Numerical Cognition Based on Precise Counting with a Single Spiking Neuron

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

doi:10.1016/j.isci.2020.101283

Cited by 6 publications

(11 citation statements)

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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 (14,23,24). A single MB output neuron (MBON) receives input from all KCs and plasticity at the synapses between KCs, and the MBON enables associative learning (25,26).…”

Section: Resultsmentioning

confidence: 99%

A spiking neural program for sensorimotor control during foraging in flying insects

Rapp

Nawrot

2020

Proc. Natl. Acad. Sci. U.S.A.

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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 neural circuits and 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 and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

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Section: Resultsmentioning

confidence: 99%

A spiking neural program for sensorimotor control during foraging in flying insects

Rapp

Nawrot

2020

Proc. Natl. Acad. Sci. U.S.A.

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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 (14,23,24). A single MB output neuron (MBON) receives input from all Kenyon cells and plasticity at the synapses between KCs and the MBON enables associative learning (25,26).…”

Section: Resultsmentioning

confidence: 99%

“…The CS+ odor is paired with a reward, the CS-odor with a punishment (see Materials and Methods). In order to establish a neural representation of the odor valence at the MB output (46)(47)(48)(49) the MBON is trained (25,26) to elicit exactly one action potential in response to the CS+ stimulus that is paired with the reward, and zero action potentials when the CS-stimulus is presented (see Materials and Methods). The system response to a single CS+ stimulus after nine conditioning trials is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Optimal discrimination of cue representation is guarantied by population sparseness and temporal precision by means of temporal sparseness. Our plastic output neuron requires population sparseness for learning and the plasticity rule (25,26) allows for temporally precise memory recall. We predict that our model can solve the challenge of odor-background segregation.…”

Section: Discussionmentioning

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 (25,26). Thus, the readout neuron is modeled as voltage-based leaky integrateand-fire neuron with soft reset following the dynamical equation 7.…”

Section: Readout Neuron and Learning Rulementioning

confidence: 99%

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A spiking neural program for sensory-motor control during foraging in flying 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 neural circuits and 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 spike-based plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast & surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

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