Editorial: Interdisciplinary Synergies in Neuroinformatics, Cognitive Computing, and Computational Neuroscience

Nabamita Deb^1*Zardad Khan²MUHAMMAD SULAIMAN³Maharani Abu Bakar⁴

• ¹Gauhati University, Guwahati, India
• ²United Arab Emirates University,, Al-Ain, United Arab Emirates
• ³Abdul Wali Khan University Mardan, Mardan, Pakistan
• ⁴Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia

This collection encompasses of four very varied articles , and a brief about each has been given below:In the first article,( Oyama and Tani ,2025) the authors developed a predictive-coding inspired variational recurrent neural network (VRNN) that autonomously shifts between focused attention and mind-wandering. The meta-prior parameter w rises when reconstruction error increases, which prompts the network to rely more on internal predictions (mind-wandering), In other case of reduced error , it lowers w, shifting focus back to external sensory input (focused state).The second article( Zeki and Dag,2025) introduce a mathematically reduced discrete-map model for inhibitory neural networks whose bursting behavior is modulated by slow calcium currents. Their model predicts the number of spikes per burst based on initial calcium levels, maps fixed points, and tests stability. It closely matches the behavior of the original continuous system, offering analytical insights into calcium's vital role in shaping neural bursts. The third arcticle (Li et al.,2025) proposes a novel digital handwriting assessment paradigm for early detection of mild cognitive impairment (MCI) due to Alzheimer's disease (AD). The study was done on 72 subjects (34 healthy controls, 38 MCI due to AD), which collected dynamic handwriting and imagery data via touchscreen and analyzed digital biomarkers from the writing process. Their method achieved AUC = 0.918-substantially outperforming classical MMSE (AUC = 0.783) and MoCA (AUC = 0.859) scales. The technique is intelligent, convenient, and demonstrates strong early-warning potential, though its generalizability across scripts and cultures remains to be verified.The final artcile (Luo et al.,2025) highlights the use of a constraint-based metabolic model to investigate bioenergetic disparities between synaptic terminals and neuronal somata in dopaminergic neurons, which are critically implicated in Parkinson's disease (PD). Their model quantifies differential metabolic demands and suggests that synaptic energy metabolism uniquely contributes to neuronal vulnerability in PD. This work connects metabolic modeling with neurodegenerative disease mechanisms and opens avenues for targeted metabolic interventionsMoving forward, the synergistic collaboration between neuroscientists, computer scientists, data engineers, psychologists, and ethicists will be indispensable. The complexity of cognition demands such pluralism in approach. As we aim to decode the brain and encode intelligence, the integrative spirit of these disciplines must guide our scientific and technological journey.This special issue is a call to celebrate and advance this interdisciplinary synergy.