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

Abstract

Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons.

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

“…In both cases, the synchronization index was kept within a low level ( SI < 0.05), indicating that asynchronous firings have been realized in this region. It has been shown by Kada et al (2016) that heterogeneity in the inhibitory-to-excitatory connections plays a key role in suppressing synchronized firing activities in a cortical network. The lognormal distribution, introduced to the inhibitory-to-excitatory connections, functioned efficiently to maintain the desynchronized firing dynamics.…”

“…It has been shown that lognormal distribution of inhibitory-to-excitatory connections plays an important role of suppressing synchronized neuronal firings especially in a high-frequency firing state (Kada et al, 2016). This setting is consistent with the physiological experiments reporting that inhibitory postsynaptic potentials (IPSPs) are indeed highly heterogeneous in the cortex (Miles and Wong, 1984; Holmgren et al, 2003; Chapeton et al, 2012).…”

“…Then, the CCG was drawn as a histogram of spike-time differences (bin size: 1 ms, time lag: ±20 ms) for all pairs of the 100 neurons. In accordance with our previous study (Kada et al, 2016), we define the synchronization index as a normalized height of the peak,…”

Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

“…An important research subject in neuroscience is to understand how cortical networks avoid or reach states of high synchronization (Kada et al, 2016). In normal activity, excitatory and inhibitory currents are well balanced (Tatti et al, 2018;Zhou and Yu, 2018), while in epileptic seizures, high synchronous behavior has been related to unbalanced current inputs (Drongelen et al, 2005;Avoli et al, 2016).…”

“…A number of studies reported that excitatory synapses facilitate neural synchronization (Borges et al, 2017;Breakspear et al, 2003), while inhibitory synapses have an opposite effect (Kada et al, 2016;Ostojic, 2014;Protachevicz et al, 2019). The time delay related to excitatory and inhibitory synapses influences the neural synchronization (Gu and Zhou, 2015;Protachevicz et al, 2020).…”