Honors & Awards

  • NIH K99/R00 Pathway to Independence Award 2018


  • PhD in Computer Science, 2016

    University of California, Irvine

  • MS in Biomedical Engineering, 2011

    ETH Zurich, Switzerland

  • BS in Electrical Engineering, 2009

    ETH Zurich, Switzerland


We optimize electrode arrangement of epiretinal implants to maximize visual subfield coverage.

We developed a spiking neural network model that showed MSTd-like response properties can emerge from evolving spike-timing dependent plasticity with homeostatic synaptic scaling (STDP-H) parameters of the connections between area MT and MSTd.

We present a SNN model that uses spike-latency coding and winner-take-all inhibition to efficiently represent visual stimuli from the Fashion MNIST dataset.

Rather than aiming to represent the visual scene as naturally as possible, a Smart Bionic Eye could provide visual augmentations through the means of artificial intelligence–based scene understanding, tailored to specific real-world tasks that are known to affect the quality of life of people who are blind.

What is the required stimulus to produce a desired percept? Here we frame this as an end-to-end optimization problem, where a deep neural network encoder is trained to invert a known, fixed forward model that approximates the underlying biological system.

We propose a perceptual stimulus encoder based on convolutional neural networks that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept.

We present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to ‘see through the eyes’ of a bionic eye user.

We show that sighted individuals can learn to adapt to the unnatural on- and off-cell population responses produced by electronic and optogenetic sight recovery technologies.

We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.

We propose HBA-U-Net: a U-Net backbone with hierarchical bottleneck attention to highlight retinal abnormalities that may be important for fovea and optic disc segmentation in the degenerated retina.

We present a systematic literature review of 216 publications from 109 different venues assessing the potential of XR technology to serve as not just a visual accessibility aid but also as a tool to study perception and behavior in people with low vision and blind people whose vision was restored with a neuroprosthesis.

We present an explainable artificiall intelligence (XAI) model fit on a large longitudinal dataset that can predict electrode deactivation in Argus II.

We propose to embed biologically realistic models of simulated prosthetic vision in immersive virtual reality so that sighted subjects can act as ‘virtual patients’ in real-world tasks.

We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.

We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.

In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering.

Brains face the fundamental challenge of extracting relevant information from high-dimensional external stimuli in order to form the neural basis that can guide an organism’s behavior and its interaction with the world. One potential approach to addressing this challenge is to reduce the number of variables required to represent a particular …

We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.

To investigate the effect of axonal stimulation on the retinal response, we developed a computational model of a small population of morphologically and biophysically detailed retinal ganglion cells, and simulated their response to epiretinal electrical stimulation. We found that activation thresholds of ganglion cell somas and axons varied …

A Commentary on: Detailed Visual Cortical Responses Generated by Retinal Sheet Transplants in Rats with Severe Retinal Degeneration by AT Foik et al. (2018).

We have developed CARLsim 4, a user-friendly SNN library written in C++ that can simulate large biologically detailed neural networks. Improving on the efficiency and scalability of earlier releases, the present release allows for the simulation using multiple GPUs and multiple CPU cores concurrently in a heterogeneous computing cluster. …

The goal of this review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision.

pulse2percept is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.

Using a dimensionality reduction technique known as non-negative matrix factorization, we found that a variety of medial superior temporal (MSTd) neural response properties could be derived from MT-like input features. The responses that emerge from this technique, such as 3D translation and rotation selectivity, spiral tuning, and heading …

We present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the …

We have developed CARLsim 3, a user-friendly, GPU-accelerated SNN library written in C/C++ that is capable of simulating biologically detailed neural models. The present release of CARLsim provides a number of improvements over our prior SNN library to allow the user to easily analyze simulation data, explore synaptic plasticity rules, and automate …

This paper presents an integrative approach to ego-lane detection that aims to be as simple as possible to enable real-time computation while being able to adapt to a variety of urban and rural traffic scenarios. The approach at hand combines and extends a road segmentation method in an illumination-invariant color image, lane markings detection …

We present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and …

We describe a simulation environment that can be used to design, construct, and run spiking neural networks (SNNs) quickly and efficiently using graphics processing units (GPUs). We then explain how the design of the simulation environment utilizes the parallel processing power of GPUs to simulate large-scale SNNs and describe recent modeling …

We present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a …

Olfactory stimuli are represented in a high-dimensional space by neural networks of the olfactory system. While a number of studies have illustrated the importance of inhibitory networks within the olfactory bulb or the antennal lobe for the shaping and processing of olfactory information, it is not clear how exactly these inhibitory networks are …