- Poster presentation
- Open Access
Spiking neural network model of reinforcement learning in the honeybee implemented on the GPU
© Yavuz et al. 2015
- Published: 18 December 2015
- Reinforcement Learning
- Lateral Inhibition
- Firing Regime
- Physiological Experiment
- Olfactory Pathway
Honeybees can learn and perform complex behavioral tasks despite their small brains that contain less than a million neurons. At the same time they are accessible to physiological experiments and the relatively small number of neurons in their brain lends itself to quite detailed numerical simulations. Bees therefore are a good model system for studying sensory cognition and reinforcement learning.
Our model is implemented in the GeNN  framework, which facilitates the use of GPUs for spiking neural network simulations using a code generation framework. Because of the massive parallelism provided by GPUs, we can simulate tens of thousands of neurons in real time in the sparse firing regime relevant here. We investigated optimization strategies and neuron and synapse model choices for a better performance on the GPU. The model presented here is a stepping-stone to more sophisticated learning models and multi-sensory integration in the Green Brain Project , in which we aim to control a flying robot with a simulation of learning and decision making mechanisms in the honeybee related both to the olfactory and visual pathways.
This project is supported by the EPSRC (Green Brain Project, grant number EP/J019690/1).
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