Andrew James Phillips

All Publications

• Precise control of neural activity using dynamically optimized electrical stimulation. eLife Shah, N. P., Phillips, A. J., Madugula, S., Lotlikar, A., Gogliettino, A. R., Hays, M. R., Grosberg, L., Brown, J., Dusi, A., Tandon, P., Hottowy, P., Dabrowski, W., Sher, A., Litke, A. M., Mitra, S., Chichilnisky, E. J. 2024; 13

  Abstract

  Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.

  View details for DOI 10.7554/eLife.83424

  View details for PubMedID 39508555

• A 1024-Channel 268 nW/pixel 36×36 μm2/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces. IEEE journal of solid-state circuits Jang, M., Hays, M., Yu, W. H., Lee, C., Caragiulo, P., Ramkaj, A., Wang, P., Phillips, A. J., Vitale, N., Tandon, P., Yan, P., Mak, P. I., Chae, Y., Chichilnisky, E. J., Murmann, B., Muratore, D. G. 2024; 59 (4): 1123-1136

  Abstract

  This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm2) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.

  View details for DOI 10.1109/jssc.2023.3344798

  View details for PubMedID 39391047

  View details for PubMedCentralID PMC11463976

• Direct-print three-dimensional electrodes for large- scale, high-density, and customizable neural inter- faces. bioRxiv : the preprint server for biology Wang, P., Wu, E. G., Uluşan, H., Phillips, A. J., Rose Hays, M., Kling, A., Zhao, E. T., Madugula, S., Vilkhu, R. S., Vasireddy, P. K., Hier-Lemann, A., Hong, G., Chichilnisky, E. J., Melosh, N. A. 2023

  Abstract

  Silicon-based planar microelectronics is a powerful tool for scalably recording and modulating neural activity at high spatiotemporal resolution, but it remains challenging to target neural structures in three dimensions (3D). We present a method for directly fabricating 3D arrays of tissue-penetrating microelectrodes onto silicon microelectronics. Leveraging a high-resolution 3D printing technology based on 2-photon polymerization and scalable microfabrication processes, we fabricated arrays of 6,600 microelectrodes 10-130 μm tall and at 35-μm pitch onto a planar silicon-based microelectrode array. The process enables customizable electrode shape, height and positioning for precise targeting of neuron populations distributed in 3D. As a proof of concept, we addressed the challenge of specifically targeting retinal ganglion cell (RGC) somas when interfacing with the retina. The array was customized for insertion into the retina and recording from somas while avoiding the axon layer. We verified locations of the microelectrodes with confocal microscopy and recorded high-resolution spontaneous RGC activity at cellular resolution. This revealed strong somatic and dendritic components with little axon contribution, unlike recordings with planar microelectrode arrays. The technology could be a versatile solution for interfacing silicon microelectronics with neural structures and modulating neural activity at large scale with single-cell resolution.

  View details for DOI 10.1101/2023.05.30.542925

  View details for PubMedID 37398164

  View details for PubMedCentralID PMC10312573

• Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants Vasireddy, P. K., Gogliettino, A. R., Brown, J. B., Vilkhu, R. S., Madugula, S. S., Phillips, A. J., Mitra, S., Hottowy, P., Sher, A., Litke, A., Shah, N. P., Chichilnisky, E. J., IEEE IEEE. 2023

  View details for DOI 10.1109/NER52421.2023.10123907

  View details for Web of Science ID 001009053700189