# brain: meditative absorption shifts brain dynamics toward criticality.

<< ️Criticality describes a regime between order and chaos that supports flexible yet stable information processing. Here (AA) examine whether neural dynamics can be volitionally shifted toward criticality through the self-regulation of attention. >>

<< (They) examined ten experienced practitioners of meditation during a 10-day retreat, comparing refined states of meditative absorption, called the jhanas, to regular mindfulness of breathing. (They) collected electroencephalography (EEG) and physiological data during these practices and quantified the signal's dynamical properties using Lempel-Ziv complexity, signal entropy, chaoticity and long-range temporal correlations. In addition, (They) estimated perturbational sensitivity using a global auditory oddball mismatch negativity (MMN) during meditation. >>

<< ️Relative to mindfulness, jhana was associated with pronounced self-reported sensory fading, slower respiration, higher neural signal diversity across multiple measures, reduced chaoticity, and enhanced MMN amplitude over frontocentral sites. Spectral analyses showed a flatter aperiodic one over f component and a frequency-specific reorganization of long-range temporal correlations. Together, increased diversity with reduced chaoticity and heightened deviance detection indicate a shift toward a metastable, near-critical regime during jhana. >>

<< ️(AA) propose an overlap of the phenomenology of jhana with minimal phenomenal experiences in terms of progressive attenuation of sensory content with preserved tonic alertness. Accordingly, (Their) findings suggest that criticality is a candidate neurophysiological marker of the absorptive, minimal-content dimension of the minimal phenomenal experience. >>

Jonas Mago, Joshua Brahinsky, Mark Miller, et al. Meditative absorption shifts brain dynamics toward criticality. arXiv: 2511.20990v1 [q-bio.NC]. Nov 26, 2025.

https://arxiv.org/abs/2511.20990

Also: brain, Zen, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, Zen, criticality, transitions, meditation, meditative absorption, jhanas, breathing mindfulness.

# gst: emergence of chimeras states in one-dimensional Ising model with long-range diffusion.

<< ️In this work, (AA) examine the conditions for the emergence of chimera-like states in Ising systems. (They) study an Ising chain with periodic boundaries in contact with a thermal bath at temperature T, that induces stochastic changes in spin variables. To capture the non-locality needed for chimera formation, (They) introduce a model setup with non-local diffusion of spin values through the whole system. More precisely, diffusion is modeled through spin-exchange interactions between units up to a distance R, using Kawasaki dynamics. This setup mimics, e.g., neural media, as the brain, in the presence of electrical (diffusive) interactions. >>

<< ️(AA) explored the influence of such non-local dynamics on the emergence of complex spatiotemporal synchronization patterns of activity. Depending on system parameters (They) report here for the first time chimera-like states in the Ising model, characterized by relatively stable moving domains of spins with different local magnetization. (They) analyzed the system at T=0, both analytically and via simulations and computed the system's phase diagram, revealing rich behavior: regions with only chimeras, coexistence of chimeras and stable domains, and metastable chimeras that decay into uniform stable domains. >>

<< ️This study offers fundamental insights into how coherent and incoherent synchronization patterns can arise in complex networked systems as it is, e.g., the brain. >>

Alejandro de Haro García, Joaquín J. Torres. Emergence of Chimeras States in One-dimensional Ising model with Long-Range Diffusion. arXiv: 2510.24903v1 [cond-mat.dis-nn]. Oct 28, 2025. 

https://arxiv.org/abs/2510.24903

Also: chimera, network, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chimera, Ising systems, stochasticity, networks, brain.

# brain: exploring aperiodic, complexity and entropic brain changes during non-ordinary states of consciousness.

<< ️Non-ordinary states of consciousness (NOC) provide an opportunity to experience highly intense, unique, and perceptually rich subjective states. The neural mechanisms supporting these experiences remain poorly understood. >>

<< ️This (AA) study examined brain activity associated with a self-induced, substance-free NOC known as Auto-Induced Cognitive Trance (AICT). Twenty-seven trained participants underwent high-density electroencephalography (EEG) recordings during rest and AICT. (They) analyzed the aperiodic component of the power spectrum (1/f), Lempel-Ziv complexity, and sample entropy from five-minute signal segments. A machine learning approach was used to classify rest and AICT, identify discriminative features, and localize their sources. >>

<< ️(AA) also compared EEG metrics across conditions and assessed whether baseline activity predicted the magnitude of change during AICT. Classification analyses revealed condition-specific differences in spectral exponents, complexity, and entropy. The aperiodic component showed the strongest discriminative power, followed by entropy and complexity. Source localization highlighted frontal regions, the posterior cingulate cortex, and the left parietal cortex as key contributors to the AICT state. Baseline neural activity in frontal and parietal regions predicted individual variability in the transition from rest to AICT. >>

<< ️These findings indicate that AICT engages brain regions implicated in rich subjective experiences and provide mechanistic insights into how self-induced trance states influence neural functioning. >>

Victor Oswald, Karim Jerbi, et al. Exploring aperiodic, complexity and entropic brain changes during non-ordinary states of consciousness. arXiv: 2509.19254v1 [q-bio.NC]. Sep 23, 2025.

https://arxiv.org/abs/2509.19254 

Also: brain, Zen, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, consciousness, auto-Induced cognitive trance (AICT), Zen.

# brain: network representations reveal structured uncertainty in music.

<< ️Music, as a structured yet perceptually rich experience, can be modeled as a network to uncover how humans encode and process auditory information. While network-based representations of music are increasingly common, the impact of feature selection on structural properties and cognitive alignment remains underexplored. >>

<< ️In this study, (AA) evaluated eight network models, each constructed from symbolic representations of piano compositions using distinct combinations of pitch, octave, duration, and interval, designed to be representative of existing approaches in the literature. By comparing these models through topological metrics, entropy analysis, and divergence with respect to inferred cognitive representations, (They) assessed both their structural and perceptual efficiency.  >>

<< ️(AA) findings reveal that simpler, feature-specific models better match human perception, whereas complex, multidimensional representations introduce cognitive inefficiencies. These results support the view that humans rely on modular, parallel cognitive networks--an architecture consistent with theories of predictive processing and free energy minimization. >>

<< ️Moreover, (AA) find that musical networks are structurally organized to guide attention toward transitions that are both uncertain and inferable. The resulting structure concentrates uncertainty in a few frequently visited nodes, creating local entropy gradients that alternate between stable and unpredictable regions, thereby enabling the expressive dynamics of tension and release that define the musical experience. >> 

<< ️These findings show that network structures make the organization of uncertainty in music observable, offering new insight into how patterned flows of expectation shape perception, and open new directions for studying how musical structures evolve across genres, cultures, and historical periods through the lens of network science. >>

Lluc Bono Rosselló, Robert Jankowski, et al. Network representations reveal structured uncertainty in music. arXiv: 2509.14053v1 [physics.soc-ph]. 17 Sep 17,  2025.

https://arxiv.org/abs/2509.14053

Also: brain, music, jazz, perception, uncertainty, network, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, music, jazz, perception, auditory information, networks, structural properties, cognitive alignment, uncertainty, uncertain-- inferable transitions.

# behav: two distinct attentional priorities guide exploratory and exploitative gaze in parallel

<< Gaze is directed to visual objects that are informative, reward-predictive, or novel. These gaze preferences may reflect the parallel influence of two separable attention systems: Exploratory attention prioritizing uncertainty and exploitative attention prioritizing learned information about reward. >>

<< (AA) tested this hypothesis in nonhuman primates learning feature-based attention to objects that had either previously learned reward associations or were novel. The reward history of features slowed down learning by attracting fixations of non-rewarded distractors that were previously targets. This reward history bias persisted in fixations used to choose objects even after choice accuracy stabilized. In contrast, fixational sampling that preceded a choice showed negligible history biases that were overcome quickly in favor of wider exploratory sampling. >>

<< Quantifying the exploratory value object features with a Parallel Belief States model of attention confirmed that exploratory fixational sampling was unaffected by reward history, while exploitative fixations that committed to a decision showed persistent target history biases. These findings suggest that gaze is guided by two separable attentional priorities in parallel. Exploratory attention prioritizes uncertain items and instantiates information sampling, while exploitative attentional priority guides gaze to current and previously goal-relevant features. >>

Xuan Wen, Alireza Seyed Hassani, et al. Two Distinct Attentional Priorities Guide Exploratory and Exploitative Gaze in Parallel. biorxiv.org. Sep 20, 2025.

https://www.biorxiv.org/content/10.1101/2025.09.20.677503v1

Also: brain, behav, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, behavior, cognition, attentional control, information seeking,  exploratory-- exploitative attentions.

# brain: self-organized learning emerges from coherent coupling of critical neurons.

<< ️Deep artificial neural networks have surpassed human-level performance across a diverse array of complex learning tasks, establishing themselves as indispensable tools in both social applications and scientific research. >>

<< ️Despite these advances, the underlying mechanisms of training in artificial neural networks remain elusive. >>

<< ️Here, (AA) propose that artificial neural networks function as adaptive, self-organizing information processing systems in which training is mediated by the coherent coupling of strongly activated, task-specific critical neurons. >>

<< ️(AA) demonstrate that such neuronal coupling gives rise to Hebbian-like neural correlation graphs, which undergo a dynamic, second-order connectivity phase transition during the initial stages of training. Concurrently, the connection weights among critical neurons are consistently reinforced while being simultaneously redistributed in a stochastic manner. >>

<< ️As a result, a precise balance of neuronal contributions is established, inducing a local concentration within the random loss landscape which provides theoretical explanation for generalization capacity. >>

<< ️(AA) further identify a later on convergence phase transition characterized by a phase boundary in hyperparameter space, driven by the nonequilibrium probability flux through weight space. The critical computational graphs resulting from coherent coupling also decode the predictive rules learned by artificial neural networks, drawing analogies to avalanche-like dynamics observed in biological neural circuits. >>

<< ️(AA) findings suggest that the coherent coupling of critical neurons and the ensuing local concentration within the loss landscapes may represent universal learning mechanisms shared by both artificial and biological neural computation. >>

Chuanbo Liu, Jin Wang. Self-organized learning emerges from coherent coupling of critical neurons. arXiv: 2509.00107v1 [cond-mat.dis-nn]. Aug 28, 2025.

https://arxiv.org/abs/2509.00107

Also: brain, neuro, network, random, transition, ai (artificial intell) (bot), in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, neurons, networks, randomness, transitions, ai (artificial intell) (bot), learning mechanisms, self-organized learning, artificial neural networks, deep learning, neuronal coupling, criticality, stochasticity, avalanche-like dynamics.

# brain: spontaneous emergence of metacognition in neuronal computation.

<< ️Metacognition, a hallmark of human intelligence, enables individuals to assess prediction uncertainty, providing an advantage over artificial intelligence in anticipating risks and performing tasks that demand trustworthiness and reliability. >>

<< ️Here, (AA) demonstrate that metacognition can naturally emerge in recurrent neural networks trained on cognitive tasks without guidance from any probabilistic inference rules or additional network architectures. Through naturally embedded nonlinear coupling with the mean of the network output, the covariance of the network output engages in metacognition by assessing the uncertainty associated with the mean, which represents the task responses. >>

<< ️(AA) further propose testable predictions about how key features of neuronal computation in the brain—noise, neuronal correlations, and heterogeneity—contribute to metacognition. >>

Hengyuan Ma, Wenlian Lu, Jianfeng Feng. Spontaneous emergence of metacognition in neuronal computation. Phys. Rev. Research 7, 033188. Aug 22, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/f1hv-bf1f

Also: brain, network, uncertainty, noise, chaos, in  https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, cognition, metacognition, learning, memory, networks, biological neural networks, biological information processing, decision making, uncertainty, stochasticity, noise, chaos.

# behav: make silence speak for itself.

<< ️Silence is a common phenomenon in classrooms, yet its implicit nature limits a clear understanding of students' underlying learning statuses. >>

<< ️This (AA) study proposed a nuanced framework to classify classroom silence based on class events and student status, and examined neurophysiological markers to reveal similarities and differences in silent states across achievement groups. (..) The study involved 54 middle school students during 34 math lessons, with simultaneous recordings of electroencephalogram (EEG), electrodermal activity (EDA), and heart rate signals, alongside video coding of classroom behaviors. >>

AA << found that high-achieving students showed no significant difference in mean EDA features between strategic silence (i.e., students choose silence deliberately) and active speaking during open questioning but exhibited higher EEG high-frequency relative power spectral density (RPSD) during strategic silence. In structural silence (i.e., students maintain silence following an external command) during directed questioning, they demonstrated significantly higher heart rates while listening to lectures compared to group activities, indicating heightened engagement. Both high- and medium-achieving students displayed elevated heart rates and EDA tonic components in structural silence during questioning compared to teaching. Furthermore, high-achieving students exhibited lower high-frequency RPSD during structural silence than strategic silence, a pattern not observed in other groups, highlighting group heterogeneity. >>

<< ️The (AA) findings contribute to validating the complexity of silence, challenge its traditional association with passivity, and offer a novel classification framework along with preliminary empirical evidence to deepen the understanding of silent learning behaviors in classroom contexts. >>

Mingxuan Gao, Jingjing Chen, et al. Make Silence Speak for Itself: a multi-modal learning analytic approach with neurophysiological data. arXiv: 2507.21063v1 [q-bio.NC]. May 23, 2025.

https://arxiv.org/abs/2507.21063

Also: silence, pause, behav, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: behavior, brain, pause, silence, strategic silence, structural silence

# brain: ubiquity of uncertainty in neuron systems.

AA << demonstrate that final-state uncertainty is ubiquitous in multistable systems of coupled neuronal maps, meaning that predicting whether one such system will eventually be chaotic or nonchaotic is often nearly impossible. (They) propose a "chance synchronization" mechanism that governs the emergence of unpredictability in neuron systems and support it by using basin classification, uncertainty exponent, and basin entropy techniques to analyze five simple discrete-time systems, each consisting of a different neuron model. >>

Their << ️results illustrate that uncertainty in neuron systems is not just a product of noise or high-dimensional complexity; it is also a fundamental property of low-dimensional, deterministic models, which has profound implications for understanding brain function, modeling cognition, and interpreting unpredictability in general multistable systems. >>

Brandon B. Le, Bennett Lamb, et al. Ubiquity of Uncertainty in Neuron Systems. arXiv: 2507.15702v1 [q-bio.NC]. Jul 21, 2025.

https://arxiv.org/abs/2507.15702

Also: brain, uncertainty, noise, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, uncertainty, noise, coupled neuronal maps, chance synchronization.

# gst: self-organization to multicriticality; when a system can self-organize to a new type of phase transition while staying on the verge of another.

<< Self-organized criticality is a well-established phenomenon, where a system dynamically tunes its structure to operate on the verge of a phase transition. Here, (AA) show that the dynamics inside the self-organized critical state are fundamentally far more versatile than previously recognized, to the extent that a system can self-organize to a new type of phase transition while staying on the verge of another. >>

<< In this first demonstration of self-organization to multicriticality, (AA) investigate a model of coupled oscillators on a random network, where the network topology evolves in response to the oscillator dynamics. (They) 

 show that the system first self-organizes to the onset of oscillations, after which it drifts to the onset of pattern formation while still remaining at the onset of oscillations, thus becoming critical in two different ways at once. >>

 

<< The observed evolution to multicriticality is robust generic behavior that (AA) expect to be widespread in self-organizing systems. Overall, these results offer a unifying framework for studying systems, such as the brain, where multiple phase transitions may be relevant for proper functioning.>>

Silja Sormunen, Thilo Gross, Jari Saramäki. Self-organization to multicriticality. arXiv: 2506.04275v1 [nlin.AO]. Jun 4, 2025. 

https://arxiv.org/abs/2506.04275

Also: network, random, self-assembly, transition, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, random, self-assembly, transition, phase transition, multiple phase transitions, self-organizing systems, self-organized criticality, multicriticality, brain.

# gst: apropos of weakness, weak but influential; nonlinear contributions of structural connectivity to human cognitive abilities and brain functions.

<< Diverse human cognitive abilities are rooted in brain structural connectivity which has weights spanning several orders of magnitude. However, due to false-positive challenges in tractography, weak connectivity has been often treated as noise and ignored - despite its prevalence across mammalian brains. >>

Here AA show << that weak connectivity significantly predicts human cognitive abilities and supports brain functions through amplification of its small weight in a nonlinear manner. >>

AA found that << weak connectivity involves high individual variability and significantly predicts general cognitive ability and memory in individuals, and it is also critical for whole-brain dynamic simulation and structure-function coupling. Importantly, fusing two post-tractography filtering methods of streamlines potentially results in more reliable connectivity that preserves weak links and outperforms conventional thresholding in predicting cognitive abilities and functional connectivity. >>

<< At the network level, weak connectivity expands the operational capacity of brain networks to enhance both global integration and fine-grained segregation, thereby supporting a functional balance essential for cognitive abilities. >>

<< Finally, (AA) identified a specific type of weak connectivity mainly linking visual/motor to limbic areas with negative gene co-expression, which has a disproportionately large impact on cognitive predictions and network dynamics. >>

Rong Wang, Zhao Chang, et al. Weak but influential: Nonlinear contributions of structural connectivity to human cognitive abilities and brain functions. arXiv: 2505.24125v1 [q-bio.NC]. May 30, 2025.

https://arxiv.org/abs/2505.24125

Also: brain, network, weak, noise, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, network, noise,  weakness, weak connectivity,  brain structural connectivity, tractography, multiple tractography algorithms, cognitive ability and memory, individual variability, global integration, fine-grained segregation, limbic areas.

# gst: disorder, chimera state, traveling chimera state, and synchronization by weak temporal couplings.

<< The mechanisms of self-sustained oscillations of brain rhythms have been studied for a long time and it is revealed that the emergence of a pacemaker loop takes a key role for these rhythms. However, it is unclear how this pacemaker loop plays a role in the resting state of the brain, where the characteristic slow-wave activities show a multi-scaled feature and can switch easily between different dynamics states. >>

<< To study this problem, herein (AA) present a neural model of pacemaker looplike network, with a weak temporal electrical coupling to mark the resting state of the brain. (They) find that different dynamics patterns can be generated by this model, including the disorder, traveling chimera state, chimera state, and synchronization. >>

<< Interestingly, (AA) observe a sensitive switching effect between the region of traveling chimera state and that of chimera state, which may provide new insights to the mechanism of quickly switching between different rhythms of the brain in the resting state. >>

<< Further, (AA) introduce an index 𝑄 to describe the fluctuations of the local order parameter of network and conjecture that there is a new regularity caused by the fluctuations. (They) find that 𝑄 is optimally dependent on the matching of parameters and thus confirms the conjecture. Moreover, (they) show that the observed traveling chimera state is robust to different forms of temporal couplings. >>

Wenbin Mao, Guoshen Liang, Zonghua Liu. Traveling chimera states by weak temporal couplings. Phys. Rev. E 111, 054220. May 27, 2025.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.111.054220

Also: network, brain, disorder & fluctuations, waves, chimera, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, brain, disorder & fluctuations, waves, chimera, brain rhythms, brain resting state, self-sustained oscillations, pacemaker loop network, traveling chimera state, temporal coupling.

# gst: hyperchaos and complex dynamical regimes in N-d neuron lattices.

AA << study the dynamics of N-dimensional lattices of nonchaotic Rulkov neurons coupled with a flow of electrical current. (They) consider both nearest-neighbor and next-nearest-neighbor couplings, homogeneous and heterogeneous neurons, and small and large lattices over a wide range of electrical coupling strengths. >>

<< As the coupling strength is varied, the neurons exhibit a number of complex dynamical regimes, including unsynchronized chaotic spiking, local quasi-bursting, synchronized chaotic bursting, and synchronized hyperchaos. >>

<< For lattices in higher spatial dimensions, (AA) discover dynamical effects arising from the ``destructive interference'' of many connected neurons and miniature ``phase transitions'' from coordinated spiking threshold crossings. In large two- and three-dimensional neuron lattices, (They) observe emergent dynamics such as local synchronization, quasi-synchronization, and lag synchronization. >>

<< These results illustrate the rich dynamics that emerge from coupled neurons in multiple spatial dimensions, highlighting how dimensionality, connectivity, and heterogeneity critically shape the collective behavior of neuronal systems. >>

Brandon B. Le, Dima Watkins. Hyperchaos and complex dynamical regimes in N-dimensional neuron lattices. arXiv: 2505.03051v1 [nlin.CD]. May 5, 2025.

https://arxiv.org/abs/2505.03051

Also: brain, network, behavior, chaos, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, network, behavior, cooperation, cooperative behavior, chaos, hyperchaos, transitions, phase transitions, transition thresholds,  synchrony, dimensionality, topology of connectivity, intermittent bursting activity, interference, destructive interference.

# aibot: I think, therefore I hallucinate: minds, machines, and the art of being wrong.

<< This theoretical work examines 'hallucinations' in both human cognition and large language models, comparing how each system can produce perceptions or outputs that deviate from reality. Drawing on neuroscience and machine learning research, (AA) highlight the predictive processes that underlie human and artificial thought. >>

<< In humans, complex neural mechanisms interpret sensory information under uncertainty, sometimes filling in gaps and creating false perceptions. This inference occurs hierarchically: higher cortical levels send top-down predictions to lower-level regions, while mismatches (prediction errors) propagate upward to refine the model. LLMs, in contrast, rely on auto-regressive modeling of text and can generate erroneous statements in the absence of robust grounding. >>

<< Despite these different foundations - biological versus computational - the similarities in their predictive architectures help explain why hallucinations occur. (AA) propose that the propensity to generate incorrect or confabulated responses may be an inherent feature of advanced intelligence. In both humans and AI, adaptive predictive processes aim to make sense of incomplete information and anticipate future states, fostering creativity and flexibility, but also introducing the risk of errors. (Their) analysis illuminates how factors such as feedback, grounding, and error correction affect the likelihood of 'being wrong' in each system. (AA) suggest that mitigating AI hallucinations (e.g., through improved training, post-processing, or knowledge-grounding methods) may also shed light on human cognitive processes, revealing how error-prone predictions can be harnessed for innovation without compromising reliability. By exploring these converging and divergent mechanisms, the paper underscores the broader implications for advancing both AI reliability and scientific understanding of human thought. >>️

Sebastian Barros. I Think, Therefore I Hallucinate: Minds, Machines, and the Art of Being Wrong. arXiv: 2503.05806v1 [q-bio.NC]. 4 Mar 4, 2025.

https://arxiv.org/abs/2503.05806

Also: brain, curiosity, novelty, uncertainty, error, mistake, jazz, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: brain, cognition, perceptions, curiosity, novelty, hallucinations, errors, prediction, prediction errors, error-prone predictions, AI, artificial intelligence, LLMs

# game: strategic decision making in biological and artificial brains.

Figure 4: Cooperation rates across different learning scenarios in Agent vs. Agent experiments. 

(a) Constrained learning using cooperation (high initial variability) 

(b) Constrained learning using defection (high initial variability) 

(c) Constrained learning using cooperation (low variability) 

(d) Constrained learning using defection (low variability) 

(e) Unconstrained learning.

(...)

<< The aim of (AA) paper is twofold. First, it seeks to uncover the algorithms that humans and other animals employ for learning in decision-making strategies within non-zero-sum games, specifically focusing on fully observable iterated prisoner’s dilemma scenarios. Second, it aims to develop a new model to explain strategic decision-making which reflects previous neurobiological findings showing that different brain circuits are responsible for self-referential processing and understanding others. The model stems from the actor-critic framework and incorporates multiple critics to allow for distinct processing of both self and others’ state. >>

AA << validate the biological plausibility and transferability of (Their) algorithm through comparisons with experimental data from human on the iterated prisoner’s dilemma game. >>️

Anushka Deshpande. Strategic Decision Making in Biological and Artificial Brains. biorxiv. doi: 10.1101/ 2025.02.17.638746. Feb 24, 2025.

https://www.biorxiv.org/content/10.1101/2025.02.17.638746v1

Also: behav, game, tit-for-tat, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: behavior, games, tit-for-tat, brain 

# behav: locomotion-dependent auditory gating to the parietal cortex guides multisensory decisions

<< Decision-making in mammals fundamentally relies on integrating multiple sensory inputs, with conflicting information resolved flexibly based on a dominant sensory modality. However, the neural mechanisms underlying state-dependent changes in sensory dominance remain poorly understood. >>

 AA << study demonstrates that locomotion in mice shifts auditory-dominant decisions toward visual dominance during audiovisual conflicts. Using circuit-specific calcium imaging and optogenetic manipulations, (They) found that weakened visual representation in the posterior parietal cortex (PPC) leads to auditory-dominant decisions in stationary mice. >>

<< Prolonged locomotion, however, promotes visual dominance by inhibiting auditory cortical neurons projecting to the PPC (ACPPC). This shift is mediated by secondary motor cortical neurons projecting to the auditory cortex (M2AC), which specifically inhibit ACPPC neurons without affecting auditory cortical projections to the striatum (ACSTR). >>

AA << findings reveal the neural circuit mechanisms underlying auditory gating to the association cortex depending on locomotion states, providing insights into the state-dependent changes in sensory dominance during multisensory decision-making. >>️

Ilsong Choi, Seung-Hee Lee. Locomotion-dependent auditory gating to the parietal cortex guides multisensory decisions. biorxiv. doi: 10.1101/ 2024.02.14.580296. Jan 24, 2025.

https://www.biorxiv.org/content/10.1101/2024.02.14.580296v2

Also: Inchingolo G. Cultural transitions and epidemiology. Proceedings of the 13th Scientific Meeting of the International Epidemiological Association - IEA, Sydney, Australia, Sept 26--29, 1993: 129. Med Hypotheses 1994; 43(4): 201-206. https://pubmed.ncbi.nlm.nih.gov/7838001/     Inchingolo G. Placebo effects via deterministic chaos during traditional dances. Genova, 7 Marzo 1995: abstract. Proceedings of the 6th Congress of the International Association of Biomedical Gerontology - IABG, (Part 1, Oriental Medicine), Makuhari, Japan, August 20-26, 1995. INRCA, Technical Report, Genova, 18 August 1995: 1-26. https://www.inkgmr.net/papers.html 

Also: behav, dance, transition, brain, sound, ethno, in https://www.inkgmr.net/kwrds.html 

Keywords: behavior, dance, transition, brain, sound, ethno

# gst: trade-off between coherence and dissipation for excitable phase oscillators.

<< Thermodynamic uncertainty relation (TUR) bounds coherence in stochastic oscillatory systems. In this paper, (AA) show that both dynamical and thermodynamic bounds play important roles for the excitable oscillators, e.g. neurons. >>

<< Excitable systems such as neurons have distinctive coherence features compared with other oscillators having no excitability. >>️

AA << combined the well-established results, i.e. the fluctuation of the ISI (inter-spike-interval) limited by 1/3 and the coherence resonance phenomenon, together with the TUR developed in recent years to investigate the coherence in the excitable phase oscillators. (AA) find quite different trade-off relation in the subthreshold (excitable) region and superthreshold (oscillatory) region, separated by the SNIC (saddle-node on an invariant circle) bound but meanwhile lower bounded by the TUR. Furthermore, (They) found that there is an optimal entropy production corresponding to the maximum coherence, which could serve as an alternative interpretation of the coherence resonance. It implies that more entropy production does not necessarily result in higher accuracy of currents. >>️

Chunming Zheng. Trade-off between coherence and dissipation for excitable phase oscillators. arXiv: 2412.16603v1 [cond-mat.stat-mech]. Dec 21, 2024.

https://arxiv.org/abs/2412.16603

Also: brain, entropy, dissipation, uncertainty, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, neurons, entropy, oscillators, excitable phase oscillators, coherence, dissipation, uncertainty

# brain: mosaic evolution of a learning and memory circuit in Heliconiini butterflies.

<< A species of tropical butterfly with unusually expanded brain structures display a fascinating mosaic pattern of neural expansion linked to a cognitive innovation. >>

<< The study (..) investigates the neural foundations of behavioural innovation in Heliconius butterflies, the only genus known to feed on both nectar and pollen. As part of this behaviour, they demonstrate a remarkable ability to learn and remember spatial information about their food sources—skills previously connected to the expansion of a brain structure called the mushroom bodies, responsible for learning and memory. >>️

Butterfly brains reveal the tweaks required for cognitive innovation. University of Bristol. Oct 18, 2024. 

https://www.bristol.ac.uk/news/2024/october/butterfly-brains.html

Max S. Farnworth, Theodora Loupasaki, et al. Mosaic evolution of a learning and memory circuit in Heliconiini butterflies. Curr. Biol. doi: 10.1016/ j.cub.2024.09.069. Oct 18, 2024. 

https://www.cell.com/current-biology/fulltext/S0960-9822(24)01337-X

Also: brain, evolution, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, evolution

# brain: time delay in 'reservoir brain' as a reservoir network, a hypothesis

<< Both the predictive power and the memory storage capability of an artificial neural network called a reservoir computer increase when time delays are added into how the network processes signals, according to a new model. >>️

<< They also suggest that incorporating time delays could offer advantages to living neural networks (such as those found in human and animal brains). Such a finding would be tantalizing, as time delays are known to decrease performance in living systems. For example, for a baseball player facing an oncoming ball, a longer time delay between perception and action (which is learned from experience) will decrease the likelihood they hit a home run. Are there instead cases in which time delays increase an organism’s ability to perform some task? Has evolution shaped our brains, which could perhaps be thought of as a collection of reservoir computers, so that the time delay between one neuron sending a signal and a second receiving it is exactly the right length for understanding the visual and audio that constantly impinge upon our eyes and ears? Does adding time delays impact the number of neurons the brain needs to operate correctly? Further work is needed to answer these questions, but such work could lead to a new understanding of how biological organism’s function.  >>️

Sarah Marzen. Time Delays Improve Performance of Certain Neural Networks. Physics 17, 111. July 22, 2024. 

https://physics.aps.org/articles/v17/111 

Also: pause, silence, jazz, network, brain, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, network, neural network, reservoir network, reservoir computer, time delay, ai, artificial intelligence

# gst: tracking criticality in unknown noise

<< Many real-world systems undergo abrupt changes in dynamics as they move across critical points, often with dramatic and irreversible consequences. >>️

AA << aim to develop noise-robust indicators of the distance to criticality (DTC) for systems affected by dynamical noise in two cases: when the noise amplitude is either fixed or is unknown and variable across recordings. (They) present a highly comparative approach to this problem that compares the ability of over 7000 candidate time-series features to track the DTC in the vicinity of a supercritical Hopf bifurcation. >>️

<< in the variable-noise setting, where these conventional indicators perform poorly, (AA) highlight new types of high-performing time-series features and show that their success is accomplished by capturing the shape of the invariant density (which depends on both the DTC and the noise amplitude) relative to the spread of fast fluctuations (which depends on the noise amplitude). >>

AA << introduce a new high-performing time-series statistic, the rescaled autodensity (RAD), that combines these two algorithmic components. >>️

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Brendan Harris, Leonardo L. Gollo, Ben D. Fulcher. Tracking the Distance to Criticality in Systems with Unknown Noise. Phys. Rev. X 14, 031021. Aug 8, 2024.

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.14.031021

Also: noise, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, noise, brain, mouse visual cortex