Innate Synchronous Oscillations in Freely-Organized Small Neuronal Circuits

Abstract

BackgroundInformation processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks?Methodology/Principal FindingsWe used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry.Conclusions/SignificanceThe nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo.

Engineered Neuronal Circuits: A New Platform for Studying the Role of Modular Topology

“…It was shown that networks ranging from 50 to 10 6 cells can sustain network bursting with statistical features which are almost size independent (Segev et al, 2002). A similar result was reported for isolated clusters in which NB statistics did not dramatically change for networks larger than 100 cells (Shein Idelson et al, 2010; Figures 3A,B). This suggests the existence of regulatory mechanisms that modify the network morphology to sustain specific activity patterns.…”

Emergence of Bursting Activity in Connected Neuronal Sub-Populations

“…It has been already observed for isolated clusters (i.e. ‘finite-size’ networks) that the frequency of synchronous network events increased with circuit size [6], [43]. An interesting prosecution of our work would consist of employing a higher number of interacting modules in order to analyze the onset of more complex dynamics, and the emergence of different hierarchical structures.…”

Multichannel activity propagation across an engineered axon network

“…These modular networks have been useful in experimentally identifying gating mechanisms that control the spread of neural activity among network nodes (Yvon et al, 2005; Feinerman et al, 2008; Levy et al, 2012; Pan et al, 2015), a phenomenon that typically has been studied through computational modeling (Shinozaki et al, 2007; Vogels and Abbott, 2009). In addition, modular networks have been used to study the initiation and maintenance of asymmetries in communication between network nodes, such as master-slave relationships (Baruchi et al, 2008; Shein Idelson et al, 2010; Kanagasabapathi et al, 2012). …”

“…Prior studies have examined the spread of spontaneous network bursts (Baruchi et al, 2008; Shein Idelson et al, 2010; Kanagasabapathi et al, 2011), but there is a relative dearth of data on the transmission of asynchronous firing activity (Reyes, 2003). Here, using multichannel optical stimulation and multisite electrical recordings (Figure 1A), we investigated the representation of stimulation-based inputs in cortical networks, factors modifying these representations during transmission to a downstream node, and the degree to which the downstream node can discriminate among different input patterns.…”