Contextual regularity and complexity of neuronal activity: From stand‐alone cultures to task‐performing animals

Ayali, Amir; Fuchs, Einat; Zilberstein, Yael; Robinson, Anat; Shefi, Orit; Hulata, Eyal; Baruchi, Itay; Ben‐Jacob, Eshel

doi:10.1002/cplx.20046

Cited by 20 publications

(20 citation statements)

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“…From a dynamic systems perspective, this operating range affords the individual neuron as a functional independent unit or autonomous agents, as well as the neuronal cluster after its formation and the networks as a whole, both flexibility and stability. This was shown to be a fundamental characteristic in various studies of neuronal circuits dynamics (LinkenkaerHansen et al, 2001;Ayali et al, 2004;Bertschinger and Natschlager, 2004;Hulata et al, 2004;Haldeman and Beggs, 2005) giving the nervous system the freedom to act locally while retaining global coordination.…”

Section: Discussionmentioning

confidence: 94%

“…At a first glance, the sequence is a realization of a probability distribution function. Since this distribution function of the interevent-intervals usually has longtail decay (Segev et al, 2002;Ayali et al, 2004; shown also in this article), there is a basic stochastic temporal structure of the intervals. The relation of our measures and this distribution function has been explored in previous studies (Hulata et al, , 2005.…”

Section: From Temporal Structure Complexity To Functional Complexitymentioning

confidence: 96%

“…They exhibit nonarbitrary temporal ordering characterized by scale-free statistics and long-range correlations. These features, which have been identified in cultured networks composed of mammalian cortical neurons (Segev et al, 2002Hulata et al, 2004;Raichman et al, 2006;Wagenaar et al, 2006a;Baruchi and Ben Jacob, 2007), were recently also described in networks obtained from a relatively simple invertebrate motor ganglion-the locust frontal ganglion (Ayali et al, 2004).…”

Section: Introductionmentioning

confidence: 91%

“…long-tail distributions ( Fig. 1, right) or evolving probability functions (Segev et al, 2002;Ayali et al, 2004).…”

Section: Temporal Structure Complexity and Regularity Of Recorded Seqmentioning

confidence: 98%

“…Furthermore, comparative investigation of a distinct neuronal population operating under different levels of structural and functional constraints have also been conducted (Ayali et al, 2004). The temporal organization of neuronal activity was characterized using an analytical method developed to characterize the dynamic activity of biological time series (Hulata et al, , 2005Fuchs et al, 2006;Ayali et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Coemergence of regularity and complexity during neural network development

Fuchs

Ayali

Robinson

et al. 2007

Developmental Neurobiology

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With the growing recognition that rhythmic and oscillatory patterns are widespread in the brain and play important roles in all aspects of the function of our nervous system, there has been a resurgence of interest in neuronal synchronized bursting activity. Here, we were interested in understanding the development of synchronized bursts as information-bearing neuronal activity patterns. For that, we have monitored the morphological organization and spontaneous activity of neuronal networks cultured on multielectrode-arrays during their self-executed evolvement from a mixture of dissociated cells into an active network. Complex collective network electrical activity evolved from sporadic firing patterns of the single neurons. On the system (network) level, the activity was marked by bursting events with interneuronal synchronization and nonarbitrary temporal ordering. We quantified these individual-to-collective activity transitions using newly-developed system level quantitative measures of time series regularity and complexity. We found that individual neuronal activity before synchronization was characterized by high regularity and low complexity. During neuronal wiring, there was a transient period of reorganization marked by low regularity, which then leads to coemergence of elevated regularity and functional (nonstochastic) complexity. We further investigated the morphology-activity interplay by modeling artificial neuronal networks with different topological organizations and connectivity schemes. The simulations support our experimental results by showing increased levels of complexity of neuronal activity patterns when neurons are wired up and organized in clusters (similar to mature real networks), as well as network-level activity regulation once collective activity forms.

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Section: Discussionmentioning

confidence: 94%