All Publications

  • Illusion of large on-chip memory by networked computing chips for neural network inference NATURE ELECTRONICS Radway, R. M., Bartolo, A., Jolly, P. C., Khan, Z. F., Le, B. Q., Tandon, P., Wu, T. F., Xin, Y., Vianello, E., Vivet, P., Nowak, E., Wong, H., Aly, M., Beigne, E., Wootters, M., Mitra, S. 2021
  • CAMBI: Contrast-aware Multiscale Banding Index Tandon, P., Afonso, M., Sole, J., Krasula, L., IEEE IEEE. 2021: 36-40
  • Automatic Identification of Axon Bundle Activation for Epiretinal Prosthesis IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Tandon, P., Bhaskhar, N., Shah, N., Madugula, S., Grosberg, L., Fan, V. H., Hottowy, P., Sher, A., Litke, A. M., Chichilnisky, E. J., Mitra, S. 2021; 29: 2496-2502


    Retinal prostheses must be able to activate cells in a selective way in order to restore high-fidelity vision. However, inadvertent activation of far-away retinal ganglion cells (RGCs) through electrical stimulation of axon bundles can produce irregular and poorly controlled percepts, limiting artificial vision. In this work, we aim to provide an algorithmic solution to the problem of detecting axon bundle activation with a bi-directional epiretinal prostheses.The algorithm utilizes electrical recordings to determine the stimulation current amplitudes above which axon bundle activation occurs. Bundle activation is defined as the axonal stimulation of RGCs with unknown soma and receptive field locations, typically beyond the electrode array. The method exploits spatiotemporal characteristics of electrically-evoked spikes to overcome the challenge of detecting small axonal spikes.The algorithm was validated using large-scale, single-electrode and short pulse, ex vivo stimulation and recording experiments in macaque retina, by comparing algorithmically and manually identified bundle activation thresholds. For 88% of the electrodes analyzed, the threshold identified by the algorithm was within ±10% of the manually identified threshold, with a correlation coefficient of 0.95.This works presents a simple, accurate and efficient algorithm to detect axon bundle activation in epiretinal prostheses.The algorithm could be used in a closed-loop manner by a future epiretinal prosthesis to reduce poorly controlled visual percepts associated with bundle activation. Activation of distant cells via axonal stimulation will likely occur in other types of retinal implants and cortical implants, and the method may therefore be broadly applicable.

    View details for DOI 10.1109/TNSRE.2021.3128486

    View details for Web of Science ID 000730473200002

    View details for PubMedID 34784278

  • A Data-Compressive Wired-OR Readout for Massively Parallel Neural Recording Muratore, D., Tandon, P., Wootters, M., Chichilnisky, E. J., Mitra, S., Murmann, B. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2019: 1128–40


    Neural interfaces of the future will be used to help restore lost sensory, motor, and other capabilities. However, realizing this futuristic promise requires a major leap forward in how electronic devices interface with the nervous system. Next generation neural interfaces must support parallel recording from tens of thousands of electrodes within the form factor and power budget of a fully implanted device, posing a number of significant engineering challenges. In this paper, we exploit sparsity and diversity of neural signals to achieve simultaneous data compression and channel multiplexing for neural recordings. The architecture uses wired-OR interactions within an array of single-slope A/D converters to obtain massively parallel digitization of neural action potentials. The achieved compression is lossy but effective at retaining the critical samples belonging to action potentials, enabling efficient spike sorting and cell type identification. Simulation results of the architecture using data obtained from primate retina ex-vivo with a 512-channel electrode array show average compression rates up to  ∼ 40× while missing less than 5% of cells. In principle, the techniques presented here could be used to design interfaces to other parts of the nervous system.

    View details for DOI 10.1109/TBCAS.2019.2935468

    View details for Web of Science ID 000507321400002

    View details for PubMedID 31425051

  • Optimization of Electrical Stimulation for a High-Fidelity Artificial Retina Shah, N. P., Madugula, S., Grosberg, L., Mena, G., Tandon, P., Hottowy, P., Sher, A., Litke, A., Mitra, S., Chichilnisky, E. J., IEEE IEEE. 2019: 714–18
  • A 43pJ/Cycle Non-Volatile Microcontroller with 4.7 mu s Shutdown/Wake-up Integrating 2.3-bit/Cell Resistive RAM and Resilience Techniques Wu, T. F., Le, B. Q., Radway, R., Bartolo, A., Hwang, W., Jeong, S., Li, H., Tandon, P., Vianello, E., Vivet, P., Nowak, E., Wootters, M. K., Wong, H., Aly, M., Beigne, E., Mitra, S., Fujino, L. C., Anderson, J. H., Belostotski, L., Dunwell, D., Gaudet, Gulak, G., Haslett, J. W., Halupka, D., Smith, K. C. IEEE. 2019: 226-+
  • A Data-Compressive Wired-OR Readout for Massively Parallel Neural Recording Muratore, D. G., Tandon, P., Wootters, M., Chichilnisky, E. J., Mitra, S., Murmann, B., IEEE IEEE. 2019