Summary: We identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These changes in neural activity helped to explain why animals learned some tasks more quickly than others.
Summary: A brain–machine interface, or BMI, directly connects the brain to the external world, translating a user's internal motor commands into action. In this chapter, we discuss the four basic components of an intracortical BMI: an intracortical neural recording, a decoding algorithm, an output device, and sensory feedback.
Summary: We establish that new neural activity patterns emerge with learning, providing evidence that the formation of new patterns of neural population activity can underlie the learning of new skills.
Summary: Millions of neurons in the brain control the activity of tens of muscles in the arm, meaning neural activity is redundant. We compared various hypotheses for how the brain deals with this redundancy by recording in primary motor cortex while subjects performed a brain-computer interface task.
Summary: We augment a neural network known as a variational autoencoder (VAE) to classify the observed data while also learning its latent representation. We show that when this network is combined with an LSTM and used to generate music, the network plays fewer incorrect notes than a standard VAE+LSTM.
Summary: We compare the time-varying improvements in sensitivity during motion discrimination tasks in 2D and 3D, and find that the two are remarkably similar, however with a lower signal-to-noise ratio in 3D.
Summary: We show that cells in the lateral intraparietal area (LIP) have firing activity that simultaneously carries decision signals and decision-irrelevant sensory signals. We conclude that LIP cells show a broader range of response motifs than previously considered.