Mapping the relationship between neural activity and motor behavior is a central aim of sensorimotor neuroscience and neurotechnology. While most progress to this end has relied on restricting complexity, the advent of foundation models instead proposes integrating a breadth of data as an alternate avenue for broadly advancing downstream modeling. We quantify this premise for motor decoding from intracortical microelectrode data, pretraining an autoregressive Transformer on 2000 hours of neural population spiking activity paired with diverse motor covariates from over 30 monkeys and humans. The resulting model is broadly useful, benefiting decoding on 8 downstream decoding tasks and generalizing to a variety of neural distribution shifts. However, we also highlight that scaling autoregressive Transformers seems unlikely to resolve limitations stemming from sensor variability and output stereotypy in neural datasets.

Corticomotoneuronal cells (CMN), located predominantly in the primary motor cortex, project directly to alpha motoneuronal pools in the spinal cord. The effects of CMN spikes on motoneuronal excitability are traditionally characterized by visualizing postspike effects (PSEs) in spike-triggered averages (SpTA; Fetz, Cheney, & German, 1976; Fetz & Cheney, 1980; McKiernan, Marcario, Karrer, & Cheney, 1998) of electromyography (EMG) data. Poliakov and Schieber (1998) suggested a formal test, the multiple-fragment analysis (MFA), to automatically detect PSEs. However, MFA's performance was not statistically validated, and it is unclear under what conditions it is valid. This paper's contributions are a power study that validates the MFA; an alternative test, the single-snippet analysis (SSA), which has the same functionality as MFA but is easier to calculate and has better power in small samples; a simple bootstrap simulation to estimate SpTA baselines with simulation bands that help visualize potential PSEs; and a bootstrap adjustment to the MFA and SSA to correct for nonlinear SpTA baselines.

The control of neuroprosthetic devices from the activity of motor cortex neurons benefits from learning effects where the function of these neurons is adapted to the control task. It was recently shown that tuning properties of neurons in monkey motor cortex are adapted selectively in order to compensate for an erroneous interpretation of their activity. In particular, it was shown that the tuning curves of those neurons whose preferred directions had been misinterpreted changed more than those of other neurons. In this article, we show that the experimentally observed self-tuning properties of the system can be explained on the basis of a simple learning rule. This learning rule utilizes neuronal noise for exploration and performs Hebbian weight updates that are modulated by a global reward signal. In contrast to most previously proposed reward-modulated Hebbian learning rules, this rule does not require extraneous knowledge about what is noise and what is signal. The learning rule is able to optimize the performance of the model system within biologically realistic periods of time and under high noise levels. When the neuronal noise is fitted to experimental data, the model produces learning effects similar to those found in monkey experiments.

Over the past few decades, developments in technology have significantly improved the ability to measure activity in the brain. This has spurred a great deal of research into brain function and its relation to external stimuli, and has important implications in medicine and other fields. As a result of improved understanding of brain function, it is now possible to build devices that provide direct interfaces between the brain and the external world. We describe some of the current understanding of function of the motor cortex region. We then discuss a typical likelihood-based state-space model and filtering based approach to address the problems associated with building a motor cortical-controlled cursor or robotic prosthetic device. As a variation on previous work using this approach, we introduce the idea of using Markov chain Monte Carlo methods for parameter estimation in this context. By doing this instead of performing maximum likelihood estimation, it is possible to expand the range of possible models that can be explored, at a cost in terms of computational load. We demonstrate results obtained applying this methodology to experimental data gathered from a monkey.