A Phase-Locking Analysis of Neuronal Firing Rhythms with Transcranial Magneto-Acoustical Stimulation Based on the Hodgkin-Huxley Neuron Model

Yuan, Yi; Pang, Na; Chen, Yudong; Wang, Yi; Li, Xiaoli

doi:10.3389/fncom.2017.00001

Cited by 24 publications

(27 citation statements)

References 17 publications

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“…It should be noted that one possible issue with connectionist models is that several modelling choices, such as the format of the input and output representations [ 85 ] or the particular choice of learning algorithm [ 86 ], may have a crucial impact on the model's behaviour. One strength of the simulations presented in this article is that we made virtually no assumptions about the input/output representation format (e.g.…”

Section: Discussionmentioning

confidence: 99%

An emergentist perspective on the origin of number sense

Zorzi

Testolin

2018

Phil. Trans. R. Soc. B

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The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a ‘nativist’ stance on the origin of number sense. Here, we tackle this issue within the ‘emergentist’ perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense.This article is part of a discussion meeting issue ‘The origins of numerical abilities'.

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Section: Discussionmentioning

confidence: 99%

An emergentist perspective on the origin of number sense

Zorzi

Testolin

2018

Phil. Trans. R. Soc. B

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“…For excitatory connections, it has been shown that slow firing rate variations can also be achieved with overlapping clusters (Litwin-Kumar and Doiron 2012 ), mutually connected weight hub units with strong inward synapses (Setareh et al. 2017 ) or a range of other connectivity types (Doiron and Litwin-Kumar 2014 ). Although the exact implementation may vary, we believe that our proposal of local balancing of structured networks provides a very robust solution for metastability.…”

Section: Discussionmentioning

confidence: 99%

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

2017

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Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

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“…Analysis of within-muscle coherence at task failure using motor unit spike trains decoded from the electromyographic signals has been reported to increase in muscles of both the upper and lower limbs [ 7 , 8 ] due to an intensification of the cortical demand associated with maintaining task stability during fatigue. Also recently, Reyes and colleagues [ 58 ] have reported a diminished beta (15–30 Hz) band intermuscular and corticomuscular coherence contributions in two synergistic hand muscles during a spring compression task, suggesting a disengagement of the two muscles at the level of motor cortex when the force becomes highly unstable. These results are comparable with ours as also in our task there is an inherent instability induced progressively by the approaching of voluntary exhaustion.…”

Section: Discussionmentioning

confidence: 99%

Effect of Task Failure on Intermuscular Coherence Measures in Synergistic Muscles

Castronovo

Marchis

Schmid

et al. 2018

Applied Bionics and Biomechanics

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The term “task failure” describes the point when a person is not able to maintain the level of force required by a task. As task failure approaches, the corticospinal command to the muscles increases to maintain the required level of force in the face of a decreased mechanical efficacy. Nevertheless, most motor tasks require the synergistic recruitment of several muscles. How this recruitment is affected by approaching task failure is still not clear. The increase in the corticospinal drive could be due to an increase in synergistic recruitment or to overlapping commands sent to the muscles individually. Herein, we investigated these possibilities by combining intermuscular coherence and synergy analysis on signals recorded from three muscles of the quadriceps during dynamic leg extension tasks. We employed muscle synergy analysis to investigate changes in the coactivation of the muscles. Three different measures of coherence were used. Pooled coherence was used to estimate the command synchronous to all three muscles, pairwise coherence the command shared across muscle pairs and residual coherence the command peculiar to each couple of muscles. Our analysis highlights an overall decrease in synergistic command at task failure and an intensification of the contribution of the nonsynergistic shared command.

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A Phase-Locking Analysis of Neuronal Firing Rhythms with Transcranial Magneto-Acoustical Stimulation Based on the Hodgkin-Huxley Neuron Model

Cited by 24 publications

References 17 publications

An emergentist perspective on the origin of number sense

An emergentist perspective on the origin of number sense

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

Effect of Task Failure on Intermuscular Coherence Measures in Synergistic Muscles

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