Inferring Population Dynamics in Macaque Cortex
This work addresses the challenge of accurately inferring neural activity for neuroscientists, though it is incremental as it builds on prior models and benchmarks.
The paper tackled the problem of modeling neural population dynamics in macaque cortex, showing that recurrent neural networks (RNNs) outperform existing models on a benchmark, with a hybrid RNN-self-attention architecture achieving further improvements.
The proliferation of multi-unit cortical recordings over the last two decades, especially in macaques and during motor-control tasks, has generated interest in neural "population dynamics": the time evolution of neural activity across a group of neurons working together. A good model of these dynamics should be able to infer the activity of unobserved neurons within the same population and of the observed neurons at future times. Accordingly, Pandarinath and colleagues have introduced a benchmark to evaluate models on these two (and related) criteria: four data sets, each consisting of firing rates from a population of neurons, recorded from macaque cortex during movement-related tasks. Here we show that simple, general-purpose architectures based on recurrent neural networks (RNNs) outperform more "bespoke" models, and indeed outperform all published models on all four data sets in the benchmark. Performance can be improved further still with a novel, hybrid architecture that augments the RNN with self-attention, as in transformer networks. But pure transformer models fail to achieve this level of performance, either in our work or that of other groups. We argue that the autoregressive bias imposed by RNNs is critical for achieving the highest levels of performance. We conclude, however, by proposing that the benchmark be augmented with an alternative evaluation of latent dynamics that favors generative over discriminative models like the ones we propose in this report.