Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

Davis, Zachary W.; Benigno, Gabriel; Fletterman, Charlee; Desbordes, Theo; Steward, Christopher; Sejnowski, Terrence J.; Reynolds, John H.; Muller, Lyle

doi:10.1038/s41467-021-26175-1

Cited by 41 publications

(53 citation statements)

References 106 publications

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“…However, we have found that it is actually independent of it, a comparable distribution of traveling speeds is found in a model with no propagation delays. This stands, for instance, in sharp contrast with a recent model of the propagation of spiking activity in visual cortex [43] which does not involve oscillatory activity but also relies on external inputs. In the present context, the observed speeds of the oscillatory waves of activity are set by the oscillatory frequency, and by the dephasings produced by the external inputs in the recorded area.…”

Section: Discussionmentioning

confidence: 57%

Beta oscillations and waves in motor cortex can be accounted for by the interplay of spatially-structured connectivity and fluctuating inputs

Lee

Ranft

Hakim

2022

Preprint

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The beta rhythm (13-30 Hz) is a prominent brain rhythm. Recordings in primates during instructed-delay reaching tasks have shown that different types of traveling waves of oscillatory activity are associated with episodes of beta oscillations in motor cortex during movement preparation. We propose here a simple model of motor cortex based on local excitatory-inhibitory neuronal populations coupled by longer range excitation, where additionally inputs to the motor cortex from other neural structures are represented by stochastic inputs on the different model populations. We show that the model accurately reproduces the statistics of recording data when these external inputs are correlated on a short time scale (25 ms) and have two different components, one that targets the motor cortex locally and another one that targets it in a global and synchronized way. The model reproduces the distribution of beta burst durations, the proportion of the different observed wave types, and wave speeds, which we show not to be linked to axonal propagation speed. When the long-range connectivity is anisotropic, traveling waves are found to preferentially propagate along the axis where connectivity decays the fastest. Different from previously proposed mechanistic explanations, the model suggests that traveling waves in motor cortex are the reflection of the dephasing by external inputs, putatively of thalamic origin, of an os- cillatory activity that would otherwise be spatially synchronized by recurrent connectivity.

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Section: Discussionmentioning

confidence: 57%

Beta oscillations and waves in motor cortex can be accounted for by the interplay of spatially-structured connectivity and fluctuating inputs

Lee

Ranft

Hakim

2022

Preprint

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“…To additionally verify performance of the SNR algorithm, we implemented this approach over 1 s recordings of a 90 by 90 array of local field potentials (LFPs) generated by a spiking network model of cortical activity in the awake state ( Davis et al, 2021 ), which does not contain the thalamic reticular loops and thalamocortical projections needed to generate sleep spindles. This model, composed of several million neurons with biologically realistic synaptic connectivity (and several thousand synapses per cell), creates realistic ongoing activity patterns consistent with the well-studied asynchronous-irregular state, corresponding to activity in the cortex of awake animals ( Destexhe, 2009 ).…”

Section: Methodsmentioning

confidence: 99%

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Mofrad

Gilmore

Koller

et al. 2022

eLife

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Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

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“…Synaptic signals arrive simultaneously but travel varying distances and therefore must have different propagation speeds [101]. Cortical recordings further reveal that spontaneous traveling waves are a general topological property of large-scale neural behavior [102]. One project studies synchrony in axon propagation speeds from data recorded at multiple spatial scales [103].…”

Section: Neural Field Theoriesmentioning

confidence: 99%

Quantum Neurobiology

2022

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Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

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Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

Cited by 41 publications

References 106 publications

Beta oscillations and waves in motor cortex can be accounted for by the interplay of spatially-structured connectivity and fluctuating inputs

Beta oscillations and waves in motor cortex can be accounted for by the interplay of spatially-structured connectivity and fluctuating inputs

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Quantum Neurobiology

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