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Brain Sciences
Special Issues
Computational Neuroscience and Artificial Intelligence: Cross-Talks and Interrelated Contributions
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Computational Neuroscience and Artificial Intelligence: Cross-Talks and Interrelated Contributions

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1497

Special Issue Editor

Dr. Ali Yadollahpour

E-Mail Website1 Website2
Guest Editor
Department of Psychology, University of Sheffield, Sheffield, UK
Interests: modeling; computational neuroscience; cognitive neuroscience; artificial intelli-gence; machine learning; diagnosis; personalized treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational neuroscience aims to understand how the nervous system processes information to produce cognitive function and behavior. The core notion of this process is the models, that is, mathematical and computational descriptions of the system under study, including the structure, physiology, information processing and cognitive functions of the nervous system. Artificial intelligence (AI) simulates human intelligence using machines to build intelligent automation of complex tasks for such machines achieve decision making and problem solving capacities comparable to that of the human brain. Computational neuroscience and AI are mutually interrelated and benefit one another. Computational neuroscience has brought various novelties and improvements into AI. Biological neural networks have inspired the building of complex deep neural network architectures successfully used in object detection, text processing, and the prediction and early detection of diseases. AI-based systems inspired by neuroscience have been successfully used in robot-based surgery, self-driving vehicles, medical diagnosis and gaming applications. This Special Issue aims to review recent advances, applications and challenges in using computational neuroscience and AI in human life.

Dr. Ali Yadollahpour
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational neuroscience
  • cognitive neuroscience
  • artificial intelligence
  • machine learning
  • precision medicine
  • early diagnosis
  • treatment

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Published Papers (2 papers)

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Research

19 pages, 6780 KiB  
Article
Sensitivity of Spiking Neural Networks Due to Input Perturbation
by Haoran Zhu, Xiaoqin Zeng, Yang Zou and Jinfeng Zhou
Brain Sci. 2024, 14(11), 1149; https://doi.org/10.3390/brainsci14111149 - 16 Nov 2024
Viewed by 503
Abstract
Background: To investigate the behavior of spiking neural networks (SNNs), the sensitivity of input perturbation serves as an effective metric for assessing the influence on the network output. However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky [...] Read more.
Background: To investigate the behavior of spiking neural networks (SNNs), the sensitivity of input perturbation serves as an effective metric for assessing the influence on the network output. However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky integrate-and-fire (LIF) neurons due to the intricate neuronal dynamics during the feedforward process. Methods: This paper first defines the sensitivity of a temporal-coded spiking neuron (SN) as the deviation between the perturbed and unperturbed output under a given input perturbation with respect to overall inputs. Then, the sensitivity algorithm of an entire SNN is derived iteratively from the sensitivity of each individual neuron. Instead of using the actual firing time, the desired firing time is employed to derive a more precise analytical expression of the sensitivity. Moreover, the expectation of the membrane potential difference is utilized to quantify the magnitude of the input deviation. Results/Conclusions: The theoretical results achieved with the proposed algorithm are in reasonable agreement with the simulation results obtained with extensive input data. The sensitivity also varies monotonically with changes in other parameters, except for the number of time steps, providing valuable insights for choosing appropriate values to construct the network. Nevertheless, the sensitivity exhibits a piecewise decreasing tendency with respect to the number of time steps, with the length and starting point of each piece contingent upon the specific parameter values of the neuron. Full article
(This article belongs to the Special Issue Computational Neuroscience and Artificial Intelligence: Cross-Talks and Interrelated Contributions)
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15 pages, 28624 KiB  
Article
Efficient Neural Decoding Based on Multimodal Training
by Yun Wang
Brain Sci. 2024, 14(10), 988; https://doi.org/10.3390/brainsci14100988 - 28 Sep 2024
Viewed by 604
Abstract
Background/Objectives: Neural decoding methods are often limited by the performance of brain encoders, which map complex brain signals into a latent representation space of perception information. These brain encoders are constrained by the limited amount of paired brain and stimuli data available for [...] Read more.
Background/Objectives: Neural decoding methods are often limited by the performance of brain encoders, which map complex brain signals into a latent representation space of perception information. These brain encoders are constrained by the limited amount of paired brain and stimuli data available for training, making it challenging to learn rich neural representations. Methods: To address this limitation, we present a novel multimodal training approach using paired image and functional magnetic resonance imaging (fMRI) data to establish a brain masked autoencoder that learns the interactions between images and brain activities. Subsequently, we employ a diffusion model conditioned on brain data to decode realistic images. Results: Our method achieves high-quality decoding results in semantic contents and low-level visual attributes, outperforming previous methods both qualitatively and quantitatively, while maintaining computational efficiency. Additionally, our method is applied to decode artificial patterns across region of interests (ROIs) to explore their functional properties. We not only validate existing knowledge concerning ROIs but also unveil new insights, such as the synergy between early visual cortex and higher-level scene ROIs, as well as the competition within the higher-level scene ROIs. Conclusions: These findings provide valuable insights for future directions in the field of neural decoding. Full article
(This article belongs to the Special Issue Computational Neuroscience and Artificial Intelligence: Cross-Talks and Interrelated Contributions)
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