Academic Publications

  • “Learning is shaped by abrupt changes in neural engagement” [link] [pdf]
    JA Hennig, ..., AP Batista*, SM Chase*, BM Yu*
    Nature Neuroscience 1546-1726 (2021)

    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.



  • “Intracortical Brain-Machine Interfaces” [link] [pdf]
    ER Oby, JA Hennig, AP Batista, BM Yu, SM Chase
    Neural Engineering, 3rd Edition 185-221 (2020)

    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.



  • “New neural activity patterns emerge with long-term learning” [link] [pdf]
    ER Oby, MD Golub, JA Hennig, ..., BM Yu*, SM Chase*, AP Batista*
    Proceedings of the National Academy of Sciences 116 (30), 15210-15215 (2019)

    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.



  • “Constraints on neural redundancy” [link] [pdf]
    JA Hennig, MD Golub, ..., BM Yu*, SM Chase*, AP Batista*
    eLife 7, e36774 (2018)

    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.



  • “A classifying variational autoencoder with application to polyphonic music generation” [link] [pdf]
    JA Hennig, A Umakantha, RC Williamson
    arXiv [preprint] arXiv:1711.07050 (2017)

    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.



  • “A distinct mechanism of temporal integration for motion through depth” [link] [pdf]
    LN Katz, JA Hennig, LK Cormack, AC Huk
    Journal of Neuroscience 35 (28), 10212-10216 (2015)

    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.



  • “Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making” [link] [pdf]
    MLR Meister, JA Hennig, AC Huk
    Journal of Neuroscience 33 (6), 2254-2267 (2013)

    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.