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Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning

Sasse, Jonathan Patrick
http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275

Abstract Details

2018, Master of Sciences, Case Western Reserve University, Biology.
Flexible and robust neural pathways are ubiquitous in animals. Previous work has demonstrated that variability in feeding behavior in the marine mollusk Aplysia californica can be useful to the animal – in general, motor components relevant to feeding show higher variability within animals, even as they vary less across different animals. (Cullins et al. Current Biology 2015). This variability, though, makes interpreting neural recordings challenging, especially in an automated context. In this research, we explore the ability for a combination of artificial neural network architectures (Long Short-Term Memory [LSTM] and Dense Fully Connected) to not only classify behaviors but to distinguish behaviors prior to any observable cue. The examined four channel recordings came from the key protractor muscle (I2) and three motor nerves that control the feeding apparatus of Aplysia californica during feeding behaviors. Each channel of the recordings had an LSTM dedicated to learning how to discern bites from swallows from white noise. The output from these four LSTMs were then passed to a dense, fully connected layer for a final classification using context from all channels. Surprisingly, the overall architecture appears able to discriminate bites from swallows (at an accuracy between 97 and 99%) at least half a second before the classic marker (I2 firing frequency exceeding 10hz) occurs. These results suggest that previously disregarded sub-threshold activity may contain high (or at least sufficient) levels of contextual information for behavioral classification which raises exciting questions about possible implications for closed circuit controllers and medical technology. TensorFlow was used with a Python interface to implement the networks.
Hillel Chiel, PhD (Advisor)
Sarah Diamond, PhD (Committee Chair)
Karen Abbott, PhD (Committee Member)
Peter Thomas, PhD (Committee Member)
53 p.
Animal Sciences; Applied Mathematics; Artificial Intelligence; Biology; Computer Science; Neurobiology; Neurosciences
Aplysia; Machine Learning; Behaviorally relevant; Classification; LSTM; Long Short-Term Memory; Feed-forward neural network; artificial neural network; decision boundary; recurrent neural network; TensorFlow

Recommended Citations

Citations

  • Sasse, J. P. (2018). Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275

    APA Style (7th edition)

  • Sasse, Jonathan. Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning. 2018. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275.

    MLA Style (8th edition)

  • Sasse, Jonathan. "Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning." Master's thesis, Case Western Reserve University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275

    Chicago Manual of Style (17th edition)

case1522755406249275
267
© 2018, some rights reserved.Creative Commons License Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning by Jonathan Patrick Sasse is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.