Abstract
Reward-modulated spike timing dependent plasticity (STDP) combines unsupervised STDP with a reinforcement signal that modulates synaptic changes. It was proposed as a learning rule capable of solving the distal reward problem in reinforcement learning. Nonetheless, performance and limitations of this learning mechanism have yet to be tested for its ability to solve biological problems. In our work, rewarded STDP was implemented to model foraging behavior in a simulated environment. Over the course of training the network of spiking neurons developed the capability of producing highly successful decision-making. The network performance remained stable even after significant perturbations of synaptic structure. Rewarded STDP alone was insufficient to learn effective decision making due to the difficulty maintaining homeostatic equilibrium of synaptic weights and the development of local performance maxima. Our study predicts that successful learning requires stabilizing mechanisms that allow neurons to balance their input and output synapses as well as synaptic noise.
Highlights
The purpose of building neural networks can be seen from two different perspectives
In our work we have chosen to concentrate on problem solving as a validation tool for showing the capabilities and drawbacks of rewarded spike timing dependent plasticity (STDP) in biologically inspired spiking neural networks
At the onset of the simulation all synaptic weights to the output layer were of uniform strength
Summary
From an experimentalist’s point of view they can be used to help find, validate, or falsify mechanistic theories about the brain through comparison with experimental data. From an engineering perspective they are powerful algorithms to solve computational problems. Biological neural networks (e.g. human and animal brains) can solve complex problems; a properly designed and valid biological model must be able to solve complex problems. Brain models are only validated by comparison with experimental data. In our work we have chosen to concentrate on problem solving as a validation tool for showing the capabilities and drawbacks of rewarded spike timing dependent plasticity (STDP) in biologically inspired spiking neural networks
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