摘要：SummaryInsects 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.Graphical AbstractDisplay OmittedHighlights•A single spiking neuron can successfully learn to solve numerical cognition tasks•The number of action potentials can represent numerosity•Learning to count within few epochs allows generalization to unseen categories•Counting with a single spiking neuron can solve numerical cognition tasks in insectsNeuroscience; Cognitive Neuroscience; In Silico Biology
