Talks
Spring 2018

Networks of Spiking Neurons Learn to Learn and Remember

Tuesday, April 17th, 2018 3:15 pm4:00 pm

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I will sketch a new approach for understanding computation and learning in recurrent networks of spiking neurons. This approach takes into account that networks of neurons in the brain have undergone long evolutionary processes and prior learning experiences before they encounter a new learning task. Hence they work in a quite different setting than most of our models, which are expected to learn from scratch.

Learning-to-learn (L2L) methods in machine learning, such as (Hochreiter et al., 2001) and (Wang et al., 2016), have started to address this inadequacy for artificial neural networks. I will show that some of these methods can be ported to biologically quite realistic networks of spiking neurons, provided one has spike-based modules for working memory that can emulate the function of Long Short-term memory (LSTM) modules from artificial neural networks. The inclusion of biologically more realistic neuron models, in combination with a suitable network architecture, turns out to provide such spike-based LSTM modules. The resulting application of L2L methods to networks of spiking neurons shows that they are able to learn in previously not expected ways. For example, they can learn complex input/output behaviors, for which one would expect that nonlocal learning algorithms such as backprop is required, with simple local learning rules.

The new spike-based LSTM modules are also of interest from two other perspectives. First, they  provide new ways of modeling computational operations of brains which require a working memory. Secondly, they enable networks of spiking neurons to attain some of the astounding computational capabilities that have been demonstrated during the last few years for recurrent networks of LSTM-modules.

Background information can be found in:
---Hochreiter, S., Younger, A. S., & Conwell, P. R. (2001, August). Learning to learn using gradient descent. In International Conference on Artificial Neural Networks (pp. 87-94). Springer, Berlin, Heidelberg.
---Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., ... & Botvinick, M. (2016). Learning to reinforcement learn. arXiv preprint arXiv:1611.05763.
---Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W. (2018) Long short-term memory in networks of spiking neurons. Arxiv 2018