Abstract
Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand to suit a particular task. In contrast, networks of neurons in the brain were optimized through extensive evolutionary and developmental processes to work well on a range of computing and learning tasks. Occasionally this process has been emulated through genetic algorithms, but these require themselves hand-design of their details and tend to provide a limited range of improvements. We employ instead other powerful gradient-free optimization tools, such as cross-entropy methods and evolutionary strategies, in order to port the function of biological optimization processes to neuromorphic hardware. As an example, we show these optimization algorithms enable neuromorphic agents to learn very efficiently from rewards. In particular, meta-plasticity, i.e., the optimization of the learning rule which they use, substantially enhances reward-based learning capability of the hardware. In addition, we demonstrate for the first time Learning-to-Learn benefits from such hardware, in particular, the capability to extract abstract knowledge from prior learning experiences that speeds up the learning of new but related tasks. Learning-to-Learn is especially suited for accelerated neuromorphic hardware, since it makes it feasible to carry out the required very large number of network computations.
Highlights
The computational substrate that the human brain employs to carry out its computational functions, is given by networks of spiking neurons (SNNs)
We investigate the impact of outer loop optimization algorithms in section 3.2 and demonstrate in section 3.3 that Meta-Plasticity yields competitive performance, while enhancing transfer learning capabilities
The human brain has been prepared by a long evolutionary process with a set of hyperparameters and learning algorithms that can be used to cover a large variety of computing and learning tasks
Summary
The computational substrate that the human brain employs to carry out its computational functions, is given by networks of spiking neurons (SNNs). There appear to be numerous reasons for evolution to branch off toward such a design. Networks of such neurons facilitate a distributed scheme of computation, intertwined with memory entities, thereby overcoming known disadvantages in contemporary computer designs such as the von Neumann bottleneck. The human brain serves as an inspiration for a power efficient learning machine, solving demanding computational tasks while consuming little resources. A characteristic property that makes energy efficient computation possible is the distinct communication among these neurons. Neurons do not need to produce an output at all times. Information is integrated over time and communicated sparsely using a format of discrete events, “spikes.”
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