Announcements

Improving our understanding of brain function requires interdisciplinary collaborations between theoretical, computational, and experimental disciplines and approaches. The “Computational Neuroscience and Neuroinformatics” Section of Brain Sciences (ISSN: 2076-3425) fosters multidisciplinary interactions between theoretical, computational, and experimental work in the field of neuroscience.

We welcome original contributions on a wide range of topics that promote theoretical modeling focused on understanding neural function at the molecular, cellular, and circuit levels via computational and model-based approaches that are experimentally testable. While this Section primarily focuses on theoretical and computational research, it seeks experimental studies that validate and test theoretical conclusions. Primarily theoretical manuscripts should be highly relevant to the neural mechanisms of neural function, while primarily experimental manuscripts should have implications for the computational analysis of nervous system function.

The submission of manuscripts investigating the physiological mechanisms underlying neuropathologies by combining theoretical and experimental approaches is highly encouraged. Similarly, manuscripts describing novel technological advances in data analysis techniques to gain further insights into the function of the nervous system are also highly sought. Modeling approaches at all levels, from biophysically motivated realistic simulations of neurons and synapses to high-level behavioral models of inference and decision making, are also welcome.

As all of the articles published in our journal are of an open access format, you have free and unlimited access to the full texts. We welcome you to read our most highly cited papers published in 2023 and 2024, which are listed below.

1. “Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence”
by Tehseen Mazhar, Dhani Bux Talpur, Tamara Al Shloul, Yazeed Yasin Ghadi, Inayatul Haq, Inam Ullah, Khmaies Ouahada and Habib Hamam
Brain Sci. 2023, 13(4), 683; https://doi.org/10.3390/brainsci13040683
Available online: https://www.mdpi.com/2076-3425/13/4/683

2. “Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning”
by Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan and Heba G. Mohamed
Brain Sci. 2023, 13(4), 602; https://doi.org/10.3390/brainsci13040602
Available online: https://www.mdpi.com/2076-3425/13/4/602

3. “Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory”
by Sunusi Bala Abdullahi, Zakariyya Abdullahi Bature, Lubna A. Gabralla and Haruna Chiroma
Brain Sci. 2023, 13(4), 555; https://doi.org/10.3390/brainsci13040555
Available online: https://www.mdpi.com/2076-3425/13/4/555

4. “Systematic Review and Future Direction of Neuro-Tourism Research”
by Abeer Al-Nafjan, Mashael Aldayel and Amira Kharrat
Brain Sci. 2023, 13(4), 682; https://doi.org/10.3390/brainsci13040682
Available online: https://www.mdpi.com/2076-3425/13/4/682

5. “The Clinical Relevance of Artificial Intelligence in Migraine”
by Angelo Torrente, Simona Maccora, Francesco Prinzi, Paolo Alonge, Laura Pilati, Antonino Lupica, Vincenzo Di Stefano, Cecilia Camarda, Salvatore Vitabile and Filippo Brighina
Brain Sci. 2024, 14(1), 85; https://doi.org/10.3390/brainsci14010085
Available online: https://www.mdpi.com/2076-3425/14/1/85

6. “Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition”
by Santhosh Peketi and Sanjay B. Dhok
Brain Sci. 2023, 13(2), 315; https://doi.org/10.3390/brainsci13020315
Available online: https://www.mdpi.com/2076-3425/13/2/315

7. “Emotion Recognition from Spatio-Temporal Representation of EEG Signals via 3D-CNN with Ensemble Learning Techniques”
by Rajamanickam Yuvaraj, Arapan Baranwal, A. Amalin Prince, M. Murugappan and Javeed Shaikh Mohammed
Brain Sci. 2023, 13(4), 685; https://doi.org/10.3390/brainsci13040685
Available online: https://www.mdpi.com/2076-3425/13/4/685

8. “Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review”
by Shaida Kargarnovin, Christopher Hernandez, Farzad V. Farahani and Waldemar Karwowski
Brain Sci. 2023, 13(5), 813; https://doi.org/10.3390/brainsci13050813
Available online: https://www.mdpi.com/2076-3425/13/5/813