Neurosciences (MSc/PhD/MD-PhD)

Conor Heins

EDUCATION

College / University

Swarthmore College

Highest Degree

Bachelor of Arts

Major Subjects

Neuroscience
Heins

Country

USA

Lab Experience

Computational techniques such as computer vision, statistical modelling, network science, and machine learning; calcium imaging acquisition and analysis (in vivo and in vitro); animal behavior (social behavior & operant conditioning); in vivo opto- and chemogenetic approaches.

Projects / Research

  • 2016 – present: Investigating neuronal ensemble coding through in vivo calcium imaging in rats with miniaturized head-mounted microscopy (Hope & Shaham labs, National Institute on Drug Abuse, NIH)
  • 2015 – 2017: Studying the neuronal basis of motivated aggressive behavior in male mice (Shaham lab, National Institute on Drug Abuse, NIH)
  • 2014 – 2015: Optogenetic interrogation of the role of short neuropeptide F (sNPF) in regulating sleep & circadian rhythms in D. melanogaster (Swarthmore College, Biology Department)
  • 2012 – 2015: Neurophysiological correlates of social inference in sentence processing using scalp electroencephalography (Swarthmore College, Cognitive Neuroscience Lab)

Scholarships / Awards

2016 – 2017: SmartStart II Training Award in Computational Neuroscience, Bernstein Association for Computational Neuroscience
2015 – 2017: NIH Post-baccalaureate Intramural Research Training Award (IRTA)
2014: Hans Wallach Research Fellowship for Independent Research in Psychology (Swarthmore College)

SCIENTIFIC INTERESTS AND GOALS

I consider the interface between network science, neuroscience, and machine learning an exciting research space that offers innovation and progress for all disciplines involved. I am fascinated by the application of apply state-of-the-art techniques from deep learning and artificial intelligence to the analysis of large neural datasets. I am simultaneously interested in the converse application: harnessing insights from experimental neuroscience for the design of more powerful and biophysically-plausible neural networks in silico, that might stand to tackle longstanding problems in artificial intelligence and computation.