Abstract
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.
Highlights
Insects have been shown to possess cognitive abilities (Chittka and Niven, 2009; Avargues-Weber et al, 2011,2012; Avargues-Weber and Giurfa, 2013; Pahl et al, 2013)
Our objective is the implementation of a spike-based method that can be trained to solve numerical cognition tasks
A particular event is represented by a specific spatiotemporal spike pattern across a population of neurons that are presynaptic to the multispike tempotron (MST)
Summary
Insects have been shown to possess cognitive abilities (Chittka and Niven, 2009; Avargues-Weber et al, 2011,2012; Avargues-Weber and Giurfa, 2013; Pahl et al, 2013) These include estimating numerosity (Rose, 2018; Skorupski et al, 2018), counting (Chittka and Geiger, 1995; Dacke and Srinivasan, 2008; Menzel et al, 2010), and other basic arithmetical concepts (Howard et al, 2018, 2019). The neural circuit studied in Vasas and Chittka (2019) was shown to successfully predict whether a particular feature (e.g. yellow) has been presented more or less often than a pre-defined threshold number, despite being presented in a sequence of other features and distractors This circuit model was hand-tuned in order to successfully estimate numerosity in a numerical ordering task similar to Howard et al (2018). This poses the question on how an efficient connectivity, which allows the network to estimate numerosity, could be learned by means of synaptic plasticity
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