Statistical complexity is maximized close to criticality in cortical dynamics

Lotfi, Nastaran; Feliciano, Thaís; Aguiar, Leandro A. A.; Silva, Thais Priscila Lima; Carvalho, Tawan T. A.; Rosso, Osvaldo A.; Copelli, Mauro; Matias, Fernanda S.; Carelli, Pedro V.

doi:10.1103/physreve.103.012415

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…Timme et al ( 2016 ) have noted that complexity, quantified using the same metric applied in the present study (Marshall et al, 2016 ), is maximized at criticality and confirmed this finding both in experimental data from dissociated hippocampal networks and a critical model. Similar findings have also been reported by Lotfi et al ( 2021 ) for in vivo cortical spiking data using the Jensen disequilibrium as a measure of criticality. Although we did not see evidence of criticality in our networks, it is possible that the higher complexity value in the high-density networks, especially at later DIVs, point to the networks approaching a critical state.…”

Section: Discussionsupporting

confidence: 90%

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Heiney

Ramstad²,

Fiskum³

et al. 2022

Front. Neural Circuits

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Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches—functional connectivity and avalanche dynamics—in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

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Section: Discussionsupporting

confidence: 90%

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Heiney

Ramstad²,

Fiskum³

et al. 2022

Front. Neural Circuits

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show abstract

“…For each pattern depth, D, and coarse graining factor k , we calculated the pattern complexity, Cx , quantified as the number of different temporal sequences of length D (Ref. 39 ; see Methods). We found that Cx peaked at k for which χ sh = 2, for both ongoing and evoked activity independent of pattern depth (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Complexity analysis was performed as described in 39 exploring patterns of depths D in the range of 4 – 7. Complexity C was calculated on the population activity as a function of k and thresholding, identically to how epochs were computed to obtain scaling.…”

Section: Methodsmentioning

confidence: 99%

Parabolic avalanche scaling in the synchronization of cortical cell assemblies

Capek

Ribeiro

Kells³

et al. 2022

Preprint

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Neurons in cortex synchronize their spiking in response to local and distant inputs. These synchronized assemblies are fundamental to cortex function, yet basic dynamical aspects about their size and duration are largely unknown. Using 2-photon imaging of neurons in superficial cortex of awake mice, we show that synchronized assemblies organize as scale-invariant avalanches that quadratically grow with duration. This quadratic expansion was found only for correlated neurons and required temporal coarse graining to compensate for spatial subsampling when network dynamics are critical, as demonstrated in simulations. The corresponding time course of an inverted parabola with an exponent of 2 described avalanches of up to 5 s duration and maximized temporal complexity in the ongoing activity of prefrontal and somatosensory cortex and in visual responses of primary visual cortex. Our results identify a scale-invariant order in the synchronization of highly diverse cortical cell assemblies in the form of parabolic avalanches.

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“…For example Zhu et al [ 51 ] studied neuromorphic nanowire networks using TE and active information storage, finding that information theoretical values peak when the networks transition from a quiescent state to an active state, illustrating the relationship between information theory as a measure of computational capacity and criticality in an artificial system. Likewise, other studies have shown that biological brains may be poised at or near a critical state [ 52 ] where it has been argued the brain is at a point of “self-organised criticality”, a term introduced by Bak [ 53 ], and see the recent critical review by Girardi-Schappo [ 54 ]. Others have argued that this may be a widespread property of many other systems as well, see, for example, the recent article by Tadić and Melnik [ 55 ].…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Entropy, Economics, and Criticality

Harré

2022

Entropy

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Information theory is a well-established method for the study of many phenomena and more than 70 years after Claude Shannon first described it in A Mathematical Theory of Communication it has been extended well beyond Shannon’s initial vision. It is now an interdisciplinary tool that is used from ‘causal’ information flow to inferring complex computational processes and it is common to see it play an important role in fields as diverse as neuroscience, artificial intelligence, quantum mechanics, and astrophysics. In this article, I provide a selective review of a specific aspect of information theory that has received less attention than many of the others: as a tool for understanding, modelling, and detecting non-linear phenomena in finance and economics. Although some progress has been made in this area, it is still an under-developed area that I argue has considerable scope for further development.

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Statistical complexity is maximized close to criticality in cortical dynamics

Cited by 16 publications

References 25 publications

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Parabolic avalanche scaling in the synchronization of cortical cell assemblies

Entropy, Economics, and Criticality

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