Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Moein Khajehnejad · Forough Habibollahi · Richard Nock · Ehsan Arabzadeh · Peter Dayan · Amir Dezfouli
Hall E #322
Keywords: [ SA: Everything Else ] [ MISC: Supervised Learning ] [ MISC: Sequential, Network, and Time Series Modeling ] [ APP: Neuroscience, Cognitive Science ] [ PM: Everything Else ] [ T: Miscellaneous Aspects of Machine Learning ] [ Deep Learning ]
One of the most important and challenging application areas for complex machine learning methods is to predict, characterize and model rich, multi-dimensional, neural data. Recent advances in neural recording techniques have made it possible to monitor the activity of a large number of neurons across different brain regions as animals perform behavioural tasks. This poses the critical challenge of establishing links between neural activity at a microscopic scale, which might for instance represent sensory input, and at a macroscopic scale, which then generates behaviour. Predominant modeling methods apply rather disjoint techniques to these scales; by contrast, we suggest an end-to-end model which exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions, and neural data. We apply this model to a public dataset collected using Neuropixel probes in mice performing a visually-guided behavioural task as well as a synthetic dataset produced from a hierarchical network model with reciprocally connected sensory and integration circuits intended to characterize animal behaviour in a fixed-duration motion discrimination task. We show that our model outperforms previous approaches and contributes novel insights into the relationships between neural activity and behaviour.