The peripheral nervous system represents the input/output system for the brain. Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content. In this paper, we consider the analysis of neural data recorded from the vagus nerve in animal models, and develop an unsupervised learner based on convolutional neural networks that is able to simultaneously de-noise and cluster regions of the data by signal content. Recent advances have made chronic observation [1] of, and limited control [2] over the peripheral nervous system possible. To characterise the dynamics of the signals passing to and from the brain, we wish to categorise patterns of activity within the peripheral nervous system. However, consistent detection of single neuron activity remains a challenge with current cuff electrode technology suitable for in vivo neural data acquisition. The relative position of an extracellular recording electrode and neuronal axons close enough to sit above the noise floor affects the polarity of presented signal components [3] , and their summation at the electrode occludes the presence of individual action potentials during periods of neuronal activity. Instead, local field potentials (LFPs), the combination of many neuronal responses arriving concurrently at the electrode are observed. These population level responses are potentially informationally richer [4] , but preclude the use of conventional spike-sorting [5] methodologies on such data. Instead, we develop a method based on convolutional neural networks (CNN) that simultaneously de-noises the data and categorises the observed signals. We train this model on approximately one hour of data taken from a single subject approximately twelve hours post surgical implantation. We further show that it is applicable without further training to data from a second subject thirty days post surgical implantation, demonstrating cross-time, cross-subject applicability of the trained models. The recent development of chronic neural interfacing implant systems that are able to record neural signals over period of months or years will create large sets of primarily unlabelled data, with numerous signals occurring over a range of time-scales. These data are currently un-characterisable with standard methods (e.g. spike-sorting). Previous work in this field has relied on mixing categorical and real-valued latent vectors. Westhuizen et al [9] used an adversarial auto-encoder to project neural data to labels, incorporating an approximately one-hot encoding in the latent space but also including an approximately Gaussian vector to allow reconstruction. Since both vectors are trained simultaneously, the Gaussian component of the latent space may contain the relevant labelling information for one or more true classes. InfoGAN [10] , a GAN implementation in which the discriminator identifies components of the latent space is similarly capable of one-hot latent representation of the data, but without constraints on the information carried within the one-hot encoding. The Coordinate-VAE approach, in restricting the information available to the encoder creating the non-categorical portion of the latent space, allows unsupervised characterisation of the signals in timeseries data, while simultaneously de-noising the signal. Models are transferable between individuals, suggesting that we may gain the ability to pre-train large models for the reduction to latent space representations. As shown in Figure 3 , there is some evidence to suggest that these latent space representations are also informative for physiological features. We might then rapidly train a final classifier or agent for monitoring or control of individual patients, as in Pandarianth et al [11] , in which an auto-encoder is used as a dimension reduction technique on collections of neural spiking data acquired from macaque motor and pre-motor cortices, following which a GLM is used to map the complex latent space to spiking activity.
Unsupervised analysis of data recorded from the peripheral nervous system denoises and categorises signals.
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