The Refractory Period Matters: Unifying Mechanisms of Macroscopic Brain Waves

Weistuch, Corey; Mujica‐Parodi, Lilianne R.; Dill, Ken A.

doi:10.1162/neco_a_01371

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…Indeed, a biological neuron has finite refractory period after firing, during which a second firing action is much more difficult to be initiated and after which the resting state of neuron is spontaneously restored. This restricts neuronal firing patterns [35], benefits neuronal reliability [36] and contributes to the complex brain oscillations [37]. However, capturing this subtle but useful neuronal feature compactly is traditionally difficult [13,14,38] and in single memristive neurons this character is rarely reported [17,23].…”

Section: Resultsmentioning

confidence: 99%

A complementary resistive switching neuron

Wang

2022

Nanotechnology

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The complementary resistive switching (CRS) memristor has originally been proposed for use as the storage element or artificial synapse in large-scale crossbar array with the capability of solving the sneak path problem, but its usage has mainly been hampered by the inherent destructiveness of the read operation (switching ‘1’ state to ‘ON’ or ‘0’ state). Taking a different perspective on this ‘undesired’ property, we here report on the inherent behavioral similarity between the CRS memristor and a leaky integrate-and-fire (LIF) neuron which is another basic neural computing element, in addition to synapse. In particular, the mechanism behind the undesired read destructiveness for storage element and artificial synapse can be exploited to naturally realize the LIF and the ensuing spontaneous repolarization processes, followed by a refractory period. By means of this biological similarity, we demonstrate a Pt/Ta2O5−x/TaOy/Ta CRS memristor that can exhibit these neuronal behaviors and perform various fundamental neuronal operations, including additive/subtractive operations and coincidence detection. These results suggest that the CRS neuron, with its bio-interpretability, is a useful addition to the family of memristive neurons.

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Section: Resultsmentioning

confidence: 99%

A complementary resistive switching neuron

Wang

2022

Nanotechnology

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“…Perhaps the most useful extension of this work would be the analysis of bifurcations when there is time-delayed coupling between regions. There has already been work [24, 32] demonstrating that time-delays and the refractory period in neural mass models can model the propagation of electrical waves throughout the brain. There has also been prior work on how the introduction of time delays in chaotic systems [18], including neural mass models [33], can introduce novel bifurcation points.…”

Section: Discussionmentioning

confidence: 99%

Ion Gradient-driven Bifurcations of a Multi-Scale Neuronal Model

Chesebro

Mujica‐Parodi

Weistuch

2022

Preprint

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Metabolic limitations within the brain frequently arise in the context of aging and disease. As the largest consumers of energy within the brain, ion pumps that maintain the neuronal membrane potential are the most affected when energy supply becomes limited. To characterize the effects of such limitations, we analyze the ion gradients present in the Larter-Breakspear neural mass model. We show the existence and locations of Neimark-Sacker and period-doubling bifurcations in the sodium, calcium, and potassium reversal potentials, and demonstrate that these bifurcations form physiologically relevant bounds of ion gradient variability. Within these bounds, we show how depolarization of the gradients will cause decreased neural activity. We also show that the depolarization of ion gradients decreases inter-regional coherence, causing a shift in the critical point at which the coupling occurs and thereby inducing loss of synchrony between regions. In this way, we show that the Larter-Breakspear model captures ion gradient variability present at the microscale level and propagates these changes to the macroscale effects congruent with those observed in human neuroimaging studies.

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“…First, as opposed to other modeling approaches, Max Ent makes minimal assumptions that are not warranted by the data itself [12]. Second, Max Ent is a widely and successfully utilized modeling framework for complex biological systems [13][14][15][16][17][18]. We provide theory and practical demonstrations of our new approach in the present work.…”

Section: Introductionmentioning

confidence: 99%

The Maximum Entropy Principle For Compositional Data

Weistuch

Zhu

Deasy

et al. 2022

Preprint

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In this work, we provide a general method for inferring the stochastic behavior of compositional systems. Our approach is guided by the principle of maximum entropy, a data-driven modeling technique. In particular, we show that our method can accurately capture stochastic, inter-species relationships with minimal model parameters. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.Author summaryCompositional systems, represented as proportions of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations. We provide a general and data-driven modeling tool for compositional systems to resolve both of these issues. We achieve this through the principle of maximum entropy, which requires only minimal assumptions and limited experimental data in contrast to current alternatives. We show that our approach provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.

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The Refractory Period Matters: Unifying Mechanisms of Macroscopic Brain Waves

Cited by 10 publications

References 44 publications

A complementary resistive switching neuron

A complementary resistive switching neuron

Ion Gradient-driven Bifurcations of a Multi-Scale Neuronal Model

The Maximum Entropy Principle For Compositional Data

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