computational-neuroscience

Author: Associate Professor Huong Ha Affiliations: Brain Health Lab, School of Biomedical Engineering, International University, Vietnam National University Ho Chi Minh City, Vietnam Key themes: • Development of neuroscience in Vietnam • Clinical to mechanistic neuroscience • Neuroinflammation research • Computational neuroscience and AI • International collaboration • Vietnam’s growing global ro…

Researchers at Helmholtz Munich, the Ludwig Maximilians University Munich (LMU) and collaborating institutions have developed an artificial intelligence (AI) framework that maps disease-related changes throughout the entire mouse body at cellular resolution.

Scientists at the Max Planck Florida Institute for Neuroscience (MPFI), in collaboration with ZEISS and MetaCell, have developed a powerful new imaging pipeline called Neuroplex.

IntroductionNEURON has been widely used as an empirically-based simulation tool, especially for multi-compartment conductance-based neuronal modeling. The network mediating feeding in Aplysia californica has been extensively studied as a model central pattern generator. Understanding the relationship between network parameter values and their effect on animal behavior is of key importance in syst…

Nature Physics, Published online: 18 May 2026; doi:10.1038/s41567-026-03306-3 In neurons, the mapping from inputs to output involves complex biophysical processes. Despite this complexity, it is now shown that simple artificial models explain a large fraction of the variability in neuronal activity.

Modern reasoning systems — especially Large Reasoning Models (LRMs) — rely on recursive continuation to perform reflective inference, counterfactual evaluation, and iterative refinement. Under admissible conditions, recursion performs eliminative work: candidate representations are tested under constraint, uncertainty contracts, and viable solution space narrows toward convergence. Within the McP…

Nature Communications, Published online: 15 May 2026; doi:10.1038/s41467-026-73032-0 How sensory systems rapidly adapt to changing stimulus statistics remains unclear. Here the authors show that gain adaptation in recurrent networks can implement fast efficient coding, unifying prior attraction and adapter repulsion, and supporting adaptive behavior.

We introduce a measurement framework for analyzing how neural representations diverge under transformations of increasing complexity. Rather than proposing a universal theory of representation learning or invariance, we define a single observable, $\Delta(c)$, which measures inter-model variance in representation stability across heterogeneous neural architectures. The framework is designed as an…

This study aims to jointly extract group-shared connectivity patterns and group-specific temporal and frequency information from multi-frequency dynamic functional network connectivity (dFNC) tensors of healthy controls (HCs) and schizophrenia patients (SZs) using a coupled canonical polyadic decomposition (CCPD) approach. Based on 145 subjects (71 SZs and 74 HCs) from the COBRE dataset, multi-fr…

Recent empirical results suggest that the relationship between representation similarity and transformation-dependent stability in neural networks is structured and non-monotonic. In particular, this relationship appears to exhibit regime-dependent behavior across transformation strengths and architectures. In this paper, we do not propose a validated theory, but a candidate structural interpreta…

People learn from experience, but with considerable individual differences in the degree and type of behavioral adjustments resulting from a given experience. Error driven learning rules provide an elegant framework for explaining both learning behavior and its neural signatures; however, implementing them requires carving the world into so-called “latent states”, that serve as substrates for lea…

BackgroundNeurite orientation dispersion and density imaging (NODDI), an emerging diffusion MRI technique for estimating the microstructural pathology of brain tissue in vivo, has attracted significant research interest. However, a systematic bibliometric analysis of this field remains unexamined. This study aims to perform a bibliometric analysis of the NODDI literature to explore the current re…

Psychotherapy schools often describe overlapping process phenomena in non-interoperable vocabularies. This pluralism is clinically valuable but computationally costly: datasets become difficult to compare, clinically load-bearing distinctions are collapsed into convenience labels, and artificial intelligence (AI) systems inherit annotation schemes rather than a clinically interpretable intermedia…

IntroductionContralateral organization is a defining feature of vertebrate nervous systems, yet its functional origin remains incompletely understood. We examined whether contralateral routing can arise as an advantageous solution in delayed bilateral control systems using a minimal computational framework.MethodsWe constructed abstract bilateral sensorimotor networks composed of sensory, central…

While perceptual multistability arises from many types of stimuli across different sensory systems, there are common dynamical features that may be rooted in universal organizing principles underlying perception. We probe the fundamental mechanisms responsible for visual multistability using a neuronal network model framework in which a set of realistic images directly drives competing pools of n…

Version 4 (April 2026) We develop a mathematical framework for neural computation in which percepts are modeled as probabilistic structures on directed acyclic graphs of subthreshold oscillatory (STO) activity, and affective valence is defined in terms of entropy dynamics on these structures. A central problem in the study of consciousness is how structured subjective experience, including both p…

Representation similarity measures such as Centered Kernel Alignment (CKA) are widely used to compare neural networks by quantifying geometric alignment in representation space. However, their relationship to functional behavior under input transformations remains unclear. We study the relationship between representation similarity and transformation-dependent stability across a sequence of neura…

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions Tuckute, Greta; Feather, Jenelle; Boebinger, Dana; McDermott, Josh H Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neura…