Tim Gardner

Associate Professor, Knight Campus
Member, ION

Ph.D. Rockefeller University
B.S. Princeton University



Phone: 541-346-3187



Tim Gardner’s research focuses on the development of 3D printing and microfabrication methods for implantable neural interfaces. In the Gardner lab, new tools are applied to study sensory-motor learning in songbirds. Specific interests include the neural basis of motor sequences, motor exploration, and reinforcement learning that shapes motor learning.

For Gardner, the goal of improving neural interfaces involves close collaboration with industry – both in industry sponsored work as well as direct support of startup companies seeking to enhance the therapeutic potential of human brain implants. Most recently, he worked as a founding member of Neuralink, a company building a fully implanted bidirectional interface to the human brain. Gardner holds a bachelor’s degree in physics from Princeton University and earned his doctorate in biology and physics from Rockefeller University. He completed his post-doctoral fellowships at Rockefeller University and the Massachusetts Institute of Technology.


Related Articles

Hidden neural states underlie canary song syntax.

Nature. 2020 06;582(7813):539-544

Authors: Cohen Y, Shen J, Semu D, Leman DP, Liberti WA, Perkins LN, Liberti DC, Kotton DN, Gardner TJ

Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identities and order of past actions. Canary songs consist of repeated syllables called phrases, and the ordering of these phrases follows long-range rules1 in which the choice of what to sing depends on the song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Here, using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC2-4 as canaries explored various phrase sequences in their repertoire. We identified neurons that encode past transitions, extending over four phrases and spanning up to four seconds and forty syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than one song history, and are active mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that the dynamics of HVC include 'hidden states' that are not reflected in ongoing behaviour but rather carry information about prior actions. These states provide a possible substrate for the control of syntax transitions governed by long-range rules.

PMID: 32555461 [PubMed - indexed for MEDLINE]