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bioRxiv · 10.1101/662932

Estimating numerosity of visual concepts with a single spiking neuron.

Rapp, H.·Nawrot, M. P.·Stern, M.
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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 spatio-temporal 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.

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Rapp, H., Nawrot, M. P., Stern, M.. 2019-06-06. Estimating numerosity of visual concepts with a single spiking neuron.. https://doi.org/10.1101/662932

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