High-density recording and stimulating microelectrodes
Electrode arrays for the central nervous system: tissue interaction
Peripheral nervous system : chronic recording and stimulation for biolectric medicine
The Gardner lab studies neural circuit dynamics, focusing on sensory-motor learning in songbirds. The neural circuits that underly song behavior are well defined, extensively studied, and in key respects homologous to the cortico-basal ganglia circuits underlying sensory-motor learning in mammals. Our work has ranged from biophysical models of vocal production (1) and statistical analysis of song syntax and song learning (2-3) to electrophysiology (4) and cellular scale imaging in singing birds (5). We are most interested in the question of how biological systems can perform robustly in the face of noise or error-prone components. As a second aim, the lab is focussed on developing neurotechnologies relevant to a wide range of researchers and clinicians. Specific efforts include high resolution time-frequency analysis methods (6-8), carbon fiber electrode arrays(4), custom head-mounted fluorescence microscopes (5), and chronic nerve interfaces.
A unifying theme of this technology development is the goal of achieving stable long term interfaces with the brain. Chronically implanted electrodes cause ongoing damage and an active process of rejection eventually silences neural signals. Failure of chronic implants over long time-scales makes it very challenging to study the neural basis of learning, and also limits many clinical applications. With the support of the NIH Brain initiative, we are now working to engineer robust, high channel count electrode arrays suitable for both recording and stimulation in basic science studies and eventually for clinical applications. With the support of Glaxo Smith Kline, we are working towards peripheral nerve interface s capable of recording from peripheral nerves the size of a human hair. In contrast to the microvolt signals achieved in existing nerve cuff technologies, our goal is to achieve stable signals in the millivolt range over time-scales of months. While we use these tools in the lab to study motor signals in songbirds, the stable interfaces will provide new opportunities for bioelectric medicine.
In parallel with the efforts to improve electrodes, we are working to develop imaging methods that can be applied in small, freely behaving animals. This has recently come to fruition in a first report of calcium imaging in freely behaving birds using head-mounted microscopes(5). With a number of the neurotechnology projects coming to fruition, the lab is re-investing in fundamental questions about the cellular basis of sensory-motor learning in songbirds:
Nearly all living creatures maintain learned motor skills over long time-scales–for days, years or even decades. However, little is known about the mechanistic basis of this stability. Some propose that while motor skills can remain stable for years, the individual neurons controlling them may significantly change their firing properties over the course of hours. Others contend the tuning of individual neurons is as stable as the motor skill itself. Merging these two viewpoints, we are now pursuing the hypothesis that the brain encodes learned behaviors on two distinct levels - a mesoscopic level that is highly stable, and a microscopic level in which single neurons change and are influenced by the recent history of reward. In other words, the stability of a memory is rooted not in single neuron stability, but in the stability of network patterns that persist in spite of drifting individual neuron dynamics.
Using the new technologies developed in the lab, we track how cells in different regions of the brain and peripheral nervous system adapt to vocal performance errors. We seek to understand what aspects of the song motor dynamics are stable during learning and what aspects plastic. For example, we have found evidence that the spatio-temporal pattern of inhibition in pre-motor cortex is stable over months in adult birds. However, preliminary data from optical imaging indicates that individual excitatory neurons in the same region shift their tuning properties over time-scales of days. To understand the functional role of this plasticity, we apply conditional sensory feedback triggered by neural activity in singing birds. This is accomplished through a brain-computer interface that delivers brief bursts of white noise that are precisely timed relative to neural firing. Neurons active during motor performance are thought to be influenced by sensory feedback in a narrowly defined time-window that corresponds to the sensorimotor loop delay between neural firing and sensory feedback. By parametrically changing the timing of sensory errors relative to spike times, we seek to understand how specific neuron types and brain regions adapt to minimize vocal errors. These experiments hold the potential to reveal single neuron rules underlying sensory-motor learning, and address long standing questions about the nature of memory stability for skilled movements.
1) Gardner T, Cecchi G, Magnasco M, Laje R, Mindlin GB. (2001) Simple motor gestures for birdsongs. Physical Review Letters. 87(20):208101.
2) Markowitz, JE, Ivie E, Kligler, K, Gardner TJ (2013). Long-range order in canary song. PLoS Comp. Bio. 2013:9(5) : e1003052.
3) Gardner TJ, Naef F, Nottebohm F. (2005). Freedom and rules: the acquisition and reprogramming of a bird's learned song. Science. 2005; 308(5724):1046.
4) Guitchounts G, Markowitz JE, Liberti WA, Gardner TJ. (2013) A carbon-fiber electrode array for long-term neural recording. Journal of Neural Engineering, 10:046016
5) Markowitz JE, Liberti WA, Guitchounts G, Velho T, Lois C, Gardner TJ (2015). Mesoscopic Patterns of Neural Activity Support Songbird Cortical Sequences. in press PLOS Biology
6) Lim Y , Shinn-Cunningham B, Gardner TJ (2012). Sparse contour representations of sound, Signal Processing Letters, IEEE 19 (10), 684-687
7) Aoi MC, Lepage KQ, Eden UT, Lim Y, Gardner TJ. (2014) An approach to time-frequency
analysis with the ridges of the continuous chirplet transform. DOI 10.1109/TSP.2014.2365756, IEEE Transactions on Signal Processing
8) Gardner TJ, Magnasco MO (2006). Sparse time-frequency representations. Proceedings of the National Academy of Sciences of the United States of America. 103(16):6094–6099.