Frontiers in Computational Neuroscience | New and Recent Articles

IntroductionAlthough neural activity is organized across multiple temporal and spatial scales, the principles determining information representation across scales remain unclear. In particular, while recent empirical results have reported mesoscale optimality in neural decoding, no theoretical accounts exist that can explain when and why such intermediate scales emerge as optimal. Here, we develo…

Recurrent Neural Networks (RNNs) are widely used to model neural activity in Computational Neuroscience. Here, we explore the mathematical foundations of three fundamental procedures that can be implemented: temporal rescaling, discretization, and linearization. These techniques provide crucial tools for characterizing the behavior of RNNs, offering insights into their temporal dynamics, facilita…

IntroductionIntracortical microstimulation (ICMS) of the primary somatosensory cortex can evoke localized tactile percepts, yet the spatial factors that influence perceptual discrimination remain poorly defined. In prior work, we showed that discrimination accuracy between behaviorally evaluated ICMS-evoked percepts declines as stimulation sites converge across cortical depths and adjacent cortic…

Reservoirs, typically implemented as recurrent neural networks (RNNs) with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of computational tasks. However, increasing the magnitude of reservoir connection weights to exploit non-linear dynamics can cause the network to develop strong spontaneous activity that drives neurons into saturati…

Heart rate variability (HRV) represents a rich physiological signal that captures the computational dynamics of autonomic regulation. Emerging evidence suggests that atypical autonomic control contributes to the neurocognitive phenotype of neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, the characterization…

IntroductionTemporal melody similarity is a fundamental problem in music modelling, and current methods are mostly based on recurrent or attention-based architectures that implicitly learn sequential structures.MethodsThis work adopts an explicit feature-representation approach using temporal-style descriptors, including first-order deltas, relative ratios, polynomial interactions, and rolling st…

A growing body of evidence indicates that arts engagement produces measurable effects on mental and physical health, yet a mechanistic account linking aesthetic experience to physiological regulation remains lacking. This review proposes a neurobiological framework grounded in two convergent theoretical traditions: the active inference formulation of brain function and the allostatic model of phy…

George A. Miller’s classic discussion of memory capacity and Sidney Smith’s recoding experiments demonstrated that cognitive limits depend more strongly on the number of active representational units (“chunks”) than on the total amount of raw information being processed. Here, we reinterpret Smith’s experiments from the perspective of modern computational neuroscience and representation learning.…

IntroductionPost-traumatic stress disorder (PTSD) is characterized by intrusive memories and an impaired resilience framework, which often leads to chronic psychological distress. Eye Movement Desensitization and Reprocessing (EMDR) is an emerging therapeutic approach targeting PTSD, yet the precise neurobiological mechanisms remain inadequately defined. Recent studies suggest immune modulation, …

Biological sex is increasingly recognized as a fundamental dimension of brain organization, yet how it influences early neurodevelopment, particularly autism spectrum disorder (ASD), remains understudied. Using data from the International Infant EEG Data Integration Platform (EEG-IP), a multi-site longitudinal cohort of 179 infants (91 males, 88 females) at elevated (ELA) and typical (TLA) likeli…

Electroencephalography (EEG)-based depression classification requires interpretable machine-learning approaches and validation strategies that avoid subject-level information leakage. This single-center pilot study developed and internally evaluated a marker-based interpretable machine-learning framework using eight-channel resting-state EEG. After quality control, 48 participants were included, …

The capacity of long-term memory seems to be extremely large, capable of storing information spanning almost a lifetime. Why does it have such a vast capacity? Why are some memories so enduring? What is the actual physical form of long-term memory? In the movie Inside Out, it is depicted as individual orbs containing information. Is that really the case? Simply explaining this by saying that the …

Dopamine (DA) is an important neuromodulator that has been suggested to play key roles in a range of neuropsychiatric disorders. However, its computational impact at a general single-neuron level has not been elucidated. Here, we extended a spike-timing-dependent plasticity (STDP)-based single-neuron spatio-temporal pattern detection model by incorporating a DA input and DA-type STDP modulation. …

Spiking recurrent neural networks (SRNNs) rival gated recurrent neural networks (RNNs) on various tasks, yet they still lack several hallmarks of biological neural networks. We introduce a biologically grounded SRNN that implements Dale's law with conductance-based stands for a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and gamma-aminobutyric acid (GABA) reversal potentials. These …

Along the lines of neuronal global workspace theories, the paper hypothesizes that active neural signal regeneration in recurrent, re-entrant circuits could constitute an organizational basis for states of conscious awareness. In this view, brains are self-production systems that regenerate their own informational, signal states (“neural autopoiesis”). States of awareness themselves depend critic…

IntroductionThis study proposes a neural-network-inspired computational framework for hierarchical, noise-tolerant coordination in distributed agent networks. The framework is evaluated in a cyber-physical demand-side management testbed in which autonomous home energy management systems perform local scheduling under a shared global price signal.MethodsPrivacy-Preserving Federated Congestion-Sign…

Neurons near a subcritical Hopf bifurcation are distinguished by their ability to produce subthreshold oscillations, maintain bistability between rest and repetitive spiking, and fire preferentially in response to inputs near their intrinsic resonant frequency. Simulating these properties in large neural networks using continuous-time models such as the Hodgkin-Huxley formalism is computationally…

Human facial emotion recognition (FER) is a vibrant research field. This research proposes a novel, biologically inspired hybrid FER framework that uniquely connects event-driven Spiking Neural Networks (SNNs) with deep learning, specifically a Spike-based Support Vector Machine (S-SVM), which is designed for its event-driven processing and energy efficiency. The article proposes a novel SNN-base…

Waves are fundamental. In our view, waves in the brain may constitute and drive organized neural activity patterns on individual neural and population levels. Their interactions follow basic physical principles. Taking a comprehensive, temporal and spatiotemporal perspective, we endeavor to explain multiple brain functions and behaviors with a unified mechanistic approach. Starting with neural ar…