Locally embedded presages of global network bursts

Tajima, Satohiro; Mita, Takeshi; Bakkum, Douglas J.; Takahashi, Hirokazu; Toyoizumi, Taro

doi:10.1073/pnas.1705981114

Cited by 18 publications

(18 citation statements)

References 58 publications

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“…In fact, this is what we found with our model: a burst becomes practically unforeseeable going some 100–200 ms in the past. This result is in contrast with the impressive findings reported in 32 , in which the authors, on recordings similar to the ones used in this work, through a new model-free method based on state-space reconstruction, were able to predict the occurrence of a burst even 1 s in advance. We applied the state-reconstruction method on our data with negative results, in agreement with what we found with our model.…”

Section: Discussioncontrasting

confidence: 99%

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Capone

Gigante

Giudice

2018

Sci Rep

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Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neuron’s information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations.

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Section: Discussioncontrasting

confidence: 99%

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Capone

Gigante

Giudice

2018

Sci Rep

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“…Finalizing the description of the qualitative picture, it is worth noting that the suggested concepts and the schematic of recruitment process during initiation of a PS are generally consistent with previous findings [35,41,42,48,73,77,[179][180][181][182]. In particular, the concept of 'trigger' neurons is closely related to the neurons of 'nacelles' [35], 'early-to-fire' neurons [42], 'major burst leaders' [48], 'leader' neurons [180][181][182], and 'critical' neurons [73].…”

Section: Discussionsupporting

confidence: 80%

“…However, despite the fact that the origin, stability, and the functional role of the regime of spontaneous, irregularly repetitive PSs have been intensively studied [42,51,52,55,[58][59][60][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78], some key aspects, including a mechanistic explanation of PSs initiation and the nucleation effect, are yet to be unraveled. Fortunately, there are two fostering factors.…”

Section: Introductionmentioning

confidence: 99%

Capturing remote activation of epilepsy source?

Paraskevov

Zendrikov

2018

Preprint

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Spontaneous focal synchronization of collective spiking followed by induced traveling waves can occur in the cortical sheet and in cultured planar neuronal networks. In the first case, it is well-known focal epilepsy leading to a seizure and, in the second, this synchronization originates from one of a few steady nucleation sites resulting in a so-called population spike. Assuming functional similarity between the nucleation sites and non-lesional epileptic foci, the major unsolved problem in both cases is that it is unclear whether activation of the focus originates internally (i.e., autonomously relative to interaction with surrounding neuronal tissue) or externally. The 'internal' scenario implies that the focus spatially contains some pacemakers. In turn, several experimental findings indicate a complex spatially non-local activation of epileptic focus. Here, we suggest a generative mechanistic model of planar neuronal network, where the spatial configuration of pacemaker neurons is artificially engineered in order to resolve the above mentioned problem: all pacemakers are placed within a circular central spot. Leaving the global dynamic regime unaffected, this crucially helps to clarify the activation process, visualizing of which is hindered in the natural spatially-uniform configuration. We show in simulations that the nucleation sites (i) can emerge in spatial regions, where pacemakers are completely absent and (ii) can be activated even without direct links from pacemakers. These results demonstrate the principle possibility of external, or remote, activation of a focal source of epileptic activity in the brain. The suggested deterministic model provides the means to study this network phenomenon systematically and reproducibly.

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“…proportional to the synaptic interaction between the two neurons [31]. Here, the goal of applying the method of cross-mapping is to study interactions that are intrinsic to the V1 network, excluding the influence of the shared input on the interaction, an analysis that is helpful for teasing apart the sources of the choice signal.…”

Section: Activity Of Bursty Neurons and Of Neurons In The Superficialmentioning

confidence: 99%

Learning from invariants predicts upcoming behavioral choice from spiking activity in monkey V1

Koren

Andrei

et al. 2020

Preprint

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Highlights• Learning from both stimuli and behavioral choice generalizes to the representation of the choice alone.• With generalization of learning, the choice signal can be read-out from parallel spike trains in V1.• Correct attribution to binary coding pools and correct spike timing are necessary for the read-out.• Bursty neurons convey more information than non-bursty neurons. Discrimination is the strongest in the superficial layer of the cortex. AbstractRepresentation of stimuli by neural ensembles and the correlation of neural activity with the behavioral choice are, in principle, two different computational problems, however, it is only the intersection between the two that is relevant for animal's behavior. Here, we propose a decoding model that learns its decoding weights in the presence of the information on both the stimulus class and the future behavioral choice. We then test the model on trials that only differ in the choice and show that the choice can be read-out from the activity of populations in V1. The read-out model is a linear weighted sum of spikes, decoding the behavioral choice in single trials and without temporal averaging. The generalization of learning suggests that the representation of the stimulus class and of the behavioral choice have a non-zero intersection. In addition, we show how the spike timing is required for discrimination, that bursty neurons carry more information than non-bursty neurons and that neurons in the superficial layer are the most important for discrimination.

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Locally embedded presages of global network bursts

Cited by 18 publications

References 58 publications

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Capturing remote activation of epilepsy source?

Learning from invariants predicts upcoming behavioral choice from spiking activity in monkey V1

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