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Showing 1–13 of 13 results
Advanced filters: Author: David Sussillo Clear advanced filters

  • Single neuron responses are highly complex and dynamic yet they are able to flexibly represent behaviour through their collective activity. Here the authors demonstrate that population activity patterns of motor cortex neurons are orthogonal during successive task epochs that are linked through a simple linear function.

    • Gamaleldin F. Elsayed
    • Antonio H. Lara
    • John P. Cunningham
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-15

  • Brain-machine interfaces (BMI) depend on algorithms to decode neural signals, but these decoders cope poorly with signal variability. Here, authors report a BMI decoder which circumvents these problems by using a large and perturbed training dataset to improve performance with variable neural signals.

    • David Sussillo
    • Sergey D. Stavisky
    • Krishna V. Shenoy
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-13

  • This study shows that in monkeys making context-dependent decisions, task-relevant and task-irrelevant signals are confusingly intermixed in single units of the prefrontal cortex, but are readily understood in the framework of a dynamical process unfolding at the level of the population; a recurrently connected neural network model reproduces key features of the data and suggests a novel mechanism for selection and integration of task-relevant evidence towards a decision.

    • Valerio Mante
    • David Sussillo
    • William T. Newsome
    Research
    Nature
    Volume: 503, P: 78-84

  • LFADS, a deep learning method for analyzing neural population activity, can extract neural dynamics from single-trial recordings, stitch separate datasets into a single model, and infer perturbations, for example, from behavioral choices to these dynamics.

    • Chethan Pandarinath
    • Daniel J. O’Shea
    • David Sussillo
    Research
    Nature Methods
    Volume: 15, P: 805-815

  • How motor cortical activity relates to muscle movement is still unclear. Here the authors trained neural networks to reproduce muscle activity of reaching monkeys. The optimal solutions produced by these networks resembled the single-neuron and population level neural activity seen in the motor cortex of the same monkeys.

    • David Sussillo
    • Mark M Churchland
    • Krishna V Shenoy
    Research
    Nature Neuroscience
    Volume: 18, P: 1025-1033

  • Behavioural experiments to study decision-making in response to context-dependent accumulation of evidence provide testable models that are consistent with the heterogeneity in neural signatures among rats that perform well in trials.

    • Marino Pagan
    • Vincent D. Tang
    • Carlos D. Brody
    ResearchOpen Access
    Nature
    Volume: 639, P: 421-429

  • The authors identify reusable ‘dynamical motifs’ in artificial neural networks. These motifs enable flexible recombination of previously learned capabilities, promoting modular, compositional computation and rapid transfer learning. This discovery sheds light on the fundamental building blocks of intelligent behavior.

    • Laura N. Driscoll
    • Krishna Shenoy
    • David Sussillo
    ResearchOpen Access
    Nature Neuroscience
    Volume: 27, P: 1349-1363

  • One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.

    • Anthony Zador
    • Sean Escola
    • Doris Tsao
    ReviewsOpen Access
    Nature Communications
    Volume: 14, P: 1-7