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Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals.

ABSTRACT: Spontaneous blinking is one of the most frequent human behaviours. While attentionally guided blinking may benefit human survival, the function of spontaneous frequent blinking in cognitive processes is poorly understood. To model human spontaneous blinking, we proposed a leaky integrate-and-fire model with a variable threshold which is assumed to represent physiological fluctuations during cognitive tasks. The proposed model is capable of reproducing bimodal, normal, and widespread peak-less distributions of inter-blink intervals as well as the more common popular positively skewed distributions. For bimodal distributions, the temporal positions of the two peaks depend on the baseline and the amplitude of the fluctuating threshold function. Parameters that reproduce experimentally derived bimodal distributions suggest that relatively slow oscillations (0.11-0.25 Hz) govern blink elicitations. The results also suggest that changes in blink rates would reflect fluctuations of threshold regulated by human internal states.

SUBMITTER: Nomura R

PROVIDER: S-EPMC6207319 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals.

Nomura Ryota R Liang Ying-Zong YZ Morita Kenji K Fujiwara Kantaro K Ikeguchi Tohru T

PloS one 20181030 10

Spontaneous blinking is one of the most frequent human behaviours. While attentionally guided blinking may benefit human survival, the function of spontaneous frequent blinking in cognitive processes is poorly understood. To model human spontaneous blinking, we proposed a leaky integrate-and-fire model with a variable threshold which is assumed to represent physiological fluctuations during cognitive tasks. The proposed model is capable of reproducing bimodal, normal, and widespread peak-less di ...[more]

PMID: 30376565

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Project description:Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the "Functional subnetwork approach" (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network's intended function, without the use of global optimization or machine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuron model have fundamental similarities to those of a non-spiking leaky integrator neuron model. We derive analytical expressions that show functional parallels between: (1) A spiking neuron's steady-state spiking frequency and a non-spiking neuron's steady-state voltage in response to an applied current; (2) a spiking neuron's transient spiking frequency and a non-spiking neuron's transient voltage in response to an applied current; and (3) a spiking synapse's average conductance during steady spiking and a non-spiking synapse's conductance. The models become more similar as additional spiking neurons are added to each population "node" in the network. We apply the FSA to model a neuromuscular reflex pathway two different ways: Via non-spiking components and then via spiking components. These results provide a concrete example of how a single non-spiking neuron may model the average spiking frequency of a population of spiking neurons. The resulting model also demonstrates that by using the FSA, models can be constructed that incorporate both spiking and non-spiking units. This work facilitates the construction of large networks of spiking neurons and synapses that perform specific functions, for example, those implemented with neuromorphic computing hardware, by providing an analytical method for directly tuning their parameters without time-consuming optimization or learning.

Project description:BACKGROUND: Since thermodynamic stability is a global property of proteins that has to be conserved during evolution, the selective pressure at a given site of a protein sequence depends on the amino acids present at other sites. However, models of molecular evolution that aim at reconstructing the evolutionary history of macromolecules become computationally intractable if such correlations between sites are explicitly taken into account. RESULTS: We introduce an evolutionary model with sites evolving independently under a global constraint on the conservation of structural stability. This model consists of a selection process, which depends on two hydrophobicity parameters that can be computed from protein sequences without any fit, and a mutation process for which we consider various models. It reproduces quantitatively the results of Structurally Constrained Neutral (SCN) simulations of protein evolution in which the stability of the native state is explicitly computed and conserved. We then compare the predicted site-specific amino acid distributions with those sampled from the Protein Data Bank (PDB). The parameters of the mutation model, whose number varies between zero and five, are fitted from the data. The mean correlation coefficient between predicted and observed site-specific amino acid distributions is larger than <r> = 0.70 for a mutation model with no free parameters and no genetic code. In contrast, considering only the mutation process with no selection yields a mean correlation coefficient of <r> = 0.56 with three fitted parameters. The mutation model that best fits the data takes into account increased mutation rate at CpG dinucleotides, yielding <r> = 0.90 with five parameters. CONCLUSION: The effective selection process that we propose reproduces well amino acid distributions as observed in the protein sequences in the PDB. Its simplicity makes it very promising for likelihood calculations in phylogenetic studies. Interestingly, in this approach the mutation process influences the effective selection process, i.e. selection and mutation must be entangled in order to obtain effectively independent sites. This interdependence between mutation and selection reflects the deep influence that mutation has on the evolutionary process: The bias in the mutation influences the thermodynamic properties of the evolving proteins, in agreement with comparative studies of bacterial proteomes, and it also influences the rate of accepted mutations.

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Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals.

Publications

Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals.

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