In the case of the ANN learning rule, we only considered structured MAB, where each of the two synapses is updated at every time step. This is prone to cause problems because a single good set of hyperparameters offers less robustness compared to an entire region of well-performing hyperparameters. In close connection to this, a characteristic property of learning processes in humans is the ability to take advantage of previous, related experiences and use them in novel tasks. Apparently, this is the first time that the idea of L2L and Meta-Plasticity was applied to a NM hardware, see section 3.3. So what is causing the long standing law to fail?

This enables the search for new plasticity rules and might also enable new research directions.A central role in the approaches explained in this paper is the used NM hardware. TB implemented and conducted experiments with regard to MDPs, benchmarked performance impact of outer loop optimization algorithms and probed the performance benefit of NM hardware. With Deep Learning progressing and new and complex algorithms being developed, there is more and more demand for the chips that can perform heavy matrix computations efficiently.Following new areas are being explored by researchers around the globe:Among the above mentioned areas, quantum computing and carbon nanotubes are still in elementary stages of development.
The Loihi research chip includes 130,000 neurons optimized for spiking neural networks. To do so, the weights Θ of the plasticity network remain fixed, while the input values to the plasticity network as well as the output from the plasticity network are considered as inputs to the fANOVA framework.

It represents a scaled-down version of the future full-size chip and is used to evaluate and demonstrate new features as illustrated in this work. In particular, we say that the environment is always in one state Similar to MDPs, we report our results for MABs in the form of a normalized cumulative reward, where we scale between the performance of a random action selection and the performance of an oracle that always picks the best possible bandit arm. All of these systems have one thing in common — all are highly energy efficient.TrueNorth draws 1/10,000th of the power density of a conventional Von Neumann processor.This all might seem esoteric at first and takes time to get the hang of the network dynamics of an SNN. FS, CP, and WM conceived meta-plasticity, FS and CP implemented it.

Probabilistic computing addresses the fundamental uncertainty and noise of natural data.

For example, 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. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. First, we report how L2L can improve the performance and learning speed in section 3.1.

It is used as a plasticity processing unit (PPU) to implement all synaptic weight changes. 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.The computational substrate that the human brain employs to carry out its computational functions, is given by networks of spiking neurons (SNNs). In addition, new optimization algorithms can be developed to further improve performance in the outer loop of L2L. Make learning your daily ritual. This happens directly inside the circuit, there is no binary value present. This property is particularly desired when it comes to noise in the fitness landscape due to imperfections of an underlying neuromorphic hardware. Specialized hardware of this type has emerged by taking inspiration of principles of brain computation, with the intent to port the advantages of distributed and power efficient computation to silicon chips (In order to further enhance the learning capabilities of NM hardware, we exploit the adjustability of the employed neuromorphic chip and consider the use of meta-plasticity. Therefore, we conduct in section 3.3 an analysis of the arising plasticity rule based on an approach called functional Analysis of Variance (fANOVA) which was presented by We adopted this method but applied it to a slightly different, but related problem. Hyperparameters included all occurring parameters of the employed TD(λ)-Learning rule α, γ, λ, the inhibition strength among the action neurons ξ, the strength of inhibitory weights connecting the action neurons to the state neurons ζ, as well as the variables influencing the hardware-specific rescaling We found that applying L2L improved the discounted cumulative reward (red solid line), compared to the case where the hyperparameters are randomly chosen (blue line).

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