Phase-resetting as a tool of information transmission

Canavier, Carmen C.

doi:10.1016/j.conb.2014.12.003

Cited by 135 publications

(117 citation statements)

References 62 publications

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“…Grossly, both of these theories consider the role of phase synchronization in gating communication channels across brain circuits. Specific frequency bands have been implicated in particular aspects of neural state: attention indexing by alpha in auditory, visual and somatosensory areas [14,15,61,62] ; stimulus timing by aligned delta-theta [63,64] ; delta with nested gamma by encoding of elements of speech signals [27,65,66] . The present study demonstrates that oscillations are ubiquitous across bands and thus would provide fertile ground for their use in these contexts.…”

Section: Functional Implicationsmentioning

confidence: 99%

Taxonomy of neural oscillation events in primate auditory cortex

Neymotin

Tal

Barczak

et al. 2020

Preprint

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Electrophysiological oscillations in neocortex have been shown to occur as multi-cycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We used two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing event related potentials, we used a wavelet transform based method to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, gamma, high gamma. Oscillation events of 1-44 cycles were present in at least one frequency band in 90% of the recordings, consistent across human and non-human primate. Individual oscillation events were characterized by non-constant frequency and amplitude. This result naturally contrasts with prior studies which assumed such constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intra -event rhythmicity, there was also evidence of inter -event rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano Factor measures differed significantly from a Poisson distribution assumption. Overall, our study demonstrates that rhythmic oscillation events dominate auditory cortical dynamics.

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Section: Functional Implicationsmentioning

confidence: 99%

Taxonomy of neural oscillation events in primate auditory cortex

Neymotin

Tal

Barczak

et al. 2020

Preprint

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“…but one could expect that the change in the onset of the next burst will also depend on 100 when during the bursting cycle the stimulation is provided. Phase response functions 101 have been studied in a mathematical model of a micro-circuit composed of connected 102 populations of excitatory and inhibitory neurons [27]. When such micro-circuit receives 103 an input, the phase of the oscillations it produces either advances or reduces depending 104 on when within the cycle the input is provided [27].…”

Section: Author Summarymentioning

confidence: 99%

“…Phase response functions 101 have been studied in a mathematical model of a micro-circuit composed of connected 102 populations of excitatory and inhibitory neurons [27]. When such micro-circuit receives 103 an input, the phase of the oscillations it produces either advances or reduces depending 104 on when within the cycle the input is provided [27]. 105 Second, we define the average activity of neural oscillators as a superposition of 106 cosine functions, i.e.…”

Section: Author Summarymentioning

confidence: 99%

Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

Weerasinghe

Duchet

Cagnan

et al. 2018

Preprint

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Deep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson's disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New 'closed-loop' approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects.The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when Introduction 1 Symptoms of several neurological disorders are thought to arise from overly synchronous 2 activity within neural populations. The severity of clinical impairment in Parkinson's 3 disease (PD) is known to be correlated with an increase in the beta (13-35 Hz) 4 oscillations in the local field potential (LFP) and in the activity of individual neurons in 5October 16, 2018 2/34 the basal ganglia [1, 2]. The tremor symptoms associated with essential tremor (ET) are 6 thought to arise from synchronous activity in a network of brain areas including the 7 thalamus [3]. In both PD and ET the muscle activity driving the tremor is coherent 8 with local field potentials in the thalamus [4] and bursts of spikes produced by 9 individual thalamic neurons [5]. 10 Deep brain stimulation (DBS) is a well-established treatment option for PD and ET 11 which involves delivering stimulation via electrodes implanted into the brain. The 12 present generation of the technology involves manually tuning the parameters of 13 stimulation, such as the pulse width, frequency and intensity in an attempt to achieve 14 the best treatment. In particular, the choice of frequency is known to be crucial for 15 efficacy, and high frequency DBS (120-180 Hz) has been found to be effective for PD 16 and ET patients [6]. High frequency DBS is known to suppress the pathological 17 oscillations occurring in PD [7], but despite its long history, the underlying mechanisms 18 causing this suppression remain unclear, and several distinct theories have been 19 proposed [8-10]. One influential theory suggests that high frequency DBS activates 20 target neurons to such an extent that their synaptic transmission becomes saturated 21 and they are no longer able to transmit pathological oscillations [11]. Since high 22 frequency DBS can cause side-effects such as speech-impairments [12] and gambling 23 tendencies [13], improvements to this treatment approach are desi...

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“…where the de-synchronisation and phase-angle time-series of alpha signals information processing (Hanslmayr, et al, 2012) and conveys information content (Canavier, 2015), respectively. Phase resets are thought to occur when there is a change in the scene during the presentation of dynamic stimuli (Michelmann, et al, 2016).…”

Section: Figure S2 -Neo-cortex Topologymentioning

confidence: 99%

Modelling the Replay of Dynamic Memories from Cortical Alpha Oscillations with the Sync-Fire / deSync Model

Parish

Michelmann

Hanslmayr

et al. 2020

Preprint

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We here propose a neural network model to explore how neural oscillations might regulate the replay of memory traces. We simulate the encoding and retrieval of a series of events, where temporal sequences are uniquely identifiable by analysing population activity, as several recent EEG/MEG studies have shown. Our model comprises three parts, each considering distinct hypotheses. A cortical region actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding region converts each event into a discrete index, enabling repetitions through a sparse encoding of events. We also instantiate a temporal region, where an oscillatory "ticking-clock" made up of hierarchical synfire chains discretely indexes a moment in time. By encoding the absolute timing between events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are replayed in the brain.

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Phase-resetting as a tool of information transmission

Cited by 135 publications

References 62 publications

Taxonomy of neural oscillation events in primate auditory cortex

Taxonomy of neural oscillation events in primate auditory cortex

Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

Modelling the Replay of Dynamic Memories from Cortical Alpha Oscillations with the Sync-Fire / deSync Model

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