Implementing Goal-Directed Foraging Decisions of a Simpler Nervous System in Simulation

Brown, Jess; Caetano-Anollés, Derek; Catanho, Marianne; Gribkova, Ekaterina; Ryckman, Nathaniel; Tian, Kun; Voloshin, Mikhail; Gillette, Rhanor

doi:10.1523/eneuro.0400-17.2018

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…Reward learning allows opportunistic, foraging generalists that hunt in unpredictable environments to exploit prey available at different times and endowed with special qualities of nutrition or defense. Motivation acts with reward learning to organize cost-benefit analysis of predatory attempts, facilitating the negotiations of risk with need in foraging 3,7 . Reward learning likely has ancient origins, and is documented among generalist foragers in annelids, mollusks, insects, spiders, and even nematodes and flatworms.…”

Section: Discussionmentioning

confidence: 99%

“…ASIMOV (Algorithm of Selectivity by Incentive, Motivation and Optimized Valuation) derives from a previous simulation 3 based on reward learning and motivation, and founded on neuronal relations used in cost-benefit choices of foraging by the predatory sea-slug Pleurobranchaea. A simple aesthetic sense is expanded in ASIMOV with a homeostatic reward circuit expressing reward experience.…”

Section: Methodsmentioning

confidence: 99%

“…1. The core of the model is the origin of behavioral choice in appetitive state, which controls economic decisions of foraging 3 . Appetitive state represents Positive and negative classical learning occur by outputs from the feeding network operating in approach or avoidance modes, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Simple Aesthetic Sense and Addiction Emerge in Neural Relations of Cost-Benefit Decision in Foraging

Gribkova

Catanho

Gillette

2020

Sci Rep

Self Cite

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A rudimentary aesthetic sense is found in the stimulus valuations and cost-benefit decisions made by primitive generalist foragers. These are based on factors governing personal economic decisions: incentive, appetite, and learning. We find that the addictive process is an extreme expression of aesthetic dynamics. An interactive, agent-based model, ASIMOV, reproduces a simple aesthetic sense from known neural relations of cost-benefit decision in foraging. In the presence of very high reward, an addiction-like process emerges. A drug-like prey provides extreme reward with no nutritive value, initiating high selectivity and prolonged cravings for drug through reward learning. Varying reward experience, caused by homeostatic changes in the neural circuitry of reward, further establishes the course of addiction, consisting of desensitization, withdrawal, resensitization, and associated changes in nutritional choice and pain sensitivity. These observations are consistent with the early evolution of addiction mechanisms in simple generalist foragers as an aesthetic sense for evaluating prey. ASIMOV is accessible to inspection, modification, and experiment, is adaptable as an educational tool, and provides insight on the possible coevolutionary origins of aesthetics and the addiction process. The aesthetic sense is a subjective, evaluative faculty used to distinguish positive and negative qualities of situations, objects, and constructs, and to bias behavioral decision toward or away from those stimuli. It is based on built-in preferences and feature detection, as well as learned preferences established from experience through reward learning. In humans, the highly developed aesthetic sense extends from judgements of taste and beauty to disgust. In other animals, it notably functions in mate choice, nest building, and foraging. Darwin and others 1,2 attributed the origin of the aesthetic sense to mate choice and reproductive displays, as are notable in many vertebrates. However, here we explore the ramifications of a potentially earlier origin in the foraging decisions of generalist animal species, where valuations of potential prey are made in estimates of nutritional value that factor in need, learned attributes, and risk. A primitive basis of the aesthetic sense appeared in our studies of the neuronal circuitry of decision in the predatory, generalist sea slug, Pleurobranchaea californica, in the animal's ability to evaluate stimuli in contexts of motivation and reward learning. It was initially implemented in an agent-based foraging simulation, Cyberslug 3. That agent made foraging decisions for approach or avoidance like the real animal, based on stimulus quality, motivation, and reward learning, and satisfied requirements of optimized foraging. We introduced the original simulation as an example of simple neuronal relations that could be elaborated for more complex cognition and behavior, as may have happened to ancestral bilaterians in evolution. Accordingly, here we introduce a new version, ASIMOV, which is upg...

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Simple Aesthetic Sense and Addiction Emerge in Neural Relations of Cost-Benefit Decision in Foraging

Gribkova

Catanho

Gillette

2020

Sci Rep

Self Cite

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“…Scientists from the University of Illinois at Urbana Champaign (IL, USA) have created a virtual sea slug that is able to simulate the relationship between the animal's hunger level and learning ability [5]. The research has the potential to provide a foundation for the creation of more advanced models that could simulate even more complicated natural behavior.…”

Section: Virtual Animalsmentioning

confidence: 99%

Animal Models and Virtual Reality

Sawyer¹,

Gleeson²

2018

BioTechniques

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“…The drawback of this approach is that large numbers of parameters must be set experimentally, and given the variability within nervous systems, the resulting network may not capture the original dynamics of the system [91, 51,8,115]. A potential third way has been to use more phenomenological neural models to capture aspects of neural architecture and dynamics with a greatly reduced set of parameters, and these have been successfully used for biological modeling and control [114] including those inspired by insects [141,142,14,10,9], lobsters [2, 3,4], Pleurobranchaea [13], lampreys [114,15,77] and fish [39], salamanders [11,52], and other tetrapods [62,63].…”

Section: Introductionmentioning

confidence: 99%

Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica

Webster-Wood,

Gill,

Thomas

et al. 2020

Preprint

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Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bio-inspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural

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Implementing Goal-Directed Foraging Decisions of a Simpler Nervous System in Simulation

Abstract: Visual Abstract

Cited by 9 publications

References 29 publications

Simple Aesthetic Sense and Addiction Emerge in Neural Relations of Cost-Benefit Decision in Foraging

Simple Aesthetic Sense and Addiction Emerge in Neural Relations of Cost-Benefit Decision in Foraging

Animal Models and Virtual Reality

Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica

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