Frontiers in Computational Neuroscience | New and Recent Articles

Electroencephalography (EEG)-based emotion recognition faces challenges such as signal noise, non-stationarity, inter-subject variability, and class imbalance, limiting its practical application in affective computing and clinical diagnostics. This study introduces the Attentive Wavelet-Transformer Network (AWT-Net), a novel framework integrating Hierarchical Wavelet Packet Decomposition (HWPD), …

We propose that current Neuroscience approaches can benefit from further integrating morphodynamics across different scales of brain organization and neural network emergent functions in complex systems. While emergence in neuroscience is commonly addressed at higher organizational levels, here we consider neuronal morphology itself as an emergent level of organization. Progressing from form-base…

IntroductionNeuronal firing patterns emerge from complex interactions between intrinsic membrane properties and synaptic receptor dynamics. N-methyl-D-aspartate (NMDA) receptors critically shape calcium influx and synaptic plasticity through their voltage-dependent Mg2+ block and prolonged activation kinetics, yet how their closing kinetics interact with glutamatergic drive and GABAergic modulati…

IntroductionHuman neural oscillations are organized according to golden ratio (φ = 1.618) mathematics: frequencies follow f(n)=f0×φn where f0≈7.6 Hz. This architecture manifests as spectral peak depletion at integer n positions (band boundaries) and enrichment at half-integer positions (band centers), providing empirical validation of a previously theorized φn architecture and identifying the abs…

Understanding and controlling the complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and model capacity. While most approaches rely on entropy-based loss functions and statistical metrics, these measures often fail to capture deeper, causally relevant algorithmic regularities embedded in network structure. We propose a shi…

Models formalize our understanding of a system and generate hypotheses that can be tested experimentally. In this study, we use a previously developed model of p-type electroreceptor afferents to support electrophysiological observations regarding the encoding of chirps in the electrosensory periphery of the weakly electric fish Apteronotus leptorhynchus. These animals employ their self-generated…

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…