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 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.
Justin Kasowski, Michael Beyeler ACM AHs ‘22
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.
Lucas Relic, Bowen Zhang, Yi-Lin Tuan, Michael Beyeler ACM AHs ‘22
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.
Rebecca B. Esquenazi, Kimberly Meier, Michael Beyeler, Geoffrey M. Boynton, Ione Fine JoV 21(10)
We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.
Jacob Granley, Michael Beyeler IEEE EMBC ‘21
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.
Shuyun Tang, Ziming Qi, Jacob Granley, Michael Beyeler MICCAI OMIA ‘21
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.
Justin Kasowski, Byron A. Johnson, Ryan Neydavood, Anvitha Akkaraju, Michael Beyeler arXiv:2109.04995
We present an explainable artificiall intelligence (XAI) model fit on a large longitudinal dataset that can predict electrode deactivation in Argus II.
Zuying Hu, Michael Beyeler IEEE EMBS NER ‘21
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.
Justin Kasowski, Nathan Wu, Michael Beyeler ACM AHs ‘21
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.
Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, Michael Beyeler ACM AHs ‘21
We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.
Michael Beyeler, Geoffrey M. Boynton, Ione Fine, Ariel Rokem MICCAI 2019
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.
Bingni W. Brunton, Michael Beyeler Curr Op Neurobiol 58:21-29
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 …
Michael Beyeler, Emily L. Rounds, Kristofor D. Carlson, Nikil Dutt, Jeffrey L. Krichmar PLOS Comp Biol 15(6):e1006908
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.
Michael Beyeler, Devyani Nanduri, James D. Weiland, Ariel Rokem, Geoffrey M. Boynton, Ione Fine SciRep 9(1):9199
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 …
Michael Beyeler IEEE/EMBS NER
A Commentary on: Detailed Visual Cortical Responses Generated by Retinal Sheet Transplants in Rats with Severe Retinal Degeneration by Foik, A. T., Lean, G. A., Scholl, L. R., McLelland, B. T., Mathur, A., Aramant, R. B., et al. (2018). J. Neurosci. 38, 10709–10724. doi: 10.1523/JNEUROSCI.1279-18.2018
Michael Beyeler Front Neurosci 13:471
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. …
Ting-Shou Chou, Hirak J. Kashyap, Jinwei Xing, Stanislav Listopad, Emily L. Rounds, Michael Beyeler, Nikil Dutt, Jeffrey L. Krichmar Proc IEEE IJCNN
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.
Michael Beyeler, Ariel Rokem, Geoffrey M. Boynton, Ione Fine J Neural Eng 14(5)
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.
Michael Beyeler, Geoffrey M. Boynton, Ione Fine, Ariel Rokem SciPy: 81-88
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 …
Michael Beyeler, Nikil Dutt, Jeffrey L. Krichmar J Neurosci 36(32)
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 …
Michael Beyeler, Nicolas Oros, Nikil Dutt, Jeffrey L. Krichmar Neur Netw 72
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 …
Michael Beyeler, Kristofor D. Carlson, Ting-Shou Chou, Nikil Dutt, Jeffrey L. Krichmar Proc IEEE IJCNN
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 …
Michael Beyeler, Florian Mirus, Alexander Verl Proc IEEE ICRA
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 …
Michael Beyeler, Micah Richert, Nikil Dutt, Jeffrey L. Krichmar Neuroinform 12(3):435-454
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 …
Kristofor D. Carlson, Michael Beyeler, Nikil Dutt, Jeffrey L. Krichmar Proc ASP-DAC
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 …
Michael Beyeler, Nikil Dutt, Jeffrey L. Krichmar Neur Netw 48:109-124
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 …
Michael Beyeler, Fabio Stefanini, Henning Proske, Giovanni Galizia, Elisabetta Chicca Proc IEEE BioCAS