Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

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fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

Applied Sciences ◽

10.3390/app11114922 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4922

Author(s):

Tengfei Ma ◽

Wentian Chen ◽

Xin Li ◽

Yuting Xia ◽

Xinhua Zhu ◽

...

Keyword(s):

Deep Learning ◽

Total Duration ◽

Signal Classification ◽

Imagery Task ◽

Neural Activities ◽

Computer Interfaces ◽

Acquisition Device ◽

The Time Domain ◽

The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

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What Is Consciousness, and Could Machines Have It?

Robotics, AI, and Humanity ◽

10.1007/978-3-030-54173-6_4 ◽

2021 ◽

pp. 43-56

Author(s):

Stanislas Dehaene ◽

Hakwan Lau ◽

Sid Kouider

Keyword(s):

Information Processing ◽

Human Brain ◽

Careful Consideration ◽

The Self ◽

Self Monitoring ◽

Unconscious Processing ◽

Word Consciousness ◽

Types Of Information ◽

The Brain ◽

Selection Of

AbstractThe controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

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ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

10.34076/2219-6838-2019-3-27-35 ◽

2019 ◽

pp. 27-35

Author(s):

Alexandr Neznamov

Keyword(s):

Information Processing ◽

Scientific Community ◽

Digital Technologies ◽

Significant Feature ◽

Legal Proceedings ◽

Civil Process ◽

Civil Proceedings ◽

Processing Information ◽

The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

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An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Complex & Intelligent Systems ◽

10.1007/s40747-021-00310-3 ◽

2021 ◽

Author(s):

Muhammad Irfan Sharif ◽

Jian Ping Li ◽

Javeria Amin ◽

Abida Sharif

Keyword(s):

Brain Tumor ◽

Tumor Detection ◽

Entropy Method ◽

Feature Extraction And Selection ◽

Tumor Region ◽

Selection For ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

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Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

Methods of Information in Medicine ◽

10.1055/s-0038-1634391 ◽

2002 ◽

Vol 41 (04) ◽

pp. 337-341 ◽

Cited By ~ 24

Author(s):

F. Cincotti ◽

D. Mattia ◽

C. Babiloni ◽

F. Carducci ◽

L. Bianchi ◽

...

Keyword(s):

Mahalanobis Distance ◽

Brain Computer Interface ◽

Computer Interface ◽

Correct Classification ◽

Interface Area ◽

System A ◽

The Brain ◽

Full Covariance Matrix ◽

Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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Application of MRI for the Diagnosis of Neoplasms

BioMed Research International ◽

10.1155/2018/2715831 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Ewa Bejer-Oleńska ◽

Michael Thoene ◽

Andrzej Włodarczyk ◽

Joanna Wojtkiewicz

Keyword(s):

Benign Neoplasms ◽

Young Males ◽

Classification Of Diseases ◽

Icd 10 ◽

Mri Scans ◽

International Statistical Classification ◽

The Central Nervous System ◽

Spss Software ◽

The Brain

Aim. The aim of the study was to determine the most commonly diagnosed neoplasms in the MRI scanned patient population and indicate correlations based on the descriptive variables. Methods. The SPSS software was used to determine the incidence of neoplasms within the specific diagnoses based on the descriptive variables of the studied population. Over a five year period, 791 patients and 839 MRI scans were identified in neoplasm category (C00-D48 according to the International Statistical Classification of Diseases and Related Health Problems ICD-10). Results. More women (56%) than men (44%) represented C00-D48. Three categories of neoplasms were recorded. Furthermore, benign neoplasms were the most numerous, diagnosed mainly in patients in the fifth decade of life, and included benign neoplasms of the brain and other parts of the central nervous system. Conclusions. Males ≤ 30 years of age with neoplasms had three times higher MRI scans rate than females of the same age group; even though females had much higher scans rate in every other category. The young males are more often selected for these scans if a neoplasm is suspected. Finally, the number of MRI-diagnosed neoplasms showed a linear annual increase.

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Event-Related Potentials in Psychiatry: Approaches to Research and Clinical Applications

Australian & New Zealand Journal of Psychiatry ◽

10.3109/00048678309160007 ◽

1983 ◽

Vol 17 (4) ◽

pp. 307-318 ◽

Cited By ~ 3

Author(s):

H. G. Stampfer

Keyword(s):

Information Processing ◽

Psychiatric Disorders ◽

Rapid Development ◽

Event Related Potentials ◽

Clinical Applications ◽

Direct Access ◽

Practical Application ◽

Clinical Methods ◽

Related Potentials ◽

The Brain

This article suggests that the potential usefulness of event-related potentials in psychiatry has not been fully explored because of the limitations of various approaches to research adopted to date, and because the field is still undergoing rapid development. Newer approaches to data acquisition and methods of analysis, combined with closer co-operation between medical and physical scientists, will help to establish the practical application of these signals in psychiatric disorders and assist our understanding of psychophysiological information processing in the brain. Finally, it is suggested that psychiatrists should seek to understand these techniques and the data they generate, since they provide more direct access to measures of complex cerebral processes than current clinical methods.

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Clinical and neuroimaging features of chronic cerebral ischemia in polycythemia vera

Medical alphabet ◽

10.33667/2078-5631-2021-3-58-62 ◽

2021 ◽

pp. 58-62

Author(s):

G. V. Zyrina ◽

T. A. Slyusa

Keyword(s):

Cerebral Ischemia ◽

Polycythemia Vera ◽

Mini Mental State Examination ◽

Comparison Group ◽

Cognitive Disorders ◽

Chronic Cerebral Ischemia ◽

State Examination ◽

The Brain

The purpose of the study. To study clinical and neuroimaging features of chronic cerebral ischemia (CCI) in polycythemia vera (PV).Materials and methods. 66 patients with PV were examined – the main group (43 men, 23 women; mean age 62.0 ± 3.4 years), of which 64 (97.0%) patients were diagnosed with CCI. The comparison group consisted of 85 patients with CCI (34 men, 51 women; mean age 67.7 ± 4.6 years), who developed against the background of cerebral vascular atherosclerosis and arterial hypertension. To identify cognitive disorders, we used Mini Mental State Examination (MMSE). Insomnia was studied in accordance with the criteria of the International Classification of Sleep ICDS‑22005. The quality of sleep was determined using a questionnaire from the Federal Somnological Center. Neuroimaging (MRI of the brain) was performed on Siemens Symphony 1.5 T and GE Signa 1.5 T tomographs.Results. Subjective symptoms CCI are characterized by a greater representation of asthenic and insomniac disorders. Transient ischemic attacks in patients with PV are significantly more common than in the comparison group, their frequency depends on the duration of PV. The revealed changes in MRI of the brain in the majority of PV patients with CCI are characteristic of multiinfarction vascular encephalopathy; in the comparison group, changes that characteristic for subcortical arteriosclerotic encephalopathy were more often recorded.

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