The Asynchronous State in Cortical Circuits

Renart, Alfonso; Rocha, Jaime de la; Barthó, Péter; Hollender, Liad; Parga, Néstor; Reyes, Alex D.; Harris, Kenneth D.

doi:10.1126/science.1179850

Cited by 1,059 publications

(1,890 citation statements)

References 28 publications

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“…This protocol produced exquisite data allowing to show that even nearby neurons, generally thought to be strongly connected and to receive a substantial part of common input showed a very low level of correlation. Similar decorrelation results were reported in the rodent neocortex [55] with the same level of accuracy.…”

Section: Iii2 Decorrelation In Experimental Data and Modelssupporting

confidence: 88%

Power-law statistics and universal scaling in the absence of criticality

2017

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Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality. In these regimes, statistical physics theory of large interacting systems predict a regime where the nodes have independent and identically distributed dynamics. We thus investigated the statistics of a system in which units are replaced by independent stochastic surrogates, and found the same power-law statistics, indicating that these are not sufficient to establish criticality. We rather suggest that these are universal features of large-scale networks when considered macroscopically. These results put caution on the interpretation of scaling laws found in nature.

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Section: Iii2 Decorrelation In Experimental Data and Modelssupporting

confidence: 88%

Power-law statistics and universal scaling in the absence of criticality

2017

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“…We apply this strategy separately to brain states of wakefulness (Awake) and Slow-Wave Sleep (SWS). In contrast to the former, the latter is known to be characterized by transients of high activity that modulate the whole population behavior [16]. Consistently, we found that the Ising model does not account for such global oscillations of the network dynamics.…”

supporting

confidence: 74%

“…Conclusions: (i) The pairwise Ising model offers a good description of the neural network activity observed during wakefulness. (ii) By contrast, taking into account pairwise correlations is not sufficient to describe the statistics of the ensemble activity during SWS, where (iii) alternating periods of high and low network activity introduce high order correlations among neurons, especially for inhibitory cells [16]. (iv) This suggests that neural interactions during wakefulness are more local and short-range, whereas (v) these in SWS are partially modulated by internally-generated activity, synchronizing neural activity across long distances [4,16,19].…”

Section: Inference Algorithmmentioning

confidence: 99%

Pairwise Ising Model Analysis of Human Cortical Neuron Recordings

Nghiem

Marre

Destexhe

2017

Lecture Notes in Computer Science

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Abstract. During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari, Phys. Rev. E (2016) [1]. We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data. Keywords: Ising model, maximum entropy principle, natural gradient, human temporal cortex, multielectrode array recording, brain states Advances in experimental techniques have recently enabled the recording of the activity of tens to hundreds of neurons simultaneously [2] and has spurred the interest in modeling their collective behavior [3,4,5,6,7,8,9]. To this purpose, the pairwise Ising model has been introduced as the maximum entropy (most generic [10]) model able to reproduce the first and second empirical moments of the recorded neurons. Moreover it has already been applied to different brain regions in different animals [3,5,6,9] and shown to work efficiently [11] .The inference problem for a pairwise Ising model is a computationally challenging task [12], that requires devoted algorithms [13,14,15]. Recently, we proposed a data-driven algorithm and applied it on rat retinal recordings [1]. In the present work we first review the algorithm structure and then describe our successful application to a recording in the human temporal cortex [4].We use the inferred Ising model to test if a model that reproduces empirical pairwise covariances without assuming any other additional information, also predicts empirical higher-order statistics. We apply this strategy separately to brain states of wakefulness (Awake) and Slow-Wave Sleep (SWS). In contrast to arXiv:1710.09929v1 [q-bio.NC]

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“…Theoretical studies have emphasized that, in a sparsely connected network, the seemingly irregular firing of cells could be the consequence of the balance between excitation and inhibition (27)(28)(29). Similarly, intracellular recordings have revealed a balance between excitatory and inhibitory conductance both in vitro (30) and in vivo (31), they and even shown a possible excess of inhibition in vivo (32).…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Peyrache

Dehghani

Eskandar

et al. 2012

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

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Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.spontaneous activity | ensemble recordings | single unit | functional dynamics F rom columnar microcircuits (1-3) to higher-order neuronal functional units, neocortical dynamics are characterized by a large range of spatial and temporal scales (4, 5). Recent technical improvements have allowed the nature of these dynamics in the human brain to be directly explored: Single-neuron activity in conjunction with local field potentials (LFPs) can be detected from the cerebral cortex and hippocampus in the course of intense monitoring of brain activity before surgical treatment of epileptic foci (6). Modern electrode systems provide the possibility of extracellular recordings of neuronal ensembles by using either microwires (7) or high-density microelectrode arrays (8,9). Prior efforts have demonstrated excellent recordings of single-neuron activity in human cerebral cortex (10-12).Separation of units between "regular-spiking" (RS) and "fastspiking" (FS) neurons, presumably excitatory (pyramidal) and inhibitory (interneuron) cells, respectively, is commonly practiced in animal experiments. In the neocortex of various mammalian species, RS and FS cells can be reliably separated based on spike waveform, duration, and firing rates (13,14). Similar criteria were also used to successfully separate units into putative pyramidal (Pyr) cells and inhibitory interneurons (Int) in human hippocampus...

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The Asynchronous State in Cortical Circuits

Cited by 1,059 publications

References 28 publications

Power-law statistics and universal scaling in the absence of criticality

Power-law statistics and universal scaling in the absence of criticality

Pairwise Ising Model Analysis of Human Cortical Neuron Recordings

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

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