In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.
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http://dx.doi.org/10.3389/fnsys.2014.00108 | DOI Listing |
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December 2024
Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida, UP, India.
Speaker verification in text-dependent scenarios is critical for high-security applications but faces challenges such as voice quality variations, linguistic diversity, and gender-related pitch differences, which affect authentication accuracy. This paper introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) architecture to address these challenges. The Gender-Aware Network utilizes Convolutional 2D layers with ReLU activation for initial feature extraction, followed by multi-fusion dense skip connections and batch normalization to integrate features across different depths, enhancing discrimination between male and female speakers.
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December 2024
Department of Pharmacy Services, Vocational School of Health Services, Osmaniye Korkut Ata University, Osmaniye, Turkey.
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December 2024
Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia.
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December 2024
Research Group for Implantable Microsystems, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, Budapest, 1083, Hungary.
Infrared neural stimulation has consistently shown that temperature is a critical neuronal state variable. However, a comprehensive understanding of the biophysical background is essential. In this study, using high-density laminar electrode recordings, we investigated the impact of pulsed and continuous-wave infrared illumination on cortical neurons in anesthetized rats ([Formula: see text]).
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December 2024
Department of Computer Science, College of Charleston, Charleston, SC, USA.
The rapid propagation of information in the digital epoch has brought a surge of rumors, creating a significant societal challenge. While prior research has primarily focused on the psychological aspects of rumors-such as the beliefs, behaviors, and persistence they evoke-there has been limited exploration of how rumors are processed in the brain. In this study, we experimented to examine both behavioral responses and EEG data during rumor detection.
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