arXiv:2604.01295v1 Announce Type: new Abstract: This work presents the Parallelized Hierarchical Connectome (PHC), a general framework that upgrades temporal-only State-Space Models (SSMs) into spatiotemporal recurrent networks. Conventional SSMs achieve high-speed sequence processing through parallel scans, yet are limited to temporal recurrence without lateral or feedback interactions within a single timestep. PHC maps the diagonal SSM core to a shared Neuron Layer and inter-neuronal communication to a shared Synapse Layer, where neurons are partitioned into hierarchical regions governed by the connectome topology. A Multi-Transmission Loop enables intra-slice spatial recurrence, allowing signals to propagate across the hierarchical connectome within each temporal window while preserving O(logT) parallelism. This framework enables integration of neuro-physical priors typically intractable for standard SSMs, including adaptive leaky integrate-and-fire dynamics, Dale's Law, short-term plasticity, and reward-modulated spike-timing-dependent plasticity. The framework is instantiated as PHCSSM, the first model to unify recurrent spiking neural network dynamics with diagonal SSM parallelism while enforcing all five biological constraints and learnable lateral connections within a fully parallelizable training pipeline. Empirical results on physiological benchmarks from the UEA multivariate time-series archive demonstrate that PHCSSM achieves performance competitive with state-of-the-art SSMs while reducing parameter complexity from Theta(D^2 L) for L-layer stacked architectures to Theta(D^2). These findings suggest that biologically grounded inductive biases offer a principled route to parameter-efficient sequence modeling, opening diagonal SSMs to spatiotemporal recurrence and enabling fully parallelizable recurrent spiking neural network training.

arXiv:2604.01475v1 Announce Type: new Abstract: Parkinsons disease (PD) alters cortical neural dynamics, yet reliable non-invasive electrophysiological biomarkers remain elusive. This study examined whether interpretable EEG features capturing complementary aspects of neural dynamics can discriminate Parkinsonian neural states. A comprehensive set of interpretable features was extracted and grouped into Standard descriptors (spectral power, phase synchronization, time-domain statistics) and Dynamical descriptors (aperiodic activity, cross-frequency coupling, scale-free dynamics, neuronal avalanche statistics, and instantaneous frequency measures). A multi-head attention transformer classifier was trained using strict LOSO validation. Group-level comparisons were performed to identify electrophysiological differences associated with disease and medication state. Standard feature sets achieved strongest performance in discriminating medication states (PDoff vs PDon), whereas Dynamical performed competitively in contrasts between PD patients and healthy controls. Random feature ablation analyses indicated that Dynamical descriptors provide complementary information distributed across features while correlation analysis revealed low redundancy within both feature sets. Group-level comparisons revealed medication-sensitive reductions in delta power and voltage variance, modulation of neuronal avalanche statistics, persistent increases in theta phase synchronization in PD patients, and disease-related alterations in cross-frequency interactions. Traditional spectral and synchronization features primarily reflect medication-related neural modulation, whereas dynamical descriptors reveal broader alterations in cortical network organization associated with disease but also with medication. These findings support multivariate EEG representations as a promising framework for developing non-invasive biomarkers of PD.

arXiv:2604.02057v1 Announce Type: new Abstract: Neural communication operates on both fast synaptic transmission and slower, diffusive extrasynaptic signaling, yet how these two modes jointly organize brain function remains unclear. Here, using the complete synaptic and neuropeptidergic connectomes of \emph{Caenorhabditis elegans}, we develop a unified multiplex framework linking anatomical wiring to functional communication. We infer structure-derived functional connectivity from the synaptic connectome using equilibrium principles from statistical physics, yielding a probabilistic map of information flow across all synaptic pathways, and compare this functional layer directly with the extrasynaptic connectome. This reveals a principled functional specialization across four communication regimes: (i) a topology-dependent layer that reinforces and stabilizes synaptic motor circuits, (ii) a topology-resilient modulatory layer supporting global regulation and behavioral state control, (iii) a purely extrasynaptic network sustaining survival and homeostasis, and (iv) a purely synaptic regime mediating rapid, low-latency sensorimotor processing. Together, these findings reveal that synaptic and extrasynaptic signaling form complementary architectures optimized for speed, modulation, robustness, and survival, and provide a general strategy for integrating structural and modulatory connectomes to understand how distinct communication modes cooperate to sustain coherent brain function.

arXiv:2604.02212v1 Announce Type: new Abstract: Accurate phase estimation at the edge of data segments is crucial for EEG applications such as EEG-TMS in offline and real-time data analysis. Our research evaluates the phase estimation performance of four commonly used methods (Phastimate, SSPE, ETP, and PhastPadding) for accuracy and systemic biases, using data from young and elderly healthy controls and chronic stroke participants. To address the identified limitations of the established methods, we introduce Phase Estimation with Autoregressive Padding (PEAP), a method that prevents strong bandpass filtering-induced artifacts. Contrary to the established methods, PEAP does not show significant biases and improves accuracy by 3.2 to 9.2% for the continuous phase estimation. Our offline analysis demonstrates how established methods are systematically biased towards some estimates and how they induce phase shifts. We also show that differences between methods do not vary between clinical and control populations, supporting their translatability. This work indicates that systematic biases in established phase estimation methods may compromise the validity and comparability of phase-dependent findings. PEAP addresses these limitations and thus offers a more reliable and more accurate alternative method.

arXiv:2412.04172v3 Announce Type: replace Abstract: Neurons rely on two interdependent mechanisms, homeostasis and neuromodulation, to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms in conductance-based models often produces unreliable outcomes, particularly when sharp neuromodulation interferes with calcium-homeostatic tuning. This study explores how a biologically inspired neuromodulation controller can harmonize with calcium homeostasis to ensure reliable neuronal function. Using computational models of stomatogastric ganglion and dopaminergic neurons, we demonstrate that controlled neuromodulation preserves neuronal firing patterns while calcium homeostasis simultaneously maintains target intracellular calcium levels. Unlike sharp neuromodulation, the neuromodulation controller integrates activity-dependent feedback through mechanisms mimicking G-protein-coupled receptor cascades. The interaction between these controllers critically depends on the existence of an intersection in conductance space, representing a balance between target calcium levels and neuromodulated firing patterns. Maximizing neuronal degeneracy enhances the likelihood of such intersections, enabling robust modulation and compensation for channel blockades. We further show that this controller pairing extends to network-level activity, reliably modulating the rhythmic activity of central pattern generators. This study highlights the complementary roles of calcium homeostasis and neuromodulation, proposing a unified control framework for maintaining robust and adaptive neural activity under physiological and pathological conditions.

arXiv:2505.22680v2 Announce Type: replace Abstract: A recently proposed geometric framework predicts that the transition from distributed belief to committed action involves a metric regime change, culminating in a boundary regime where cross-mode structure becomes algebraically necessary for continued state-space compression. This paper examines whether reported magnetic resonance measurements of proton spins in neural tissue provide an empirical probe of this regime. A companion analysis identifies the detected signal as the readout-converted signature of double-quantum SU(1,1) pair coherence, which correlates with short-term memory performance and cardiac-phase dynamics during wakefulness. We show that the mathematical bridge between the abstract transport framework and the physical spin system is the Bures metric, which natively governs both Gaussian Wasserstein geometry and quantum density matrices. We argue that the observed signal is best understood as a probe of entry into a deep boundary regime where single-mode compression is exhausted and collective cross-mode squeezing emerges. Because high-temperature bulk NMR obstructs strictly bipartite entanglement witnesses, we contextualize the signal within a macroscopic multiple-quantum-coherence (MQC) framework. Consequently, the current data provide evidence for the metric-driven onset of collective non-compact SU(1,1) structure, establishing the necessary physical foundation for future macroscopic many-body entanglement certification.

arXiv:2511.22828v2 Announce Type: replace Abstract: Understanding how nonlinear dynamical systems (e.g., artificial neural networks and neural circuits) process information requires comparing their underlying dynamics at scale, across diverse architectures and large neural recordings. While many similarity metrics exist, current approaches fall short for large-scale comparisons. Geometric methods are computationally efficient but fail to capture governing dynamics, limiting their accuracy. In contrast, traditional dynamical similarity methods are faithful to system dynamics but are often computationally prohibitive. We bridge this gap by combining the efficiency of geometric approaches with the fidelity of dynamical methods. We introduce fast dynamical similarity analysis (fastDSA), a computationally efficient and accurate metric for measuring (dis)similarity between nonlinear dynamical systems. FastDSA leverages modern computational tools, including random matrix theory to determine optimal system rank, novel optimization pipelines for aligning system flow fields, and Koopman embeddings. Across benchmark nonlinear systems and recurrent network models, fastDSA is robust to arbitrary coordinate choices while remaining sensitive to meaningful dynamical differences, capturing variations in system evolution that geometric methods may miss and traditional methods detect only at high computational cost. To our knowledge, fastDSA is the fastest method that retains accuracy in comparing nonlinear dynamical systems. It enables scalable, statistical analyses across diverse systems, significantly expanding the practical applicability of dynamical similarity analysis.

arXiv:2603.02491v2 Announce Type: replace-cross Abstract: As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *implemented* using belief states or world models, but not that such representations are required. We prove quantitative "selection theorems" showing that strong task performance (low *average-case regret*) forces world models, belief-like memory and -- under task mixtures -- persistent variables resembling core primitives associated with emotion, along with informational modularity under block-structured tasks. Our results cover stochastic policies, partial observability, and evaluation under task distributions, without assuming optimality, determinism, or access to an explicit model. Technically, we reduce predictive modeling to binary "betting" decisions and show that regret bounds limit probability mass on suboptimal bets, enforcing the predictive distinctions needed to separate high-margin outcomes. In fully observed settings, this yields approximate recovery of the interventional transition kernel; under partial observability, it implies necessity of predictive state and belief-like memory, addressing an open question in prior world-model recovery work.

arXiv:2603.16880v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) provides a non-invasive window into neural dynamics at high temporal resolution and plays a pivotal role in clinical neuroscience research. Despite this potential, prevailing computational approaches to EEG analysis remain largely confined to task-specific classification objectives or coarse-grained pattern recognition, offering limited support for clinically meaningful interpretation. To address these limitations, we introduce NeuroNarrator, the first generalist EEG-to-text foundation model designed to translate electrophysiological segments into precise clinical narratives. A cornerstone of this framework is the curation of NeuroCorpus-160K, the first harmonized large-scale resource pairing over 160,000 EEG segments with structured, clinically grounded natural-language descriptions. Our architecture first aligns temporal EEG waveforms with spatial topographic maps via a rigorous contrastive objective, establishing spectro-spatially grounded representations. Building on this grounding, we condition a Large Language Model through a state-space-inspired formulation that integrates historical temporal and spectral context to support coherent clinical narrative generation. This approach establishes a principled bridge between continuous signal dynamics and discrete clinical language, enabling interpretable narrative generation that facilitates expert interpretation and supports clinical reporting workflows. Extensive evaluations across diverse benchmarks and zero-shot transfer tasks highlight NeuroNarrator's capacity to integrate temporal, spectral, and spatial dynamics, positioning it as a foundational framework for time-frequency-aware, open-ended clinical interpretation of electrophysiological data.