Project for our paper published in iScience:
Rapp, H., Nawrot, M. P., & Stern, M.. (2020). Numerical Cognition Based on Precise Counting with a Single Spiking Neuron. iScience, 23(2), 100852.
If you use our implementation we kindly ask to cite our paper:
@article{Rapp2020,
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.},
author = {Rapp, Hannes and Nawrot, Martin Paul and Stern, Merav},
doi = {10.1016/j.isci.2020.100852},
file = {::},
issn = {25890042},
journal = {iScience},
keywords = {Cognitive Neuroscience,In Silico Biology,Neuroscience},
number = {2},
pages = {100852},
publisher = {Elsevier Inc.},
title = {{Numerical Cognition Based on Precise Counting with a Single Spiking Neuron}},
url = {https://doi.org/10.1016/j.isci.2020.100852},
volume = {23},
year = {2020}
}