The purpose of this proposal is to design and build a smart MEMs device for recording multicellular activity in the visual nervous system. This miniaturized and implantable microelectrode array system will automatically adjust the depths of the electrodes to optimize recording performance. The rationale for this system is to improve long term, chronic recording from populations of neurons in the visual system for scientific and neuroprosthetic applications. Current systems with fixed electrode geometries cannot be adjusted to optimize recordings in terms of yield or cell type. Moreover, these systems cannot """"""""follow"""""""" cells over time to overcome loss of signal due to movement of the tissue with respect to the electrodes. Manual systems have the drawback of becoming unmanageable for large arrays in scientific studies, and unacceptable for permanent implants for prosthetics applications. The proposed system overcomes all of these drawbacks by automating the position of each electrode based on recorded signal quality.
The first aim of this study is to develop algorithms to automatically search for, and hold, recorded signals from neurons. Preliminary data show that this is possible for cortical neurons recorded from awake, behaving monkeys and anesthetized rats.
The second aim i s to develop MEMS actuators that have low heat dissipation, are lockable with minimal energy application, and can provide large displacements. Preliminary studies indicate that electrolysis actuators developed by one of the co-investigators are ideal for this application.
Aim three will integrate all of the components developed in the earlier aims into a single system. This device will consist of a MEMS-based multielectrode array with independently moveable electrodes, and on-board control, monitoring and communications circuits. Although this is a proposal to develop technology, an underlying hypothesis is that this system will improve multiunit, chronic recordings substantially over what can be achieved with fixed geometry systems. This hypothesis will be tested initially with cortical recordings in anesthetized rats followed by recordings from visual cortical areas in behaving monkeys.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY015545-04
Application #
7345357
Study Section
Special Emphasis Panel (ZRG1-BDCN-E (10))
Program Officer
Oberdorfer, Michael
Project Start
2005-02-15
Project End
2010-01-31
Budget Start
2008-02-01
Budget End
2009-01-31
Support Year
4
Fiscal Year
2008
Total Cost
$748,385
Indirect Cost
Name
California Institute of Technology
Department
Type
Schools of Arts and Sciences
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Rutishauser, Ueli; Aflalo, Tyson; Rosario, Emily R et al. (2018) Single-Neuron Representation of Memory Strength and Recognition Confidence in Left Human Posterior Parietal Cortex. Neuron 97:209-220.e3
Zhang, Carey Y; Aflalo, Tyson; Revechkis, Boris et al. (2017) Partially Mixed Selectivity in Human Posterior Parietal Association Cortex. Neuron 95:697-708.e4
Klaes, Christian; Kellis, Spencer; Aflalo, Tyson et al. (2015) Hand Shape Representations in the Human Posterior Parietal Cortex. J Neurosci 35:15466-76
Aflalo, Tyson; Kellis, Spencer; Klaes, Christian et al. (2015) Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348:906-10
Andersen, Richard A; Kellis, Spencer; Klaes, Christian et al. (2014) Toward more versatile and intuitive cortical brain-machine interfaces. Curr Biol 24:R885-R897
Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer et al. (2014) Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task. J Neural Eng 11:066014
Andersen, Richard A; Andersen, Kristen N; Hwang, Eun Jung et al. (2014) Optic ataxia: from Balint's syndrome to the parietal reach region. Neuron 81:967-983
Wolf, Michael T; Burdick, Joel W (2009) A Bayesian clustering method for tracking neural signals over successive intervals. IEEE Trans Biomed Eng 56:2649-59
Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L et al. (2009) A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders. Conf Proc IEEE Eng Med Biol Soc 2009:4214-7
Mulliken, Grant H; Musallam, Sam; Andersen, Richard A (2008) Decoding trajectories from posterior parietal cortex ensembles. J Neurosci 28:12913-26

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