# brain: apropos of persistent exploratory predisposition, chaotic pigeons are helping redefine what we know about learning.

<< ️Pigeons seem to defy a century-old psychology law about how rewards and consequences help us learn. >>

<< ️New research suggests the birds themselves avoid stability in their decision-making, instead choosing to live “at the edge of chaos.” As model species for learning and behavior, these birds are helping researchers test a century-old law about how humans and other creatures learn. >>

<< ️When learning something new, people and animals alike tend to repeat behaviors that are rewarded. First proposed by Edward Thorndike in 1898, this principle is so well established in psychology that it's become known as the law of effect. But the law implies that beyond making a behavior more frequent, rewards also make it more consistent: reducing variability in the specific way behaviors are performed over time. >>

<< ️Edward A. Wasserman and his colleagues decided to put it to the test in pigeons—a species that has been integral to the study of learning at the university’s Comparative Cognition Laboratory for more than 50 years. And the study’s results, published in the Journal of Experimental Psychology: Animal Learning and Cognition, suggest these birds experience variability as the spice of life. >>

<< ️You could argue the birds are just utterly resistant to locking into anything stable. >> Edward A. Wasserman.

<< ️this paper leaves open many questions about the [neurological] mechanisms for future scientists to explore. >> Aaron Blaisdell.

K. R. Callaway, Sarah Lewin Frasier. Chaotic pigeons are helping redefine what we know about learning. SciAm. Jun 30, 2026. 

https://www.scientificamerican.com/article/chaotic-pigeons-are-helping-redefine-what-we-know-about-learning

Wasserman E.A., Orr O.R.P., Li S. Variability, stability, and the law of effect. J. of Exp. Psychol: Animal Learning and Cognition, 52(3), 129–138. Jul 2026. 

https://psycnet.apa.org/record/2027-47688-001

Also: brain, neuro, behav, curiosity, game, Nomads, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, behavior, curiosity, games, Nomads.

# brain: collective dynamics in spiking neural networks beyond Dale's principle.

<< ️Dale's Principle has historically guided neuroscience research as a valuable rule of thumb, namely that all synapses on each neuron release the same set of neurotransmitters. Most existing Spiking Neuron Network models share this dichotomous assumption that neurons are either excitatory or inhibitory; however, recent experimental evidence points towards co-release mechanisms that violate this assumption. >>

<< ️Here, (AA) introduce a minimal model of "Bilingual" neurons violating Dale's principle that can exert both excitatory and inhibitory effects. (They) identify parameter regimes in which this architecture exhibits transitions between synchronous and asynchronous dynamics that differ quantitatively from those observed in a matched monolingual control architecture. >>

<< ️(AA) report distinct information-processing signatures both at the level of neurons and higher-order interactions between them near the phase transitions. These (AA) results suggest that the population of neurons violating Dale's principle may provide an alternative mechanism for regulating large-scale oscillatory activity in neural circuits. >>

Ross Ah-Weng, Hardik Rajpal. Collective Dynamics in Spiking Neural Networks Beyond Dale's Principle. arXiv: 2602.23202v1 [q-bio.NC]. Feb 26, 2026.

https://arxiv.org/abs/2602.23202

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

Keywords: gst, brain, neuro, networks, transitions, neural circuits, spiking neuron networks, excitatory and inhibitory effects, Dale's principle, Henry Hallett Dale, bilingual neurons, transitions between synchronous and asynchronous dynamics.

# gst: from chimera states to spike avalanches and quasicriticality; the role of superdiffusive coupling.

<< ️The partial synchronization states of collective activity, as well as the spike avalanches realization in systems of interacting neurons, are extremely important distinguishing features of the neocortical circuits that have multiple empirical validations. However, at this stage, there is a limited number of studies highlighting their potential interrelationship at the level of nonlinear mathematical models. >>

<< ️In this study, (AA) investigate the development of chimera states and the emergence of spike avalanches in superdiffusive neural networks, as well as analyze the system's approach to quasicriticality. >>

<< ️The analysis of the available ideas suggests that partial synchronization states, spike avalanches, and quasicritical neuronal dynamics are all directly implicated in core cognitive functions such as information processing, attention, and memory. Given this fundamental role, the results presented in this (AA) work could have significant implications for both theoretical neuroscience and applied machine learning, particularly in the development of reservoir computing systems. >>

I. Fateev, A. Polezhaev. From chimera states to spike avalanches and quasicriticality: The role of superdiffusive coupling. Phys. Rev. E 113, 014215. Jan 20, 2026.

https://journals.aps.org/pre/abstract/10.1103/rmx5-8fys

Also: network, brain, neuro, behav, chimera, random, walk, walking, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, neuronal network models, chimera, random, walk, walking, avalanches, neuronal avalanches, collective behaviors, criticality.

# 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.

# life: ghostly psyche revisited; ghosts, zombies, gris-gris, and so on ...

<< Most people imagine philosophers as rational thinkers who spend their time developing abstract logical theories and strongly reject superstitious beliefs. But several 20th-century philosophers actively investigated spooky topics such as clairvoyance, telepathy – even ghosts.

Many of these philosophers, including Henri Bergson and William James, were interested in what was called “psychical research”. This was the academic study of paranormal phenomena including telepathy, telekinesis and other-worldly spirits. These thinkers attended seances and were attempting to develop theories about ghosts, life after death and the powers exhibited by mediums in trances. >>

AA << recent archival research has been looking at how these topics shaped 20th-century philosophy. >>️

Matyas Moravec. Many important 20th-century philosophers investigated ghosts – here’s how they explained them. Oct 24, 2024. 

https://theconversation.com/many-important-20th-century-philosophers-investigated-ghosts-heres-how-they-explained-them-241635

Also: ethno, gris-gris, neuro, zombie, perception, psychedelic, delirium, in https://www.inkgmr.net/kwrds.html 

Keywords: ethno, gris-gris, neuro, zombie, perception, psychedelic, delirium 

FonT: even G.A. Romero's zombie filmography - which, as with other "spooky" themes, I have always marginalized - still seems relevant.

https://en.m.wikipedia.org/wiki/George_A._Romero

# gst: discontinuous transition to chaos in a canonical random neural network

AA << study a paradigmatic random recurrent neural network introduced by Sompolinsky, Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition from a homogeneous rest state to chaotic behavior, with the Lyapunov exponent gradually increasing from zero. (AA)  generalize the SCS model considering odd saturating nonlinear transfer functions, beyond the usual choice 𝜙⁡(𝑥)=tanh⁡𝑥. A discontinuous transition to chaos occurs whenever the slope of 𝜙 at 0 is a local minimum [i.e., for 𝜙′′′⁢(0)>0]. Chaos appears out of the blue, by an attractor-repeller fold. Accordingly, the Lyapunov exponent stays away from zero at the birth of chaos. >>

In the figure 7 << the pink square is located at the doubly degenerate point (𝑔,𝜀)=(1,1/3). >>️️

Diego Pazó. Discontinuous transition to chaos in a canonical random neural network. Phys. Rev. E 110, 014201. July 1, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.014201

Also: chaos, random, network, transition, neuro, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chaos, random, network, transition, neuro