All Publications


  • Direct-Print 3D Electrodes for Large-Scale, High-Density, and Customizable Neural Interfaces. Advanced science (Weinheim, Baden-Wurttemberg, Germany) Wang, P., Wu, E. G., Uluşan, H., Zhao, E. T., Phillips, A. J., Kling, A., Hays, M. R., Vasireddy, P. K., Madugula, S., Vilkhu, R., Hierlemann, A., Hong, G., Chichilnisky, E. J., Melosh, N. A. 2024: e2408602

    Abstract

    Silicon-based microelectronics can scalably record and modulate neural activity at high spatiotemporal resolution, but their planar form factor poses challenges in targeting 3D neural structures. A method for fabricating tissue-penetrating 3D microelectrodes directly onto planar microelectronics using high-resolution 3D printing via 2-photon polymerization and scalable microfabrication technologies are presented. This approach enables customizable electrode shape, height, and positioning for precise targeting of neuron populations distributed in 3D. The effectiveness of this approach is demonstrated in tackling the critical challenge of interfacing with the retina-specifically, selectively targeting retinal ganglion cell (RGC) somas while avoiding the axon bundle layer. 6,600-microelectrode, 35 µm pitch, tissue-penetrating arrays are fabricated to obtain high-fidelity, high-resolution, and large-scale retinal recording that reveals little axonal interference, a capability previously undemonstrated. Confocal microscopy further confirms the precise placement of the microelectrodes. This technology can be a versatile solution for interfacing silicon microelectronics with neural structures at a large scale and cellular resolution.

    View details for DOI 10.1002/advs.202408602

    View details for PubMedID 39588825

  • 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

  • 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