Abstract concept learning in a simple neural network inspired by the insect brain

Cope, Alex; Vasilaki, Eleni; Minors, Dorian; Sabo, Chelsea; Marshall, James A. R.; Barron, Andrew B.

doi:10.1371/journal.pcbi.1006435

Cited by 76 publications

(49 citation statements)

References 62 publications

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“…They are crucial for many other aspects of cognition, including motor learning (Brooks, 1986), any comparison of sensory input to predictions or internal state (e.g., novelty detection in the hippocampus; Kumaran & Maguire, 2007) and short-term memory tasks such as delayed-match to sample tasks (Cope et al, 2018;Engel & Wang, 2011). Accordingly, grammar-like rules based on sameness/difference relations can be learned in many non-linguistic domains in humans (Dawson & Gerken, 2009;Endress, Dehaene-Lambertz, & Mehler, 2007;Marcus, Fernandes, & Johnson, 2007;Saffran, Pollak, Seibel, & Shkolnik, 2007) and by many non-human animals (de la Mora & Toro, 2013;Hauser & Glynn, 2009;Martinho & Kacelnik, 2016;Murphy, Mondragon, & Murphy, 2008;Neiworth, 2013;Pepperberg, 1987;Smirnova, Zorina, Obozova, & Wasserman, 2015;Versace, Spierings, Caffini, Ten Cate, & Vallortigara, 2017; but see Heijningen, Visser, Zuidema, & Cate, 2009;Hupé, 2017;Langbein & Puppe, 2017), possibly through a specialized sameness-detector (Endress, 2013;Endress et al, 2007) that might exist from birth (Antell, Caron, & Myers, 1985;Gervain, Berent, & Werker, 2012;Gervain, Macagno, Cogoi, Peña, & Mehler, 2008).…”

Section: Sameness/difference Relations In Language and Other Domains mentioning

confidence: 99%

“…A number of models of how sameness-relations might be computed have been proposed in the literature (Arena et al, 2013;Carpenter & Grossberg, 1987;Cope et al, 2018;Engel & Wang, 2011;Hasselmo & Wyble, 1997;J. S. Johnson, Spencer, Luck, & Schöner, 2009;Ludueña & Gros, 2013;Wen, Ulloa, Husain, Horwitz, & Contreras-Vidal, 2008).…”

Section: Models Of Sameness/difference Relationsmentioning

confidence: 99%

“…Some rely on the fact that repeatedly activated representations suffer some form of neural "fatigue" (Grill-Spector, Henson, & Martin, 2006;Kumaran & Maguire, 2007), others on circuitry where the combined input from some form of memory and from sensory representations matching (or mismatching) the memory representations must be sufficiently strong (Carpenter & Grossberg, 1987;Hasselmo & Wyble, 1997;Wen et al, 2008) or where the difference between input from memory and from sensory representations is the critical variable (Engel & Wang, 2011). Still other models detect reduced levels inhibition for novel compared to previously encountered items (Cope et al, 2018;. I discuss these models in more detail in Supplementary Material 1, where I show that they fall short on at least one of two criteria of grammar learning: they either do not generalize to unseen exemplars or they require labeled counter-examples.…”

Section: Models Of Sameness/difference Relationsmentioning

confidence: 99%

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A Simple, Biologically Plausible Feature Detector for Language Acquisition

Endress

2020

Journal of Cognitive Neuroscience

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Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. “Different” items are suppressed through inhibition, but presenting two “identical” items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.

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Section: Sameness/difference Relations In Language and Other Domains mentioning

confidence: 99%

Section: Models Of Sameness/difference Relationsmentioning

confidence: 99%

Section: Models Of Sameness/difference Relationsmentioning

confidence: 99%

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A Simple, Biologically Plausible Feature Detector for Language Acquisition

Endress

2020

Journal of Cognitive Neuroscience

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“…We are not the first to present a MB model that learns different rewards for multiple cues for decision making (19)(20)(21). However, these models utilise bounded synapses to prevent reward predictions from growing indefinitely with continued reward experience.…”

Section: Previous Mushroom Body Modelsmentioning

confidence: 99%

“…As such, DANs that integrate feedforward reward signals and feedback reward predictions from MBONs are primed to signal RPEs for learning. To the best of our knowledge, these latter two features have yet to be incorporated in computational models of the MB (19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

Learning with reward prediction errors in a model of the Drosophila mushroom body

Bennett

Philippides

Nowotny

2019

Preprint

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Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body (MB), where dopamine neurons (DANs) signal reinforcing stimuli to modulate plasticity presynaptic to MB output neurons (MBONs). Here, we extend previous MB models, in which DANs signal absolute rewards, proposing instead that DANs signal reward prediction errors (RPEs) by utilising feedback reward predictions from MBONs. We formulate plasticity rules that minimise RPEs, and use simulations to verify that MBONs learn accurate reward predictions. We postulate as yet unobserved connectivity, which not only overcomes limitations in the experimentally constrained model, but also explains additional experimental observations that connect MB physiology to learning. The original, experimentally constrained model and the augmented model capture a broad range of established fly behaviours, and together make five predictions that can be tested using established experimental methods.

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How can we apply decision‐making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Teichroeb,

Smeltzer,

Mathur

et al. 2023

American J Primatol

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Our understanding of decision‐making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision‐making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision‐making process. Based on the literature and evidence from our multi‐destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision‐making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio‐ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human‐induced rapid environmental change.

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Abstract concept learning in a simple neural network inspired by the insect brain

Cited by 76 publications

References 62 publications

A Simple, Biologically Plausible Feature Detector for Language Acquisition

A Simple, Biologically Plausible Feature Detector for Language Acquisition

Learning with reward prediction errors in a model of the Drosophila mushroom body

How can we apply decision‐making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

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