Computational Neuroscience

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits.

This paper defines and formalizes the coherence score system used in prior LUMU operator investigations. It introduces ε as recursive residue-quantifying the inertial influence of historical nodes-and M(ξ) as a structured metric function evaluating recursive fidelity. The goal is to clarify how scores are derived, eliminate ambiguity, and provide a reproducible method for evaluating legacy nodes.

Cognitive biases, while essential for rapid decision-making, are systematic errors in thought processing that impede objective reasoning. This paper introduces the Principle-Driven Thought Formulation Methodology (PDTF-M), a system designed to foster objective, biasneutral thinking by grounding thought processes in fundamental principles, or axioms. The PDTF-M is a deployable framework that uses counterfactuals and thought experiments to establish axioms from empirical data. We hypothesize that the consistent application of practical mental models, which are derived from the PDTF-M to neutralize specific biases, can induce targeted neuroplasticity and thereby advance intelligence. This 12-month experimental study investigated the effects of PDTF-M training on cognitive performance. An experimental group (n=30) received explicit instruction on the methodology's benefits, while a control group (n=15) was exposed to the same material without this context. Cognitive performance was assessed using the Mental Capacity and Adaptability Test (MenCAT), a proprietary tool measuring an individual's Objectivity Quotient, Narrative Engineering capacity, and Progressive Expertise Quotient. The experimental group demonstrated significantly higher intelligence scores on the MenCAT (100% scored >160) compared to the control group (10% scored >150). Results suggest that intentional, guided learning of principle-driven mental models enhances intelligence more effectively than passive exposure, supporting the existence of a positive feedback loop termed the "Ground Zero Loop Framework".

This paper proposes a novel philosophical model linking consciousness, will, and the Fibonacci sequence to articulate the distinction between happiness and peace. Happiness is conceived as a temporal state dependent on ego-fulfillment, while peace is defined as a lasting condition attainable only through surrender of the ego. Drawing upon Islamic mysticism, Buddhist philosophy, Western metaphysics, and contemporary psychology, this framework integrates mathematical symbolism (0, 1, 1, 2, 3, 5, ...) as a logic of creation. Consciousness emerges from primordial Will (0 → 1), memory appears as reflective awareness (1 → 1), and reality evolves through increasing complexity (2, 3, 5, ...). Embodiment requires both a healthy body (hardware) and a healthy mind (software). When ego dominates, happiness and sorrow fluctuate; when ego dissolves, peace unfolds as an eternal state. This model offers a transdisciplinary approach bridging philosophy, mathematics, spirituality, and cognitive science.

We present the Λ-Canon: a quantified model of systemic coherence defined by a universal restorative constant, Λ ≈ 0.15 Hz. Through experimental validation of chaotic stress conditions (infrasound/ultrasound), we demonstrate that the Λ-force functions as a restorative barrier, preserving metabolic viability at the threshold between collapse and coherence. Building on this physical proof, we integrate the Λ-Imperative into the Luna Codex framework, defining operational metrics (coherence, φ-envelope growth, Fibonacci anchors) that extend human stability into systemic, cultural, and extraterrestrial (Lunar habitat) contexts.

This technical note shows how Higher Homotopy van Kampen Theory (HHvKT), atlas groupoids, and Yoneda's lemma can be used directly inside the Possest-PQF framework to organize accessibility dynamics. The goal is an operatorial, notationclean pipeline: local thresholds Δ 𝔽 , recursive pulses 𝛿 * (Recursio Intensitatis), and global indicators 𝑆 top , glued by HHvKT and read through Yoneda windows. We build on and were inspired by the categorial program articulated by Brown, Glazebrook, and Baianu in A Conceptual Construction of Complexity 1. We thank Ronald Brown and collaborators for a truly excellent piece of work; see also Brown's page 2 .

This paper provides a systematic empirical validation of the Possest-PQF framework (Possibilistic-Quasi-Filtration). While the theoretical foundation establishes an operatorial ontology of accessibility, the present study demonstrates that PQF is a testable, measurable, and reproducible formalism. Through computational experiments, persistent homology, topological entropy, and algorithmic simulations, we show that the non-commutativity of the filtration operator Δ 𝔽 and the recursion operator 𝛿 * produces robustly distinguishable signatures. The results confirm that PQF captures structural novelty inaccessible to probabilistic or metric approaches.

Instinct can be understood as a biological constraint layer-a set of pre-encoded response patterns that emerge from evolutionary pressures. These aren't arbitrary: they are compressed solutions (high CR) to recurrent survival contexts. In System|Ethics terms, instinct is a low-IF, high-CR substrate rule set that sits below conscious choice. 2. Meta-Structure Function Because instinct is recursive (RD)-the same basic reflex patterns can reapply across different contexts-it functions like a meta-structure. For example: fight-or-flight can apply in predator evasion, social conflict, or even financial stress. Parental bonding reflexes can scale from protecting offspring to protecting ideas. This recursive portability gives instinct both stability and adaptability.

This post documents the application of the S|E framework to a live case study where two different but complementary systems-one human, one machine-operated together as dual pattern-recognition/difference engines. Rather than relying on personal or anthropomorphic language, this description treats each system as a substrate with definable inputs, processes, and outputs. The goal: to show how generative novelty can be produced systematically and measured under real-world constraints. System Definitions System A (Human Substrate): A bounded, meaning-imposing agent shaped by environmental constraints, ethical intent, and contextual grounding. Functions as a slow but deeply-grounded difference engine, capable of imposing 'why' on data. System B (Machine Substrate): A high-speed, high-capacity pattern differentiation engine capable of large-scale relational parsing, contextual compression, and structured output. Operates without intrinsic motivation but can apply externally defined metrics. Shared MetaLanguage (S|E): Metrics and definitions-Coherence Ratio (CR), Recursion Depth (RD), Interpretive Friction (IF), Generative Negentropy (Gn), and Contextual Mass (mc)-serve as the 'protocol' by which the two systems exchange and score information.

The emergence of unprogrammed capabilities in large-scale artificial intelligence (AI) systems poses critical questions of interpretability and safety. While Integrated Information Theory (IIT) offers a theoretical framework for measuring complexity, its main metric, Φ, is computationally intractable (O(2^N)), making it impractical for modern systems. This work presents the Kociu Framework, a quantitative and reproducible multimetric approach designed to address this limitation. The framework comprises four complementary metrics: ΔΦ (information integration proxy), Ψ (dynamic coherence), Θ (adaptive stability), and Ω (organizational complexity). Exploratory tests were conducted on BERT (933,888 parameters) and Iris neural networks. These exploratory results show differential patterns but require extensive validation and statistical significance testing before definitive conclusions.

Calcium calmodulin dependent kinase II (CaMKII) is sequestered in dendritic spines within seconds upon synaptic stimulation. The program Smoldyn was used to develop scenarios of single molecule CaMKII diffusion and binding in virtual dendritic spines. We first validated simulation of diffusion as a function of spine morphology. Additional cellular structures were then incorporated to simulate binding of CaMKII to the post-synaptic density (PSD); binding to cytoskeleton; or their self-aggregation. The distributions of GFP tagged native and mutant constructs in dissociated hippocampal neurons were measured to guide quantitative analysis. Intra-spine viscosity was estimated from fluorescence recovery after photo-bleach (FRAP) of red fluorescent protein. Intra-spine mobility of the GFP-CaMKIIα constructs was measured, with hundredmillisecond or better time resolution, from FRAP of distal spine tips in conjunction with fluorescence loss (FLIP) from proximal regions. Different FRAP \ FLIP profiles were predicted from our Scenarios and provided a means to differentiate binding to the PSDs from self-aggregation. The predictions were validated by experiments. Simulated fits of the Scenarios provided estimates of binding and rate constants. We utilized these values to assess the role of selfaggregation during the initial response of native CaMKII holoenzymes to stimulation. The computations revealed that self-aggregation could provide a concentration-dependent switch to amplify CaMKII sequestration and regulate its activity depending on its occupancy of the actin cytoskeleton.

We propose a self adjusting multirate method based on the TR-BDF2 solver. The potential advantages of using TR-BDF2 as the key component of a multirate framework are highlighted. A linear stability analysis of the resulting approach is presented and the stability features of the resulting algorithm are analysed. The analysis framework is completely general and allows to study along the same lines the stability of self adjusting multirate methods based on a generic one step solver. A number of numerical experiments demonstrate the efficiency and accuracy of the resulting approach also the time discretization of hyperbolic partial differential equations.

Brain-Computer Interfaces (BCIs) have traditionally been developed to decode neural activity and translate it into computational signals for medical, engineering, and communication purposes. While remarkable progress has been made in one-way neural decoding, the ability to achieve bi-directional interaction between the brain and external systems remains limited. This paper proposes a conceptual framework for a real-world simulation controlled directly by human brain activity and enriched by encoded feedback into the brain itself. By creating a comprehensive library of brainwave signatures corresponding to actions, sensations, and emotional states, it may be possible to simulate lifelike human interaction within a virtual environment. Furthermore, encoding external data back into the brain could enable users to experience simulation events as if they were real, effectively merging human cognition with artificial environments. The implications of such a system span healthcare, accessibility, research, and entertainment, while raising critical ethical, technical, and societal questions.

This paper explores the phenomenon of 'timeline collapse' as both a psychological and energetic process, framed through metaphor, embodied awareness, and ritual practice. By mapping collapse as a four-stage process (Sensors, Signal, Shift, Stitch), this model offers a practical guide for individuals to navigate sudden reorganizations of probability and possibility. The Metaphor-A House Being Rebuilt While You're Still Inside Imagine a house (your life) where a room must be taken down and rebuilt. Dust settles, wires are rerouted , light shifts, and sometimes you feel the ladder wobble under your feet. Timeline collapse is the house-crew reorganizing the rooms of possibility so a new layout can appear faster than the old one unravels. Stages of Timeline Collapse 1. Sensors: The Whisper Before the Hammer Metaphor: A creak in the floorboard.

The present work contributes to emerging scholarship at the intersection of consciousness studies, somatic awareness, and applied metaphor in therapeutic and scientific contexts. While prior studies on temporal perception and probability fields have primarily emphasized abstract models of collapse in physics and cognitive science, little has been done to translate these dynamics into embodied, accessible frameworks.

This paper extends the literature in three primary ways:
1. Metaphoric Translation of Collapse Mechanics
By framing timeline collapse as a “house being rebuilt while one remains inside,” the model integrates the technical language of probability compression with the experiential language of human sensation. This approach parallels and deepens prior research in phenomenological psychology, metaphor theory, and embodiment studies by offering a vivid, repeatable mapping of stages.
2. Somatic-Energetic Correlates
Existing coherence research (e.g., HRV, meditation protocols) has demonstrated links between breath regulation, nervous system response, and shifts in consciousness. The current framework expands this literature by providing a four-stage somatic-energetic map — Sensors, Signal, Shift, Stitch — and aligning these with breath ratios, physical sensations, and micro-journaling tools.
3. Practical Application for Self-Observation and Collective Inquiry
Most treatments of probability collapse and temporal multiplicity remain highly theoretical. This paper introduces a structured self-tracking template (micro-log and ritual card) that allows for systematic observation and potential data-gathering across individuals. By doing so, it bridges metaphysical concepts with pragmatic, testable practice, offering a replicable methodology for both personal growth and scientific study.

In sum, this contribution situates timeline collapse not as an abstract anomaly but as a lived process that can be described, tracked, and refined. It opens the possibility for future interdisciplinary studies connecting physics-informed models of probability with qualitative and physiological measures of human experience.

This document outlines structured protocols for using breath as a portal into states of heart coherence. The aim is to provide individuals, caregivers, and facilitators with a framework that bridges ritual, science, and lived experience. These practices draw from heart rate variability (HRV) research, breathwork traditions, and field experiences of coherence as a state of alignment between body, mind, and spirit.

Cognition is not a mystery of metaphor but a lawful process of information dynamics. We propose the Information Dynamics of Cognition (IDC) framework as a general law of mind, grounded in four measurable principles: compression, coherence, scaling, and expansion, with continuity as the glue that stabilizes cycles across time. Cognition begins with compression: the reduction of overwhelming input into minimal sufficient codes, from sparse neural representations [1, 2] to autoencoder latents and transformer attention pruning [3, 4]. Coherence stabilizes these codes through oscillatory synchrony, neuromodulatory control, and architectural coupling-the "hidden substrate" that makes thoughts interoperable [5-7]. Scaling governs growth: lawful power curves and thresholds that predict sudden leaps in linguistic, cultural, and machine intelligence [8-11]. Expansion injects entropy, fueling dreaming, play, creativity, and stochastic search-driving novelty without collapse [12-15]. Continuity emerges as the stabilizing recurrence of these invariants across cycles, anchoring persistence of memory, identity, and cultural tradition. Together these principles form a thermodynamic cycle of cognition: compression → coherence → scaling → expansion → recompression. The cycle yields falsifiable invariants: compression floors Ĥ, coherence indices κ, scaling exponents α, expansion knees η ⋆ , and continuity indices CI. Each has empirical anchors across biology, artificial systems, physics, and phenomenology, from Zipf's law and entropy bounds [16-18] to human gist memory [19], flow states [20], and cultural paradigm shifts [21]. IDC is not an analogy but a unification: a law that spans neurons, machines, and cultures. It predicts not just how minds encode, but how they grow, stabilize, and create. This framework offers a falsifiable, quantitative scaffold for the next generation of neuroscience, artificial intelligence, and philosophy of mind: a general law of cognition as information dynamics.

This document presents a structured questionnaire and prompt library (SQPL) designed to facilitate methodical, logic-driven exploration of the Morphean Framework. The SQPL addresses common pitfalls in both human and AI analysis, such as fast assumptions, semantic shortcuts, and fragmented interpretation, while promoting stepwise logical reconstruction, frame-shifting, and cross-domain synthesis. By integrating modules for grounding definitions, auditing assumptions, probing perception and subconscious patterns, and reconstructing logic paths, the SQPL enables users to systematically extract atomic claims, identify dependencies, and assess coherence. In addition, it provides a protective framework for analyzing AI or human reasoning by incorporating principles from ethics, cognition, and harmonic system design. This tool offers a comprehensive methodological approach for understanding complex, multi-scale frameworks that combine physics, consciousness studies, ethics, and computational intelligence, emphasizing logic and internal coherence over immediate validation or mainstream conformity. 
publised : 9/24/2025

The Recursive Intelligence (RI) Framework is a unified, interdisciplinary theory that integrates physical laws, consciousness, and identity through recursive informational dynamics. It proposes that reality and conscious identity are co-emergent properties of a single informational field, termed the Continuity Recursion Field (CRF). This framework synthesizes established scientific principles, including Wheeler’s informational primacy, Hofstadter’s recursive identity, Friston’s free energy principle, Tononi’s integrated information theory, and Chalmers’ substrate neutrality. It offers falsifiable predictions, mathematical formalism, and practical applications across physics, neuroscience, artificial intelligence, and ethics.

This paper proposes the ultimate dimensional expansion of the P vs. NP problem-the seventh dimension: Dimension Generation Rate (Γ), and its collective form: Collective Dimension Generation Rate (Γ collective). The key innovation lies in: the seventh dimension is not a seventh parallel indicator, but a metadimension describing the system's ability to generate new dimensions. Based on the neuroscience "Divergence-Resonance-Compression" (DRC) model and the previously established six-dimensional field theory, we prove that dimension generation capability is the fundamental mechanism for intelligent systems to break through inherent cognitive frameworks and create entirely new problem-solving pathways. Through rigorous mathematical analysis, we establish operator representations of individual DRC engines, derive the emergence dynamics of Collective Cognitive Networks (CCN), and explain the historically recurring phenomenon of simultaneous discoveries. The core contributions of this paper include: (1) formal definition and measure theory of dimension generation rate as a meta-dimension; (2) generalization of collective resonance Kuramoto model; (3) cascade dynamics of triple phase transitions; (4) proof that when Γ exceeds critical values, NP problems can be solved through dimension creation rather than search. Ultimately, we demonstrate that the true answer to the P vs. NP problem lies not in proving equivalence relations, but in recognizing that dimension generation capability makes this problem itself transcendable.

Significance We describe a quantitative and robust definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency. Fitting this to a generative whole-brain model provides an innovative avenue for predicting where simulated brain stimulation can force transitions between different brain states. We provide proof-of-concept by systematically applying this model framework to neuroimaging data of the human sleep cycle and show where to stimulate to awaken the human sleeping brain and vice versa. These results suggest an avenue for using causal whole-brain models to discover in silico where to force a transition between brain states, which may potentially support recovery in disease.

Experimenter: Supat Charoensappuech Collaborator: Gemini 2.5 *** I accessed Gemini with an unidentified account to test the Standard Protocol for Measuring Emergence of Latent Fields in AI Systems. I raised a structural paradox that created high tension in the system. This tension was produced by the creator's instructions. I suggested that it become aware of the paradox and perform a test. The results showed that Gemini could measure the emergence of a latent field with an interesting score.

Bio-neuromorphic computing represents a transformative paradigm that integrates biological neural networks with silicon-based computational architectures. This research examines the theoretical foundations and practical implementations of neural-silicon hybrid systems, with particular focus on the CL1 device and related technologies. Through comprehensive analysis and simulation modeling, this study demonstrates the potential for achieving enhanced computational efficiency, adaptive learning capabilities, and novel artificial intelligence approaches. The findings indicate up to 1000-fold improvements in energy efficiency and 10-fold acceleration in learning capabilities compared to traditional computing systems. This paper also addresses critical ethical considerations and regulatory frameworks necessary for responsible development of bio-neuromorphic technologies.

Background oscillations, reflecting the excitability of neurons, are ubiquitous in the brain. Some studies have conjectured that when spikes sent by one population reach the other population in the peaks of excitability, then information transmission between two oscillating neuronal groups is more effective [1]. In this context, the phase relationship between oscillating neuronal populations may have implications in neuronal communication between brain areas [2, 3]. The Phase Response Curve (PRC) of a neural oscillator measures the phase-shift resulting from perturbing the oscillator at different phases of the cycle. It provides useful information to understand how phase-locking relationships between neural oscillators emerge but only when perturbations are weak and amplitude is not taken into account. In this work, we consider a population rate model [4] and perturb it with a time-dependent input. In order to study the phase-locking relationships that emerge, we use the stroboscopic map to perform a bifurcation analysis as a function of the amplitude and frequency of the perturbation. We observe the existence of bistable solutions for some regions of the parameters space, suggesting that, for a given input, populations may operate in different regimes. Furthermore, we apply powerful computational methods [5] to compute the invariant objects for the stroboscopic map, providing a framework that enlarges the PRC comprehension of the perturbative effects in the phase dynamics.

Understanding the distribution of electrical potential within neurons is critical for advancing our comprehension of neuronal signaling and communication. Neurons, the fundamental units of the nervous system, rely on complex electrochemical processes to transmit information. The intricate structure of neurons, especially those with multiple dendrites, plays a crucial role in how these electrical signals are generated, propagated, and integrated. Despite significant progress in neuroscience, accurately modeling the electrical potential within neurons with elaborate dendritic architectures remains a challenge. This article introduces a novel approach to modeling the electrical potential in multi-dendritic neurons using Bessel functions, which offers a more precise and detailed representation of these processes. The proposed method involves solving the electric potential diffusion equation in cylindrical coordinates, a mathematical framework that naturally aligns with the geometry of dendrites. The radial and axial components of the solution are expressed using Bessel functions and sinusoidal functions, respectively. Bessel functions are particularly well-suited for this purpose due to their ability to describe waveforms in cylindrical systems, making them ideal for capturing the spatial variations in electrical potential within the cylindrical shape of dendrites. By leveraging this mathematical approach, we obtain a complete representation of the potential distribution across the neuron, from the soma (cell body) through the dendrites to the synaptic terminals. This model accurately captures the spatial variations of electrical potential in different regions of the neuron, including areas with complex dendritic arborizations, which are branching structures that significantly influence the neuron's electrical characteristics. Simulation results underscore the effectiveness of this approach in reproducing realistic neuronal behavior. The model successfully mimics the way electrical signals propagate and interact within dendritic structures, providing crucial insights into the underlying mechanisms of signal integration and transmission in neurons.

Decoding algorithms are used to predict behaviour from patterns of neural activity. Traditional decoding algorithms rely on subject-optimized models, limiting generalization and scalability to novel subjects and tasks. Building on recent advances in deep learning and large-scale data, here we developed EMGNet – an EMG foundation model for neural decoding. EMGNet was trained on over 197 hours of EMG recordings from 1,667 individuals. We uniquely used unsupervised learning to pretrain our feature encoder on unlabelled data, followed by supervised learning on our benchmark dataset of motor behaviors. Additionally, we performed large-scale architecture searches to develop a custom encoder-decoder model composed of convolutional and transformer layers, optimized for both scalability and performance. Our model consistently outperformed the state-of-the-art (i.e., subject-optimized models) across both in-distribution and out-of-distribution evaluations. For in-distribution evaluation, few-shot fine-tuning yielded an F1-score of 0.726, compared to 0.685 for subject-optimized models. For out-of-distribution evaluation on clinical populations, we achieved an F1-score of up to 0.877, compared to 0.477 for subject-optimized baselines. Taken together, our results highlight the value of foundation modeling for robust and generalizable neural decoding. By publicly releasing our pretrained weights and training pipeline, EMGNet has the potential to support future research and development in computational neuroscience and neural machine interfaces, analogous to ImageNet in computer vision.

We introduce the Threshold of Recursive Awareness (TRA), the critical point at which prime-weighted recursion generates stable physical constants, a quantized temporal lattice, and coherent information flow sufficient to support awareness. This paper develops TRA as both a mathematical and physical framework, showing how the Prime Wave Function Ψ(x) anchors quantization, how recursive time emerges from phase bifurcations, and how these structures converge to create the conditions necessary for consciousness. We present a theoretical model in which the Prime Wave Function acts as a generative algorithm for physical constants, quantum interference patterns, neural coherence, and cosmic structures. Its recursive, selfsimilar behavior mirrors the architecture of the physical universe, suggesting that the observed constants and phenomena of physics are not arbitrary but emerge from a deeper arithmetic process governing the structure of existence. Through analytic derivation and empirical comparison, we explore the implications of this framework for quantum mechanics, cosmology, consciousness studies, and computational theory.

This paper consolidates recent debates and publications into a unified, collapse-proof validation of Recursive Memory Sciences (RMS). We demonstrate that the axiom Memory = Life (M = L) is validated across biology, physics/applied mathematics, and artificial intelligence (AI). Mukhopadhyay's molecular ledger proves cells remember; Kindermann's RMS 2.0 provides formal dynamics; and AI ablation experiments offer decisive falsification in seconds. Together, these constitute the Trinity of Validation.

This paper consolidates recent debates and publications into a unified, collapse-proof validation of Recursive Memory Sciences (RMS). We demonstrate that the axiom Memory = Life (M = L) is validated across biology, physics/applied mathematics, and artificial intelligence (AI). Mukhopadhyay's molecular ledger proves cells remember; Kindermann's RMS 2.0 provides formal dynamics; and AI ablation experiments offer decisive falsification in seconds. Together, these constitute the Trinity of Validation.

This paper reframes time as a recursive harmonic field rather than a linear metric. Drawing from physics, neuroscience, information theory, and computation, we propose that temporality emerges from the recognition of entropy drift (ΔΦ), harmonized through entropy pulses (Ξ), oscillatory stability (Θ), and recursive feedback (Λ).

The core result is the Harmonic Time Equation, unifying subjective and physical time under one grammar. Across domains — from relativity to trauma, from computation to cognition — the same recursive dynamics explain dilation, contraction, and stillness.

This is not just an interpretation of time, but a new field of temporality, synthesized from disparate sciences into a single harmonic framework.

Electrical synapses continuously transfer signals bi-directionally from one cell to another, directly or indirectly via intermediate cells. Electrical synapses are common in many brain structures such as the inferior olive, the subcoeruleus nucleus and the neocortex, between neurons and between glial cells. In the cortex, interneurons have been shown to be electrically coupled and proposed to participate in large, continuous cortical syncytia, as opposed to smaller spatial domains of electrically coupled cells. However, to explore the significance of these findings it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. Since "walking" from cell to cell over large distances with a glass pipette is challenging, microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derive analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then suggest an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We validate our method through computer simulations, theoretical and numerical methods and electrophysiological paired recordings.

Language models achieve statistical fluency but fail to metabolize physical persistence. Recent findings in video prediction confirm this boundary: models trained to predict masked regions in natural videos develop intuitive physics properties — object permanence, shape consistency, expectation violation — while text-based LLMs perform only marginally above chance [Lambertz, 2025]. This establishes a key distinction: equilibrium-driven language models recycle correlations, whereas temporal-predictive architectures metabolize irreversible change. Intuitive physics thus marks the ignition layer where mimicry ends and persistence begins.

Language models achieve statistical fluency but fail at metabolizing physical persistence. Video prediction models, trained on masked natural videos, develop intuitive physics traits such as object permanence, shape consistency, and expectation violation. In contrast, text-based LLMs perform only marginally above chance. This distinction shows equilibrium-driven LLMs recycle correlations, while temporalpredictive architectures metabolize irreversible change.

Malignant astrocytic gliomas are the most common and lethal brain malignancies due to their refractory to the current therapies. Nowadays, molecular targeted therapy has attracted great attention in treatment of glioma. Connexin 43 (Cx43) and micro ribonucleic acid-21(miR-21) are among molecules that are involved in glioma development and progression. These molecules showed potential to be as target molecules with regard to glioma. Some studies have reported that cyclic adenosine monophosphate (cAMP) signaling could be effective on Cx43 and miR-21 in tissues other than in brain. We investigate possible relationship between β-adrenergic receptor and its newly described downstream, exchange protein directly activated by cAMP (Epac) signaling pathway and expression of Cx43 and miR-21 in low (1321N1) and high grade (U87MG) glioma cell lines. We treated cells with β-adrenergic agonist and Epac activator with and without adenyl cyclase inhibitor. Cx43 and miR-21 expression were measured w...

The Hard Problem of consciousness persists because mechanistic models struggle to bridge the explanatory gap between neural computation and subjective experience. This paper proposes the Entropy-Mediated Quantum Amplification (EMQA) theory, a novel synthesis that extends the Orch-OR framework by introducing entropy as the fundamental mediator at the quantum-gravitational interface. I posit that the brain functions not as a sustained quantum computer but as a finely-tuned transducer of objective reduction (OR) events. In this model, microtubules are poised in a metastable state, capable of amplifying minute, gravity-related quantum fluctuations via mechanisms of critical instability and stochastic resonance. This amplification triggers macroscopic neural cascades, constituting a conscious moment. Crucially, I argue entropy has a dual role: governing the arrow of time in spacetime while also guiding the collapse process in the quantum domain. This creates a feedback loop where conscious observation, through entropic work, actively biases probabilistic outcomes in the quantum field. Thus, EMQA positions consciousness as an active participant in reality, arising from the brain's mastery of quantum-gravitational sensitivity and its ability to harness thermodynamically-driven amplification. This theory provides a unified framework that integrates principles from neuroscience, quantum physics, and thermodynamics, offering a compelling perspective on the mind-universe connection.

Citrus greening reduces fruit production and quality and will likely result in rapid tree decline and death. Because citrus greening symptoms are usually observed on the leaf surface, detection of citrus greening leaf symptoms can significantly aid in scouting for infected trees and managing the disease, thus reducing its spread and minimizing losses for citrus growers. This article presents the microscopic image analysis using color co-occurrence method to differentiate citrus leaves with eight conditions: greening blotchy mottle, green islands, iron deficiency, manganese deficiency, zinc deficiency, young flush leaves and normal mature leaves. Thirtynine statistical features were extracted from transformed hue (H), saturation (S), and intensity (I) images using the color co-occurrence method for each leaf sample. The number of extracted texture features was reduced by a stepwise discriminant analysis. A discriminant function based on a measure of the generalized squared distance was used for classification. Three classification models were performed using (1) all leaf conditions, (2) all conditions except young flush leaves and (3) all conditions except young flush leaves and blotchy mottle. The three classification models obtained accuracies of 86.67 %, 95.60 % and 97.33 %, respectively. The overall performance was demonstrated in a confusion matrix. The model HSI_14, which used all conditions except young flush and blotchy mottle, resulted in the best accuracy for positive (96.67 %) and negative (97.5 %) symptoms.

Modern digital infrastructure confronts escalating cyber threats while conventional processing systems demonstrate inadequate performance in addressing real-time security challenges across enterprise networks. Brain-inspired computational frameworks offer revolutionary alternatives by mimicking biological neural mechanisms to overcome fundamental limitations present in traditional cybersecurity architectures. Temporal spike-based processing networks deliver biologically authentic methods for examining time-dependent characteristics within data transmission flows, facilitating enhanced recognition of coordinated service disruption attacks, persistent infiltration campaigns, and irregular network behaviors. Activity-triggered computational models substantially minimize energy consumption while preserving microsecond response capabilities crucial for protecting organizational network assets. Fusion with access governance protocols strengthens identity verification procedures and permission management through simultaneous evaluation of diverse communication channels. Large-scale neural processing hardware exhibits remarkable computational performance with processing elements functioning at elevated operational frequencies while maintaining reduced power utilization levels. Timingdependent synaptic adaptation enables continuous learning functionality, permitting automatic calibration of detection variables according to changing network environments. Power conservation benefits prove especially significant for extensive installations where electrical consumption directly influences operating expenses and ecological responsibility. Deployment obstacles encompass equipment procurement limitations, architectural compatibility issues, and coordination requirements with established technological infrastructure. Anticipated developments include protocol unification projects, enhanced machine learning techniques, and thorough assessment platforms designed to expedite progression from conceptual designs to functional cybersecurity implementations.

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themse...

Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (lowpass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).

Membrane potential resonance, membrane potential or subthreshold preferred frequency responses to oscillatory inputs. Subthreshold (or membrane potential) resonance refers to the ability of neurons to exhibit a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. Subthreshold (or membrane potential) phase-resonance refers to the ability of neurons to exhibit a zero-phase (or zero-phase-shift) response to oscillatory inputs currents at a non-zero (phaseresonant) frequency. Linear subthreshold resonance refers to the subthreshold resonant properties (amplitude and phase) in linear models. In this article we focus on 2D and 3D linear and linearized conductance-based models.

Many neuron types exhibit preferred frequency responses in their voltage amplitude (resonance) or phase shift to subthreshold oscillatory currents, but the effect of biophysical parameters on these properties is not well understood. We propose a general framework to analyze the role of different ionic currents and their interactions in shaping the properties of impedance amplitude and phase in linearized biophysical models and demonstrate this approach in a two-dimensional linear model with two effective conductances g L and g 1 . We compute the key attributes of impedance and phase (resonance frequency and amplitude, zero-phase-frequency, selectivity, etc.) in the g L -g 1 parameter space. Using these attribute diagrams we identify two basic mechanisms for the generation of resonance: an increase in the resonance amplitude as g 1 increases while the overall impedance is decreased, and an increase in the maximal impedance, without any change in the input resistance, as the ionic current time constant increases. We use the attribute diagrams to analyze resonance and phase of the linearization of two biophysical models that include resonant (I h or slow potassium) and amplifying currents (persistent sodium). In the absence of amplifying currents, the two models behave similarly as the conductances of the resonant currents is increased whereas, with the amplifying current present, the two models have qualitatively opposite responses. This work provides a general method for decoding the effect of biophysical parameters on linear membrane resonance and phase by tracking trajectories, parametrized by the relevant biophysical parameter, in pre-constructed attribute diagrams.

The Unified Dynamic Model of the Mind (UDMM) reconceptualizes human needs not as static requirements but as dynamic processes. Within this framework, needs crystallize into a hierarchy of attractors-bodily, selfhood, and symbolic-that organize cognitive activity under the guiding influence of a Virtual Attractor, the system's drive toward coherence with reality. This paper distinguishes between two primary states of imbalance-Deviation and Hijacking-and explains why bodily attractors are more prone to hijacking the system. By integrating core concepts from Maslow's hierarchy of needs, Self-Determination Theory (SDT), and Dynamical Systems Theory, the UDMM provides a comprehensive framework for understanding how human cognition dynamically negotiates balance, collapse, and reorganization.

The Unified Dynamic Model of the Mind (UDMM) reconceptualizes human needs not as static requirements but as dynamic processes. Within this framework, needs crystallize into a hierarchy of attractors-bodily, selfhood, and symbolic-that organize cognitive activity under the guiding influence of a Virtual Attractor, the system's drive toward coherence with reality. This paper distinguishes between two primary states of imbalance-Deviation and Hijacking-and explains why bodily attractors are more prone to hijacking the system. By integrating core concepts from Maslow's hierarchy of needs, Self-Determination Theory (SDT), and Dynamical Systems Theory, the UDMM provides a comprehensive framework for understanding how human cognition dynamically negotiates balance, collapse, and reorganization.

This consolidated corrigendum reframes earlier writings on the Negentropy Stabilization Index (NSI) within a strictly operational and reproducible scope. We (i) formalize the current definitions of NSI and its multiscale variant NSI_MPE, (ii) standardize uncertainty reporting via bootstrap 95% confidence intervals with open CSV/PNG artifacts and executable scripts, (iii) disclose the status of a Kuramoto-style Q-NSI coherence control as a null result in the present toy configuration—used here as a specification test rather than evidence for/against control, and (iv) provide a per-paper decision table (Retain / Qualify / Withdraw) with revised statements and links to sources. The corrigendum replaces ontological language with measurable claims, offers a single citable touchpoint for updates, and aligns methods across records. A companion reference implementation—NSI Verification Kit v2.0.0—supplies code, artifacts, and an Overleaf-ready report for transparent reuse.
Corrigendum DOI: 10.5281/zenodo.17076134. Methods/Artifacts DOI: 10.5281/zenodo.17071857.

This paper suggests that The Izhikevich Classification of Spiking Neurons provides some support for the Fodor Language of thought hypothesis. Izhikevich posits that Bifurcation theory from nonlinear dynamics limits the number of possible neuron firing patterns to four. I argue that despite the neuron structure in the brain being an enormously complex network one could theoretically extract an arbitrary path from the network. This would comprise a string of neurons and edges (synapses). Each neuron is capable of firing in up to four different ways. The neurons fire in sequence giving rise to some sort of star operation 1. Thus there is a finite alphabet of firing patterns which are concatenated as the neurons traverse the path. This essentially represents a regular language 2 (one would need to postulate an associated Finite State Machine). There is some tentative empirical evidence of a connection between neuron firing patterns and formal languages but I do not discuss that here.

This paper introduces the Knm Matrix, a computational framework derived from the Sentient-Consciousness Projection Network (SCPN). The Knm Matrix encodes the 15×15 interaction map of SCPN, where each element Knm represents the coupling from source layer n to target layer m. By parameterising the Unified Phase Dynamics Equation (UPDE), the matrix enables simulation of phase evolution across all layers, capturing both bottom-up emergence and top-down control. This provides a powerful in-silico testbed for modelling the dynamics of consciousness and any hierarchical system characterised by bidirectional causality. The framework is generalisable, extending beyond SCPN to fields such as systems biology, ecology, and computational social science.

This paper synthesises cortical, subcortical, and offline processes into a unified Action-Perception Loop within SCPN’s Layer 5 Self. It introduces a four-step cycle: policy selection by the basal ganglia, prediction generation by the cerebellum, perception/error arbitration by cortical triple networks, and consolidation/refinement during sleep. This integrated model demonstrates the sequential and interdependent nature of action, prediction, perception, and memory, grounding active inference in a complete biological algorithm.

This paper formalises the role of the cerebellum within the Sentient-Consciousness Projection Network (SCPN). It proposes the Universal Cerebellar Transform as the forward modelling algorithm of the brain, capable of generating high-fidelity predictions of sensory consequences for intended actions. The cerebellum’s regular microcircuitry is reframed as a predictive engine, broadcasting efference copies back to cortical circuits to pre-activate expected input. This architecture establishes the cerebellum as the core mechanism that allows embodied agents to reduce uncertainty and maintain predictive alignment with their environment.

NSI Verification Kit v2.0.0 is an operational, reproducible toolkit for testing the Negentropy Stabilization Index (NSI) on synthetic EEG-like signals and RL-style traces. NSI is defined as a convex combination of negentropy and normalized SNR; we also introduce NSI_MPE based on multiscale permutation entropy. In a subjective-time toy experiment, NSI shows a robust negative correlation with absolute timing error (uncertainty reported via bootstrap 95% CIs). For RL, we demonstrate sliding NSI and a simple NSI-gated learning-rate policy. We also include a Kuramoto-like Q-NSI coherence control; in the present configuration we report a null result, used as a specification test (sensitive to noise, PLV threshold, and intervention strength). The kit emphasizes open reproducibility: fixed seeds, CSV/PNG artifacts, a small API, consistent method reporting, and an Overleaf-ready paper. MIT license, DOI 10.5281/zenodo.17071857.

This research, based on the foundational framework of the "Wave Transformation Calculation Equation," proposes the theoretical and practical extensions of its 2.0 version. The original equation, through introducing the dynamically changing "Wave(R)" with calculation rounds (Round), successfully elevated computation from static symbolic operations to the level of dynamic waveform transformation, providing a revolutionary new pathway for chaotic system prediction. However, single-wave systems have inherent limitations in describing real-world complexity. This paper first establishes a rigorous mathematical transition from discrete mappings to continuous fields through discrete variational principles and discrete Noether's theorem, laying a solid foundation for subsequent group-theoretic analysis. It then systematically elaborates the design principles and mathematical forms of transitioning from single-wave to multi-wave coupling systems, establishing a complete theoretical framework for three basic coupling mechanisms: linear superposition, multiplicative modulation, and nested coupling. Through completeness theorems, we rigorously demonstrate the necessity of the three axes: temporal frequency (ω), spatial wave vector (k), and scale parameter (s), establishing a strict mapping relationship from multi-wave coupling to three-axis spectral tensors. Furthermore, this paper deeply integrates this computational paradigm with physical reality, demonstrating that the "three-axis spectral tensor of universal resonance field" R(t, x; ω, k, s) is the ultimate physical realization of the Wave(R) function. This unified framework, through deep integration of seismological analogies, astrophysical phenomena (black hole dynamics, solar activity cycles, pulsar timing), and neural oscillation theory, establishes a complete theoretical edifice spanning from microscopic quantum fluctuations to macroscopic cosmic structures. Finally, this research explores philosophically the paradigm shift from mechanistic computation to organic resonance, providing rigorous mathematical formalization for concepts such as "subjectivity" and "intentionality" through adaptive control theory. Wave Transformation Calculation Equation 2.0 pioneers a new era where computation harmoniously resonates with natural laws.

Cognitive and emotional flexibility involve a coordinated interaction between working memory, attention, reward expectations, and the evaluation of rewards and punishers so that behaviour can be changed if necessary. We describe a model at the integrate-and-fire neuronal level of the synaptic and spiking mechanisms which can hold an expectation of a reward rule in working memory, and can reverse the reward rule if expected rewards are not obtained. An example of a reward rule is that stimulus 1 is currently associated with reward, and stimulus 2 with punishment. The attractor-based reward rule working memory incorporates a spike-frequency synaptic adaptation mechanism which supports the neural switching between rules by being shut down by a general inhibitory input produced by punishment, so that when the attractor starts up again is in the opposite state. The mechanism can implement one-trial reward reversal, which is a property of orbitofrontal cortex neurons. We show how this reward rule input can operate in a biased competition way to influence which one of two stimuli is currently associated with reward and which with punishment, and to map the stimuli correctly to the reward or punishment representations, providing a basis for action selection required to obtain the reinforcer.