally distributed synapses further shows that many weak and a few very strong synapses are the key feature of the synaptic heterogeneity, supporting the network firing activity.