Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Pręgowska, Agnieszka

doi:10.3390/e23010092

Cited by 13 publications

(5 citation statements)

References 51 publications

(49 reference statements)

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“…Knoblauch et al [59] examined the relationship between noise and inter-spike-interval statistics in real neurons in working brains. Pregowska [60] explored how spike fluctuations affect the information transmission rates. Moreover, how neurons communicate is also an important aspect of studying brain function.…”

Section: Discussionmentioning

confidence: 99%

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Wang

Yao

Bai

et al. 2023

Entropy

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Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.

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Section: Discussionmentioning

confidence: 99%

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Wang

Yao

Bai

et al. 2023

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“…It was found that improved transmission properties in the case of limited visibility were obtained for a wavelength of about 10 µ m, compared to the near-infrared waves. Analogous situations related to the selection/determination of appropriate parameters of the sources generating signals being sent through noisy transmission channels to improve the transmission efficiency also occur in classic transmission channels [28][29][30][31][32][33].…”

Section: Methodsmentioning

confidence: 99%

Experimental Study of the Free Space Optics Communication System Operating in the 8–12 µm Spectral Range

et al. 2021

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(1) Background: Free space optics communication (FSO) has improved wireless communication and data transfer thanks to high bandwidth, low power consumption, energy efficiency, a high transfer capacity, and a wide applicability field. The FSO systems also have their limitations, including weather conditions and obstacles in the way of transmission. (2) Methods: This research assesses the atmospheric conditions’ influence on the intensity of received radiation, both experimentally and theoretically. The construction of a laboratory test stand of the FSO system, which is operating in the third-atmosphere transmission window (8–12 µm), is proposed. Next, considering different atmospheric conditions, the experimental validation was conducted, both in a laboratory and real conditions. (3) Results: The measurements were carried out for two optical links working with wavelengths of 1.5 µm and 10 µm. It was found that optical radiation with a wavelength of about 10 µm is characterized by better transmission properties in the case of limited visibility (e.g., light rain and fogs) than in the case of near-infrared waves. The same conclusion was found in analytical investigations. (4) Conclusions: The results obtained show that optical radiation with a wavelength of about 10 µm in limited visibility is characterized by better transmission properties than near-infrared waves. This demonstrates the validity of designing FSO links operating in the range 8–12 µm band, e.g., based on quantum cascade lasers and HgCdTe photodiodes.

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“…Since the literature of neuroscience is vast [39], we will not review it here, but we do want to highlight some progress in neuroscience that can potentially inspire and improve current machine learning systems. The study of neural codes [40], neural information processing and transmission [41][42][43], and neural spikes [44] may provide tools and language towards the study and design of artificial neural networks.…”

Section: Related Workmentioning

confidence: 99%

Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability

Liu,

Gan,

Tegmark

2023

Entropy

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We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the length of each neuron connection. This is inspired by the idea of minimum connection cost in evolutionary biology, but we are the first the combine this idea with training neural networks with gradient descent for interpretability. We demonstrate that BIMT discovers useful modular neural networks for many simple tasks, revealing compositional structures in symbolic formulas, interpretable decision boundaries and features for classification, and mathematical structure in algorithmic datasets. Qualitatively, BIMT-trained networks have modules readily identifiable by the naked eye, but regularly trained networks seem much more complicated. Quantitatively, we use Newman’s method to compute the modularity of network graphs; BIMT achieves the highest modularity for all our test problems. A promising and ambitious future direction is to apply the proposed method to understand large models for vision, language, and science.

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Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Cited by 13 publications

References 51 publications

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Experimental Study of the Free Space Optics Communication System Operating in the 8–12 µm Spectral Range

Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability

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