Numerical Cognition Based on Precise Counting with a Single Spiking Neuron

Rapp, Hannes; Nawrot, Martin Paul; Stern, Merav

doi:10.1016/j.isci.2020.100852

Cited by 15 publications

(14 citation statements)

References 38 publications

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“…For neuron numbers and their connectivity patterns we here rely on the adult Drosophila melanogaster where anatomical knowledge is most complete (Turner et al, 2008;Takemura et al, 2017;Xu 75 et al, 2020;Aso et al, 2014a). A single MB output neuron (MBON) receives input from all Kenyon cells and plasticity at the synapses between KCs and the MBON enable associative learning (Gütig, 2016;Rapp et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Our plastic output neuron requires population sparseness for learning and the plasticity rule (Gütig, 2016;Rapp et al, 2020) allows for temporally precise memory recall. We predict that our model can solve the challenge of odor-background 255 segregation.…”

Section: Odor-background Segregation: a Joint Effect Of Temporal And mentioning

confidence: 99%

“…To fit the readout neuron to the stimuli such that it generates 1 spike for pleasant odor stimuli (CS+) and 0 spikes for any other stimuli (CS-) we use a modified implementation of the Multispike Tempotron Gütig (2016); Rapp et al (2020). Thus, the readout neuron is modeled as voltage-based leaky integrate-and-fire neuron with soft reset following the dynamical equation 7.…”

Section: Readout Neuron and Learning Rulementioning

confidence: 99%

“…2B. To obtain a neural representation of the odor valence at the MB output (Strube-Bloss et al, 2011;Aso et al, 2014b;Strube-Bloss et al, 2016), the MBON is trained (Gütig, 2016;Rapp et al, 2020) to elicit exactly one action potential in response to a stimulus that is paired with reward (CS+) and zero action potentials when the CS-stimulus is presented (see Methods).…”

mentioning

confidence: 99%

“…This term refers to the fact, that after a model has been trained on one task and gets re-trained on a 2nd task, it will completely forget everything it has learned on the previous task. In this work we used a method similarly to the latter approach of transfer-learning but 345 without any additional retraining and we used spike-based learning based on an improved implementation (Rapp et al, 2020) of the Multispike Tempotron (Gütig, 2016). We predict that spike-based methods inspired by biological learning will become increasingly important for artificial intelligence.…”

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confidence: 99%

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Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Rapp

Nawrot

2020

Preprint

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Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying nervous systems and abstract computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a synaptic plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. Without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short timescales generates cast & surge motor commands. Our systems approach is generic and predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. IntroductionNavigating towards a food source during foraging requires dynamical sensory processing, accumulation of sensory evidence and appropriate high level motor control. Navigation based on an animals' olfactory sense is a challenging task due to the complex spatiotemporal landscape of odor molecules. A core aspect of foraging is the acquisition of sensory cue samples in the natural environment where odor concentrations vary rapidly and steeply across space. Experimental 5 access to the neural substrate is challenging in freely behaving insects. Biologically realistic models thus play a key role in investigating the relevant computational mechanisms. Consequently, recent efforts at understanding foraging behavior have focused on identifying viable computational strategies for making navigation decisions (Vergassola et al.

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Section: Resultsmentioning

confidence: 99%

Section: Odor-background Segregation: a Joint Effect Of Temporal And mentioning

confidence: 99%

Section: Readout Neuron and Learning Rulementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Rapp

Nawrot

2020

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Consciousness and cognition in plants

Segundo-Ortin

Calvo

2021

WIRES Cognitive Science

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Unlike animal behavior, behavior in plants is traditionally assumed to be completely determined either genetically or environmentally. Under this assumption, plants are usually considered to be noncognitive organisms. This view nonetheless clashes with a growing body of empirical research that shows that many sophisticated cognitive capabilities traditionally assumed to be exclusive to animals are exhibited by plants too. Yet, if plants can be considered cognitive, even in a minimal sense, can they also be considered conscious? Some authors defend that the quest for plant consciousness is worth pursuing, under the premise that sentience can play a role in facilitating plant's sophisticated behavior. The goal of this article is not to provide a positive argument for plant cognition and consciousness, but to invite a constructive, empirically informed debate about it. After reviewing the empirical literature concerning plant cognition, we introduce the reader to the emerging field of plant neurobiology. Research on plant electrical and chemical signaling can help shed light into the biological bases for plant sentience. To conclude, we shall present a series of approaches to scientifically investigate plant consciousness. In sum, we invite the reader to consider the idea that if consciousness boils down to some form of biological adaptation, we should not exclude a priori the possibility that plants have evolved their own phenomenal experience of the world.

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Does quantity matter to a stingless bee?

2021

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Quantitative information is omnipresent in the world and a wide range of species has been shown to use quantities to optimize their decisions. While most studies have focused on vertebrates, a growing body of research demonstrates that also insects such as honeybees possess basic quantitative abilities that might aid them in finding profitable flower patches. However, it remains unclear if for insects, quantity is a salient feature relative to other stimulus dimensions, or if it is only used as a “last resort” strategy in case other stimulus dimensions are inconclusive. Here, we tested the stingless bee Trigona fuscipennis, a species representative of a vastly understudied group of tropical pollinators, in a quantity discrimination task. In four experiments, we trained wild, free-flying bees on stimuli that depicted either one or four elements. Subsequently, bees were confronted with a choice between stimuli that matched the training stimulus either in terms of quantity or another stimulus dimension. We found that bees were able to discriminate between the two quantities, but performance differed depending on which quantity was rewarded. Furthermore, quantity was more salient than was shape. However, quantity did not measurably influence the bees' decisions when contrasted with color or surface area. Our results demonstrate that just as honeybees, small-brained stingless bees also possess basic quantitative abilities. Moreover, invertebrate pollinators seem to utilize quantity not only as "last resort" but as a salient stimulus dimension. Our study contributes to the growing body of knowledge on quantitative cognition in invertebrate species and adds to our understanding of the evolution of numerical cognition.

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Numerical Cognition Based on Precise Counting with a Single Spiking Neuron

Cited by 15 publications

References 38 publications

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Consciousness and cognition in plants

Does quantity matter to a stingless bee?

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