Honors & Awards


  • Best graduate student poster award, FASEB Retinal Neurobiology and Visual Processing Conference (2018-2018)
  • Predoctoral Fellow, Ruth L. Kirschstein NRSA (F31), NIH National Eye Institute (2018-2021)

Professional Education


  • Master of Science, Ohio State University (2012)
  • Bachelor of Science, Ohio State University (2010)
  • Doctor of Philosophy, Northwestern University (2020)
  • Ph.D., Northwestern University, Neuroscience (2021)
  • M.S., The Ohio State University, Electrical and Computer Engineering (2012)
  • B.S., The Ohio State University, Electrical and Computer Engineering (2010)

Stanford Advisors


Current Research and Scholarly Interests


Analysis of neurons in the human and macaque retina

Lab Affiliations


All Publications


  • Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression. Cell reports Goetz, J., Jessen, Z. F., Jacobi, A., Mani, A., Cooler, S., Greer, D., Kadri, S., Segal, J., Shekhar, K., Sanes, J. R., Schwartz, G. W. 2022; 40 (2): 111040

    Abstract

    Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

    View details for DOI 10.1016/j.celrep.2022.111040

    View details for PubMedID 35830791

  • An offset ON-OFF receptive field is created by gap junctions between distinct types of retinal ganglion cells NATURE NEUROSCIENCE Cooler, S., Schwartz, G. W. 2021; 24 (1): 105-+

    Abstract

    In the vertebrate retina, the location of a neuron's receptive field in visual space closely corresponds to the physical location of synaptic input onto its dendrites, a relationship called the retinotopic map. We report the discovery of a systematic spatial offset between the ON and OFF receptive subfields in F-mini-ON retinal ganglion cells (RGCs). Surprisingly, this property does not come from spatially offset ON and OFF layer dendrites, but instead arises from a network of electrical synapses via gap junctions to RGCs of a different type, the F-mini-OFF. We show that the asymmetric morphology and connectivity of these RGCs can explain their receptive field offset, and we use a multicell model to explore the effects of receptive field offset on the precision of edge-location representation in a population. This RGC network forms a new electrical channel combining the ON and OFF feedforward pathways within the output layer of the retina.

    View details for DOI 10.1038/s41593-020-00747-8

    View details for Web of Science ID 000724151300003

    View details for PubMedID 33230322

    View details for PubMedCentralID PMC7769921

  • Gap Junctions between Heterotypic RGCs Mix ON and OFF Polarity Signals Cooler, S., Schwartz, G. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2019
  • Premotor and Motor Cortices Encode Reward PLOS ONE Ramkumar, P., Dekleva, B., Cooler, S., Miller, L., Kording, K. 2016; 11 (8): e0160851

    Abstract

    Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.

    View details for DOI 10.1371/journal.pone.0160851

    View details for Web of Science ID 000382496300004

    View details for PubMedID 27564707

    View details for PubMedCentralID PMC5001708