Leader neurons in population bursts of 2D living neural networks

Eckmann, Jean‐Pierre; Jacobi, Shimshon; Marom, Shimon; Moses, Elisha; Zbinden, Cyrille

doi:10.1088/1367-2630/10/1/015011

Cited by 75 publications

(117 citation statements)

References 25 publications

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“…Our picture of the bursting network activity at elevated calcium is based on the idea that without blockers, a background of spontaneous synaptic activity exists and makes the neurons slightly more excitable. We have shown previously that under these conditions of strong connectivity, firing of a small fraction of the neurons, presumably those neurons that are more sensitive to inputs and lead the activity, suffices to ignite the rest of the network (28)(29)(30). An additional effect of increased network synchronization with elevated [Ca 2+ ] o was recently reported in cortical slices (31), but through differential sensitivity of excitatory and inhibitory synapses to calcium.…”

Section: Discussionmentioning

confidence: 87%

Network synchronization in hippocampal neurons

Penn

Segal

Moses

2016

Proc. Natl. Acad. Sci. U.S.A.

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129

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Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zerolag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits.neuron | network | oscillator | synchrony | persistent Na current P eriodicity emerges as a key physiological characteristic at all levels of neuronal activity, from the dynamics of neurons at subthreshold potentials (1, 2), through rhythmic neuronal ensembles within local networks, and all of the way up to global oscillations measured by electroencephalography (EEG) (3). The range of observed frequencies is surprisingly wide, from the millisecond range typical for interspike intervals all of the way to several seconds in the case of slow EEG. Over the years, accumulating evidence and theory have attributed different mechanisms for the origin of each measured periodic activity. Within local networks, the role of known collective mechanisms for periodicity, such as the balance between excitatory and inhibitory neurons and recurrent network architecture (4-6), is often contrasted with single neuron contributions, for example, the role of pacemaker neurons in oscillatory network dynamics (7). Although the physiological properties of single neurons are diverse and well documented (2, 8), their role in emergent network oscillations was predicted theoretically (9), but has not been observed experimentally. This contribution may involve a subtle interplay between intrinsic excitability and network connectivity (10, 11).The connectivity and excitability together determi...

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Section: Discussionmentioning

confidence: 87%

Network synchronization in hippocampal neurons

Penn

Segal

Moses

2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

129

View full text Add to dashboard Cite

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“…Experimental studies on cortical networks provide ample evidence for the existence of a special set of self-firing neurons that precede, or even ignite, network bursts as described in the "Introduction," e.g., privileged neurons (Eytan and Marom 2006), endogenously active neurons (Latham et al 2000), and leader neurons (Eckmann et al 2008). Eckmann et al suggested three possible scenarios for how the leader neurons could be excited (Eckmann et al 2008).…”

Section: Simulated Datamentioning

confidence: 99%

“…Eckmann et al suggested three possible scenarios for how the leader neurons could be excited (Eckmann et al 2008). In the first two scenarios, one or several leader neurons are self-excited or stimulated by low-level noise (in parallel) while exciting a region around them and randomly igniting network bursts.…”

Section: Simulated Datamentioning

confidence: 99%

Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture

et al. 2011

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Rhythmic bursting is the most striking behavior of cultured cortical networks and may start in the second week after plating. In this study, we focus on the intervals between spontaneously occurring bursts, and compare experimentally recorded values with model simulations. In the models, we use standard neurons and synapses, with physiologically plausible parameters taken from literature. All networks had a random recurrent architecture with sparsely connected neurons. The number of neurons varied between 500 and 5,000. We find that network models with homogeneous synaptic strengths produce asynchronous spiking or stable regular bursts. The latter, however, are in a range not seen in recordings. By increasing the synaptic strength in a (randomly chosen) subset of neurons, our simulations show interburst intervals (IBIs) that agree better with in vitro experiments. In this regime, called weakly synchronized, the models produce irregular network bursts, which are initiated by neurons with relatively stronger synapses. In some noise-driven networks, a subthreshold, deterministic, input is applied to neurons with strong synapses, to mimic pacemaker network drive. We show that models with such "intrinsically active neurons" (pacemaker-driven models) tend to generate IBIs that are determined by the frequency of the fastest pacemaker and do not resemble experimental data. Alternatively, noise-driven models yield realistic IBIs. Generally, we found that large-scale noise-driven neuronal network models required synaptic strengths with a bimodal distribution to reproduce the experimentally observed IBI range. Our results imply that the results obtained from small network models cannot simply be extrapolated to models of more realistic

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“…In Ref. [14] the neuron which fires first is considered as leader. Further, a leader can be assigned based on its degree in a network [15].…”

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confidence: 99%

“…Leaders naturally arise in real-world networks such as in social networks [27], neural networks [14], protein translation regulatory networks [28]. In social network, a leader may possess one of the characteristics of power, experience, fame or wealth, while in biological networks, such as in neural and the protein translation regulatory networks, a leader may be one which initiate certain processes [14,28].…”

mentioning

confidence: 99%

Impact of a leader on cluster synchronization

et al. 2015

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We study the mechanisms of frequency synchronized cluster formation in coupled non-identical oscillators and investigate the impact of presence of a leader on the cluster synchronization. We find that the introduction of a leader, node having large parameter mismatch, induces a profound change in the cluster pattern as well as in the mechanism of the cluster formation. The emergence of a leader generates a transition from the driven to the mixed cluster state. The frequency mismatch turns out to be responsible for this transition. Additionally, for a chaotic evolution, the driven mechanism stands as a primary mechanism for the cluster formation, whereas for a periodic evolution the self-organization mechanism becomes equally responsible. The interaction between individual units of a system leads to many emerging behaviors, among which synchronization is one of the most fascinating phenomenon which has been gaining tremendous attention since the first experimental demonstration of the phenomena by Christian Huygens [1]. The unexpected sway and twist of the Millennium bridge, synchronization of the neurons in the brain and synchronous fire flies [2][3][4] are few examples of the synchronization in the real world systems. Synchronization is defined as an emergence of some relation between the functional of two processes due to interaction [3]. Earlier studies on coupled non-identical oscillators have shown that the exact synchronization is hard to achieve when there is a parameter mismatch in the local dynamics [3], rather they exhibit phase or generalized synchronization [5,6]. Additionally, there exists a nontrivial transition to the global phase synchronization in a population of globally coupled chaotic non-identical oscillators [7]. Moreover, cluster pattern and frequency of the nodes in a cluster have been shown to be controlled by local external forcing as well as by changing the network architecture [8]. Furthermore, the coupled oscillators with heterogeneous coupling have been reported to exhibit synchronization triggered by the oscillators having strong couplings, further facilitating the synchronization among the nodes having weak coupling [9].We present results of cluster synchronization due to its importance and occurrence in various real world systems represented in terms of interacting units [10]. We study mechanisms of formation of frequency synchronized clusters in coupled non-identical oscillators and investigate the influence of a leader on the dynamical evolution of other nodes. One possible way of defining a leader is to make the natural frequency of a node much higher than * sarika@iiti.ac.in that of other nodes in the network [11,12]. This is one of the traditional way to define a leader in the coupled dynamics on network models. Some other ways of defining leaders are those which depend upon the application and motivation of the problem, e. g., Ref.[13] considers a leader which exchanges information with its neighbors as well as has access to its own state. In Ref.[14] the neuron which fires...

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Leader neurons in population bursts of 2D living neural networks

Cited by 75 publications

References 25 publications

Network synchronization in hippocampal neurons

Network synchronization in hippocampal neurons

Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture

Impact of a leader on cluster synchronization

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