Daniel Gardner   Professor of Physiology and Biophysics

Phone
  • +1 212 746 6373

My research is two fold. In the Laboratory of Neuroinformatics, we have developed multiple global resources in neuroinformatics and computational neuroinformatics; as a teacher for half a century, I am developing new modalities for medical education.

COMPUTATIONAL NEUROINFORMATICS

One of the most exciting unsolved problems of biomedical science is how the cellular and network properties of individual neurons, and the information they convey, give rise to the complex behavior of the brain. This fundamental question is examined by synthesizing state-of-the-art neurobiology with informatics: the science of information underlying both brains and machines.

The Lab's very newest in-development project will address a major barrier to progress in neuroscience. Contemporary neuroscientists record increasingly larger numbers of signals from nervous systems and correlate them with many behaviors and with disease states. However, this new capacity to acquire signals simultaneously from a hundred or more neurons or networks far outstrips our ability to analyze and to ask questions about these data, severely limiting progress. To address this imbalance, we have just begun to apply new massively-parallel computer technology to proven analytic methods. Such innovative enhanced analyses will enable neuroscience laboratories to derive greater insight from their data, and so advance our understanding of the relation of neural signals and brain function to sensation, perception, decision, and action. Our major impact, aim, and significance are encapsulated in the acronym NEAT: the Neurophysiology Extended Analysis Tool.

The software to address this critical barrier to progress in the field is based on the multiple verified algorithms and proven design of our Spike Train Analysis Toolkit, described below. We are extending the STAToolkit so that it leverages the very new technology of low-cost, drop-in, massively parallel graphics processing units (GPUs).

Other recent work described below was funded by the NIH, including the NIH Blueprint for Neuroscience Research, and NIMH via the Human Brain Project, with past support from NIMH, NINDS, and NSF.

Our most recent completed project developed and implemented parallelized computational algorithms to explore the information content of spike trains and other neuronal signals, towards an understanding of the neural coding underlying visual and somatosensory processing. The major deliverable of this work, the Spike Train Analysis Toolkit, has been downloaded by over 1,900 labs and is in active use by neurophysiologists across the globe.

A major prior project initiated development of a central resource for neuroscientists to discover and explore the wide span of web-accessible neurodatabases, computational tool sites, and portals providing neuron and brain information and materials. This Neuroscience Information Framework was developed for the NIH by a multi-institution consortium directed by Weill Cornell's Laboratory of Neuroinformatics. This work is carried on at neuinfo.org and nif.nih.gov.

NEUROPHYSIOLOGY

Believing strongly that informational, computational, or theoretical biology should never be divorced from experimental work, this thrust also continues my laboratory's long-standing interest in neural networks, their neuronal and synaptic components, and their emergent properties. Using techniques I developed and introduced for simultaneous voltage-clamping of multiple interconnected neurons, we will analyze the information carrying and processing capabilities of parallel channels formed by paired Aplysia neurons. These form a testbed, bridging the gap between the single neurons characteristic of invertebrates and the massively parallel columns and modules found in mammalian brains. Related experiments may test aspects of the fire-together, wire-together hypothesis. This work descends as well from studies of interneuronal organization begun more than 45 years ago in the laboratory of Eric R. Kandel.

MEDICAL EDUCATION

I’m developing two new approaches to undergraduate medical education: MASER and Flexner++.

MASER--a Multidimensional Accessible Syllabus and Educational Resource--is designed to be a student-empowering, accessible, syllabus and guide to the fundamental science curriculum. Emphasizing links in the curriculum throughout the basic science years, MASER should serve as a resource for the entire four years and beyond. Such a persistent resource would not only aid recall of—and relationships among—concepts, it would also reinforce the understanding that encounters and courses are interconnected and linked components synergistically advancing the bases of medical practice. Multidimensional sets of controlled-vocabulary terms will be arranged in hierarchical trees covering each of: concepts, molecules, genes, cell or tissue, organ system, diagnosis, disease, therapies. Such hierarchical controlled vocabularies would permit precise markup of and search for specific educational encounters, including small groups, flipped classrooms, lectures, computer simulations, videos, and other student-faculty or student-patient interactions.

Flexner++ seeks to enhance medical education in order to enable students throughout their careers to adapt to new findings in basic, translational, and clinical biomedicine. Undergraduate medical education should prepare for a lifetime of practice, but delivers only a snapshot of what is known at one moment in time. This static knowledge model of science is inevitably incomplete: biomedical understanding, translational relevance, and standards of practice change constantly, and will be nuanced, enriched, or superseded during the subsequent careers of today’s students. I propose transcending this static model by developing students’ informed receptivity: the ability to efficiently learn and incorporate future advances—both evolutionary and revolutionary—that will become understood during the course of a career and that will modify the future practice of medicine. These are not now known, so their content can’t be taught. Medical students can be taught how to be adaptive physicians who can evolve their understanding and update their knowledge base as neuroscience and the other biomedical sciences advance. This requires development of critical life skills including informed evaluation of emergent findings. Enhanced teaching modalities will be needed to implement Flexner++, and a challenge is to select, evaluate, and implement such modalities to equip students not only with current facts, but also the methods and skills to anticipate, monitor, evaluate, appreciate new findings with real clinical-translational significance, and put this new material into practice.

e-mail: dan@med.cornell.edu Further Information: http://physiology.med.cornell.edu/faculty/gardner/index.html

Publications

Sort by

Selected publications

Research

Sort by

Grants awarded

Background

Contact

Primary Email

  • dgardner@med.cornell.edu

additional emails

  • dan.gardner@caa.columbia.edu
  • dgardner@cornell.edu

Identity

eRA Commons ID

  • DanGardner

Other

Primary Affiliation

  • Weill Cornell Medical College, Cornell University