Networks of spiking neurons ( SNNs)are frequently studied as models for networks of neurons in the brain, but also as paradigm for novel energy efficient computing hardware. In principle they are especially suitable for computations in the temporal domain , such as speech processing, because their computations are carried out via events in time and space. But so far they have been lacking the capability to preserve information for longer time spans during a computation, until it is updated or needed - like a register of a digital computer. This function is provided to artificial neural networks through Long Short-Term Memory (LSTM) units. We show here that SNNs attain similar capabilities if one includes adapting neurons in the network. Adaptation denotes an increase of the firing threshold of a neuron after preceding firing. A substantial fraction of neurons in the neocortex of rodents and humans has been found to be adapting. It turns out that if adapting neurons are integrated in a suitable manner into the architecture of SNNs, the performance of these enhanced SNNs, which we call LSNNs, for computation in the temporal domain approaches that of artificial neural networks with LSTM-units. In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn (L2L ) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month .
The brain carries out demanding computations and learning processes with recurrent networks of spiking neurons ( RSNNs). But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons , such as Long Short-Term Memory (LSTM) networks. In this article, we investigate whether deep learning can improve RSNN performance. We applied backpropagation through time (BPTT), augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons. We found that the resulting RSNNs approximate, for the first time, the computational power of LSTM networks on two common benchmark tasks. Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L ) to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain. In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples, using previously unknown network learning mechanisms. Furthermore, meta-reinforcement learning (meta-RL) shows that LSNNs can learn and execute complex exploration and exploitation strategies .
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Networks
The brain carries out demanding computations and learning processes with recurrent networks
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SNNs)are frequently studied as models for networks of neurons in the brain, but also as paradigm for novel energy efficient computing hardware. In principle they are especially suitable for computations in the temporal domain
RSNNs). But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons
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speech processing, because their computations are carried out via events in time and space. But so far they have been lacking the capability to preserve information for longer time spans during a computation, until it is updated or needed - like a register of a digital computer. This function is provided to artificial neural networks through
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units. We show here that SNNs attain similar capabilities if one includes adapting neurons in the network. Adaptation denotes an increase of the firing threshold of a neuron after preceding firing. A substantial fraction of neurons in the neocortex of rodents and humans has been found to be adapting. It turns out that if adapting neurons are integrated in a suitable manner into the architecture of SNNs, the performance of these enhanced SNNs, which we call LSNNs, for computation in the temporal domain approaches that of artificial neural networks with LSTM-units. In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn
networks. In this article, we investigate whether deep learning can improve RSNN performance. We applied backpropagation through time (BPTT), augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons. We found that the resulting RSNNs approximate, for the first time, the computational power of LSTM networks on two common benchmark tasks. Furthermore, our results show that recent successes with applications of Learning-to-Learn
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) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month
) to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain. In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples, using previously unknown network learning mechanisms. Furthermore, meta-reinforcement learning (meta-RL) shows that LSNNs can learn and execute complex exploration and exploitation strategies