Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations

Abstract

Many living organisms have the ability to execute complex behaviors and cognitive processes that are reliable. In many cases, such tasks are generated in the absence of an ongoing external input that could drive the activity on their underlying neural populations. For instance, writing the word "time" requires a precise sequence of muscle contraction in the hand and wrist. There has to be some patterns of activity in the areas of the brain responsible for this behaviour that are endogenously generated every time an individual performs this action. Whereas the question of how such neural code is transformed in the target motor sequence is a question of its own, their origin is perhaps even more puzzling. Most models of cortical and sub-cortical circuits suggest that many of their neural populations are chaotic. This means that very small amounts of noise, such as an additional action potential in a neuron of a network, can lead to completely different patterns of activity. Reservoir computing is one of the first frameworks that provided an efficient solution for biologically relevant neural networks to learn complex temporal tasks in the presence of chaos. We showed that although reservoirs (i.e. recurrent neural networks) are robust to noise, they are extremely sensitive to some forms of structural perturbations, such as removing one neuron out of thousands. We proposed an alternative to these models, where the source of autonomous activity is no longer originating from the reservoir, but from a set of oscillating networks projecting to the reservoir. In our simulations, we show that this solution produce rich patterns of activity and lead to networks that are both resistant to noise and structural perturbations. The model can learn a wide variety of temporal tasks such as interval timing, motor control, speech production and spatial navigation.