# brain: spiral waves at the edge of neural tissue during cognitive processing.

AA << have discovered human brain signals travelling across the outer layer of neural tissue that naturally arrange themselves to resemble swirling spirals. >>️

<< The research (..) indicates these ubiquitous spirals, which are brain signals observed on the cortex during both resting and cognitive states, help organise brain activity and cognitive processing. >>️

<< Our study suggests that gaining insights into how the spirals are related to cognitive processing could significantly enhance our understanding of the dynamics and functions of the brain, (..) These spiral patterns exhibit intricate and complex dynamics, moving across the brain’s surface while rotating around central points known as phase singularities. >> Pulin Gong.

<< One key characteristic of these brain spirals is that they often emerge at the boundaries that separate different functional networks in the brain, >> Yiben Xu. 

Philip Ritchie. Scientists discover spiral-shaped signals that organise brain activity. sydney.edu.au. Jun 16, 2023. 

https://www.sydney.edu.au/news-opinion/news/2023/06/16/scientists-discover-spiral-shaped-signals-that-organise-brain-activity.html

https://youtu.be/YZlqokljqY8️

Yiben Xu, Xian Long, Jianfeng Feng & Pulin Gong. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nat Hum Behav. doi: 10.1038/ s41562-023-01626-5. Jun 15,  2023.

https://www.nature.com/articles/s41562-023-01626-5

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

Keywords: brain, vortex, waves,  cognition

# behav: predicting long-term collective behavior with deep learning (among fish species Hemigrammus rhodostomus)

<< Deciphering the social interactions that govern collective behavior in animal societies has greatly benefited from advancements in modern computing. Computational models diverge into two kinds of approaches: analytical models and machine learning models. This work introduces a deep learning model for social interactions in the fish species Hemigrammus rhodostomus, and compares its results to experiments and to the results of a state-of-the-art analytical model. To that end, (AA) propose a systematic methodology to assess the faithfulness of a model, based on the introduction of a set of stringent observables. (They) demonstrate that machine learning models of social interactions can directly compete against their analytical counterparts. Moreover, this work demonstrates the need for consistent validation across different timescales and highlights which design aspects critically enables (AA) deep learning approach to capture both short- and long-term dynamics. (AA) also show that this approach is scalable to other fish species. >>️

Vaios Papaspyros, Ramon Escobedo, et al. Predicting long-term collective animal behavior with deep learning. bioRxiv. doi: 10.1101/ 2023.02.15.528318. Feb 15, 2023.

https://www.biorxiv.org/content/10.1101/2023.02.15.528318v1

Also

keywords 'behav' in FonT

https://flashontrack.blogspot.com/search?q=behav

keyword 'ai' in FonT

https://flashontrack.blogspot.com/search?q=ai

keyword 'ia' | 'ai' in Notes 

(quasi-stochastic poetry)

https://inkpi.blogspot.com/search?q=ia

https://inkpi.blogspot.com/search?q=ai

Keywords: ai, gst, behav, behavior, cognition, machine learning, deep learning, social interactions

# behav: cognitive maps to get out of trouble, a jumping behaviour could be a goal-directed behaviour (in guppies, Poecilia reticulata).

<< Spatial cognitive abilities allow individuals to remember the location of food patches, predator hide-outs, or shelters. Animals typically incorporate learnt spatial information or use external environmental cues to navigate their surroundings. A spectacular example of how some fishes move is through aerial jumping. >>️

<< what information such re-orientation behaviour during jumping is based on remains enigmatic. Here (AA) combine a lab and field experiment to test if guppies (Poecilia reticulata) incorporate learnt spatial information and external environmental cues (visual and auditory) to determine where to jump. >>

AA << show that in unfamiliar entrapments guppies direct their jumps by combining visual and auditory cues, while in familiar entrapments they use a cognitive map. (..) jumping behaviour is a goal-directed behaviour, guided by different sources of information and involving important spatial cognitive skills. >>️️

Hannah de Waele, Catarina Vila Pouca, et al. Jumping out of trouble: Evidence for a cognitive map in guppies (Poecilia reticulata).  bioRxiv. doi: 10.1101/ 2022.03.30.486400. Mar 30, 2022.

https://www.biorxiv.org/content/10.1101/2022.03.30.486400v1

Also

Voli a casaccio. Notes (quasi-stochastic poetry). Oct 01, 2006. 

https://inkpi.blogspot.com/2006/10/2066-voli-casaccio.html

the flexible mental maps of flies. FonT. Nov 21, 2019. 

https://flashontrack.blogspot.com/2019/11/brain-flexible-mental-maps-of-flies.html

Keyword: behav, cognition, jumping behaviour, aerial jumping