Upcoming talk at COSYNE workshop “Learning in multi-layer spiking neural networks”

I am very much looking forward to presenting some recent work with Surya on learning in spiking neural networks at the CoSyNe workshop “Deep learning” and the brain (6.20–6.50p on Monday, 27 February 2017 in “Wasatch”).

Schematic drawing of a multi-layer spiking neural network

In my talk I will revisit the problem of training multi-layer spiking neural networks using an objective function approach. Due to the non-differentiable nature of spiking neurons and their non-trivial history-dependence induced by the spike reset, it is generally not possible to apply gradient-based learning methods like the ones used to train deep neural networks in machine learning.

During my presentation, I will one-by-one address the core problems typically encountered when trying to train spiking neural networks and introduce Superspike, a new approach to training deterministic spiking neural networks to solve complex and non-linearly separable temporal tasks.

Illustration of Superspike algorithm solving a 4-way classification task. In this example each output neuron needs to learn to spike in response to one out of 100 noisy input spike patterns. All neurons in the network are implemented as standard leaky integrate-and-fire neurons. Left: Schematic setup of the network. Middle: Initially all output and hidden neurons are quiescent. At a later time the output neurons have learned to respond to the correct stimulus class (indicated by shaded color region), while the hidden neurons show sparse and temporally irregular activity.

Importantly, Superspike has a direct interpretation as a Hebbian three-factor learning rule. Moreover, I am going to share some of my ideas on how I think similar algorithms could be implemented in neurobiology. For instance, when combined with feedback alignment (Lillicrap et al. 2016) the weight transport problem can be alleviated (see the Figure below for a simple example). With all that said, it would be great if you would care to join me for my talk. I am looking forward to fruitful discussions during the workshop and your feedback.


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