Frontiers in Computational Neuroscience | New and Recent Articles

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…

Neurorehabilitation poses a crucial problem in clinical recovery tasks, particularly for individuals with poor motor functions and neurological impairments, and problems in activities of daily living (ADL). To resolve this, we design a novel model, Rehab-DRLX, with a hybrid deep learning (HDL) framework that combines deep reinforcement learning (DRL) with an explainable transformer model to provi…

Background and objectivesElderly patients (≥65 years) who sustain burn injuries encounter a clinically significant perioperative challenge: a dysregulated hyperinflammatory response, characterized by elevated levels of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP), compounded by a markedly reduced hemodynamic reserve. Both propofol and low-dose ketamine e…

This paper focuses on possible time-domain neurocomputational mechanisms for short-term anticipatory processes. Here we present a simple, signal processing functional model of how short-term rhythmic pattern expectancies could be computed on the fly using recurrent neural timing nets (RTNs). The model is inspired by Gestaltist grouping principles for repeating temporal patterns of events (beats, …

IntroductionUnderstanding how deep learning models map neural population activity to stimuli requires both high predictive accuracy and interpretable internal mechanisms.MethodsIn this work, we employ the POYO framework, a scalable transformer architecture based on spike tokenization and latent modeling, to decode large-scale retinal ganglion cell recordings. We ask whether the model's attention …

Biological agents face an energy-information bottleneck: inference requires rapid exploration of large hypothesis spaces, yet high-gain spiking is metabolically expensive. We propose Coherent-Resonant Netting (CRN) as a two-regime decision architecture in which a low-amplitude Stage-I transport process filters candidate routes on a structural graph before a higher-cost Stage-II commitment step. I…

IntroductionThe process of precise structural evaluation for paranasal sinuses based on CT scan data establishes a foundation for medical professionals to assess human anatomical variations, supporting the diagnosis and treatment of ear, nose, and throat (ENT) conditions. Existing deep learning methods face difficulties in analyzing complex sinus structures due to limited annotated datasets and l…

The hippocampus is thought to support spatial memory and navigation by constructing predictive representations of the environment. Predictive map theory formalizes this function as a successor representation (SR). However, existing models assume a fixed and uniform distribution of place fields, despite experimental findings that place cell density is dynamically modulated by rewards and objects. …

IntroductionThe clinical assessment of patients with Disorders of Consciousness (DoC), ranging from the Vegetative State (VS/UWS) to the Minimally Conscious State (MCS), remains a significant challenge in neurology. Gold-standard behavioral tools are prone to high misdiagnosis rates because they depend on overt motor responses, which may be masked by physical impairments. Consequently, there is a…

Alzheimer’s Disease (AD) is a neurodegenerative disorder with insidious onset, making early diagnosis challenging. Electroencephalogram (EEG) is a promising noninvasive tool for AD diagnosis, but high-density EEG configurations cause computational burdens and hinder clinical translation. Thus, developing an efficient sparse EEG channel selection method with high classification accuracy is urgent …

Dopamine signaling has become closely associated with reward prediction errors (RPEs)–the difference between expected and experienced value. Although not without controversy, the dopamine RPE hypothesis is one of the most influential ideas in neuroscience. This review briefly summarizes its origins, empirical foundations, and theoretical development. We begin with early psychological studies whic…

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…

Artificial intelligence (AI) and machine learning (ML) have shown remarkable promise in advancing medical image analysis, yet their potential in neurology and psychiatry remains underexplored. This work explores the use of deep learning approaches for automated brain tumor classification, leveraging multimodal neuroimaging data comprising computed tomography (CT) and magnetic resonance imaging (M…

This paper critically analyzes MBTI-based personality profiling using Large Language Models (LLMs), examining both their use as tools for inferring human personality and as subjects evaluated through psychometric frameworks. We review recent work (2020–2025) spanning traditional machine learning, fine-tuned transformer models, and zero-shot prompting approaches across datasets such as Kaggle MBTI…

The ability to anticipate future events continuously is a hallmark of biological vision, yet standard deep learning models often struggle with long-term coherence due to the rigid discretization of time. In this paper, we propose NeuralVisionNet, a probabilistic framework that models visual anticipation as a continuous generative process, drawing inspiration from the predictive coding mechanisms …

Large neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity. Different types of differential equation models have been developed to comprehend these phenomena, connecting deterministic, stochastic, and mean-field descriptions. At the deterministic level, ordinary dif…

The brain is a highly recurrent, nonlinear network hypothesized to remain near the edge of chaos for optimal performance. Excitation and inhibition must be balanced precisely within every neuron to ensure a consistent level of dynamical stability and rich dynamics during transition to chaos. However, analysis of biologically realistic synaptic weight matrices suggests that sparsity and low-dimens…