Michael Beyeler

University of California, Santa Barbara | UCSB · CCS - Department of Computer Science

Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practice, for which the fovea and optic disc (OD) are important retinal landmarks. However, the occurrence of lesions, drusen, and other...

Visual neuroprostheses are the only FDA-approved technology for the treatment of retinal degenerative blindness. Although recent work has demonstrated a systematic relationship between electrode location and the shape of the elicited visual percept, this knowledge has yet to be incorporated into retinal prosthesis design, where electrodes are typic...

Bionic vision uses neuroprostheses to restore useful vision to people living with incurable blindness. However, a major outstanding challenge is predicting what people 'see' when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which is difficult to simulate on a computer screen. In...

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capacities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can often predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy sol...

The nervous system is under tight energy constraints and must represent information efficiently. This is particularly relevant in the dorsal part of the medial superior temporal area (MSTd) in primates where neurons encode complex motion patterns in order to support a variety of behaviors. A sparse decomposition model based on a dimensionality redu...

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model...

Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible visual percepts (phosphenes). Here we propose a PSE based on CNN that is trained in an end-to-end fashion to pre...

Introduction: Retinal implants provide artificial vision to blind individuals through electrically stimulating remaining non-photoreceptor retinal cells. For epiretinal implants, placed over the ganglion cell layer, individual electrodes produce elongated 'streaks' due to the unselective stimulation of underlying ganglion axons (Beyeler, 2019). He...

Many forms of artificial sight recovery, such as electronic implants and optogenetic proteins, generally cause simultaneous, rather than complementary firing of on- and off-center retinal cells. Here, using virtual patients-sighted individuals viewing distorted input-we examine whether plasticity might compensate for abnormal neuronal population re...

Retinal neuroprostheses are the only FDA-approved treatment option for blinding degenerative diseases. A major outstanding challenge is to develop a computational model that can accurately predict the elicited visual percepts (phosphenes) across a wide range of electrical stimuli. Here we present a phenomenological model that predicts phosphene app...

Over the past decade, extended reality (XR) applications have increasingly been used as assistive technology for people with low vision (LV). Here 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 stud...

Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practice, for which the fovea and optic disc (OD) are important retinal landmarks. However, the occurrence of lesions, drusen, and other...

Retinal neuroprostheses are the only FDA-approved treatment option for blinding degenerative diseases. A major outstanding challenge is to develop a computational model that can accurately predict the elicited visual percepts (phosphenes) across a wide range of electrical stimuli. Here we present a phenomenological model that predicts phosphene app...

To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds ('system fitting'). Nonfunctional electrodes may then be deactivated to reduce power consumption and improve visual outcomes. However, thresholds vary drastically not just across electrodes but also over time, thus calling fo...

Bionic vision is a rapidly advancing field aimed at developing visual neuroprostheses ('bionic eyes') to restore useful vision to people who are blind. However, a major outstanding challenge is predicting what people 'see' when they use their devices. The limited field of view of current devices necessitates head movements to scan the scene, which...

Retinal degenerative diseases cause profound visual impairment in more than 10 million people worldwide, and retinal prostheses are being developed to restore vision to these individuals. Analogous to cochlear implants, these devices electrically stimulate surviving retinal cells to evoke visual percepts (phosphenes). However, the quality of curren...

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual ‘streaks’ that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept an...

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual 'streaks' that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept an...

Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches a...

Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas...

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percept...

Retinal degenerative diseases such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD) are among the leading causes of blindness in the world. Retinal sheet transplants offer a promising alternative approach to current treatment options. Here I summarize the findings of a recent study demonstrating intact visually evoked respons...

Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts (‘phosphenes’). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across...

Visual prostheses aim to restore vision to people blinded from degenerative photoreceptor diseases by electrically stimulating surviving neurons in the retina. However, a major challenge with epiretinal prostheses is that they may accidentally activate passing axon fibers, causing severe perceptual distortions. To investigate the effect of axonal s...

Large-scale spiking neural network (SNN) simulations are challenging to implement, due to the memory and computation required to iteratively process the large set of neural state dynamics and updates. To meet these challenges, we have developed CARLsim 4, a user-friendly SNN library written in C++ that can simulate large biologically detailed neura...

By 2020 roughly 200 million people worldwide will suffer from photoreceptor diseases such as retinitis pigmentosa and age-related macular degeneration, and a variety of retinal sight restoration technologies are being developed to target these diseases. One technology, analogous to cochlear implants, uses a grid of electrodes to stimulate remaining...

By 2020 roughly 200 million people worldwide will suffer from pho-toreceptor diseases such as retinitis pigmentosa and age-related macular de-generation, and a variety of retinal sight restoration technologies are being developed to target these diseases. One technology, analogous to cochlear implants , uses a grid of electrodes to stimulate remain...

The "bionic eye" - so long a dream of the future - is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the vision provided by these devices differs substantially from normal sight. Consequently, the ability to learn to make...

Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encoding high-dimensional stimulus spaces using a sparse and parts-based population code. Reducing the dime...

The “bionic eye” – so long a dream of the future – is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the vision provided by these devices differs substantially from normal sight. Consequently, the ability to learn to make...

Poster presented at Computational and Systems Neuroscience (Cosyne) 2017. I-34 Poster Session 1 Thursday, February 23, 2017, 8.00p

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. Source code: https://github.com/UCI-CARL/CARLsim3 Documentation: http://www.socsci.uci.edu/~jkrichma/CARLsim/doc

PowerPoint slides from UW eScience Community Seminar on 29 Nov 2016.

Poster from Society for Neuroscience (SfN) Annual Meeting 2016. http://www.abstractsonline.com/pp8/#!/4071/presentation/30223

Unlabelled: Neurons in the dorsal subregion of the medial superior temporal (MSTd) area of the macaque respond to large, complex patterns of retinal flow, implying a role in the analysis of self-motion. Some neurons are selective for the expanding radial motion that occurs as an observer moves through the environment ("heading"), and computational...

VisualStimulusToolbox provides a number of classes for creating, plotting, and storing visual stimuli such as: DotStim: field of randomly drifting dots GratingStim: drifting sinusoidal grating PlaidStim: drifting plaid stimulus (composed of two sinusoidal gratings) BarStim: drifting bar stimulus PictureStim: stimulus made from a picture (BMP, CUR,...

Animals use vision to traverse novel cluttered environments with apparent ease. Evidence suggests that the mammalian brain integrates visual motion cues across a number of remote but interconnected brain regions that make up a visual motion pathway. Although much is known about the neural circuitry that is concerned with motion perception in the Pr...

Initial code release for the following book: M. Beyeler (2015). "OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python". Packt Publishing Ltd., London, England, 230 pages, ISBN 978-178528269-0. https://github.com/mbeyeler/opencv-python-blueprints https://www.amazon.com/OpenCV-Python-Blueprints...

Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the ne...

Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning para...

PowerPoint slides from a 2015 Guest Lecture in PSYCH-268A: Computational Neuroscience, Prof. Jeff Krichmar, University of California, Irvine (UCI).

Road and lane detection play an important role in autonomous driving and commercial driver-assistance systems. Vision-based road detection is an essential step towards autonomous driving, yet a challenging task due to illumination and complexity of the visual scenery. Urban scenes may present additional challenges such as intersections, multi-lane...

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processin...

Neuromorphic engineering takes inspiration from biology to design brain-like systems that are extremely low-power, fault-tolerant, and capable of adaptation to complex environments. The design of these artificial nervous systems involves both the development of neuromorphic hardware devices and the development neuromorphic simulation tools. In this...

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes...

These authors contributed equally to this work. Abstract— Olfactory stimuli are represented in a high-dimensional space by neural networks of the olfactory system. A great deal of research in olfaction has focused on this representation within the first processing stage, the olfactory bulb (vertebrates) or antennal lobe (insects) glomeruli. In part...