Cunningham, J. P. | bioRxiv Science

bioRxiv2019

Unsupervised Clusterless Decoding using a Switching Poisson Hidden Markov Model

Spike sorting is a standard preprocessing step to obtain ensembles of single unit data from multiunit, multichannel recordings in neuroscience. However, more recently, some researchers have started doing analyses directly on the unsorted data. Here we present a new computational model that is an extension of the standard (unsupervised) switching Poisson hidden Markov model (where observations are time-binned spike counts from each of N neurons), to a clusterless approximation in which we observe only a d-dimensional mark for each spike. Such an unsupervised yet clusterless approach has the potential to incorporate more information than is typically available from spike-sorted approaches, and to uncover temporal structure in neural data without access to behavioral correlates. We show that our approach can recover model parameters from simulated data, and that it can uncover task-relevant structure from real neural data.

neuroscience↗

bioRxiv2019

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

The supplementary motor area (SMA) is believed to contribute to higher-order aspects of motor control. To examine this contribution, we employed a novel cycling task and leveraged an emerging strategy: testing whether population trajectories possess properties necessary for a hypothesized class of computations. We found that, at the single-neuron level, SMA exhibited multiple response features absent in M1. We hypothesized that these diverse features might contribute, at the population level, to avoidance of population trajectory divergence - ensuring that two trajectories never followed the same path before separating. Trajectory divergence was indeed avoided in SMA but not in M1. Network simulations confirmed that low trajectory divergence is necessary when guidance of future action depends upon internally tracking contextual factors. Furthermore, the empirical trajectory geometry - helical in SMA versus elliptical in M1 - was naturally reproduced by networks that did, versus did not, internally track context.

neuroscience↗