Numerical Cognition Based on Precise Counting with a Single Spiking Neuron
2020-02-21 | journal article; research paper
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Details
- Authors
- Rapp, Hannes; Nawrot, Martin Paul ; Stern, Merav
- Abstract
- Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatiotemporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free-flying bees that requires numerical cognition, the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.
- Issue Date
- 21-February-2020
- Journal
- iScience
- Project
- FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body
FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies - Working Group
- RG Nawrot
- eISSN
- 2589-0042
- Language
- English