Benjamin D. Evans

• BA, MSc, MSc, DPhil
• Lecturer at University of Sussex

Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explore the importance of temporal parameters, we trained spiking neural networks on tasks with varying te...

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are...

Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitating precise and unbiased staging. Here we asked if we could train a deep convolutional neural networ...

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that DNNs will play an important role in modelling human vision going forward. But there are also disagreements abo...

Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks is extremely limited, even wit...

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the bes...

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the bes...

Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks is extremely limited, even wit...

Recent work has indicated a need for increased temporal resolution for studies of the early chick brain. Over a 10-hour period, the developmental potential of progenitor cells in the HH10 brain changes, and concomitantly, the brain undergoes subtle changes in morphology. We asked if we could train a deep convolutional neural network to sub-stage HH...

Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of their architectural and representational similarities. However, closer scrutiny of these models suggests...

Clinical classification is essential for estimating disease prevalence but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining disease prevale...

Wnt signaling regulates cell proliferation and cell differentiation as well as migration and polarity during development. However, it is still unclear how the Wnt ligand distribution is precisely controlled to fulfil these functions. Here, we show that the planar cell polarity protein Vangl2 regulates the distribution of Wnt by cytonemes. In zebraf...

Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of their architectural and representational similarities. However, closer scrutiny of these models suggests...

The Wnt signalling network regulates cell proliferation and cell differentiation as well as migration and polarity in development of multicellular organisms. However, it is still unclear how distribution of Wnt ligands is precisely controlled to fulfil all of these different functions. Here, we show that the four-pass transmembrane protein Vangl2 o...

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to buildin...

When deep convolutional neural networks (CNNs) are trained “end-to-end” on raw data, some of the feature detectors they develop in their early layers resemble the representations found in early visual cortex. This result has been used to draw parallels between deep learning systems and human visual perception. In this study, we show that when CNNs...

Deep neural networks (DNNs) are becoming increasingly popular as a model of the human visual system. However, they show behaviours that are uncharacteristic of humans, including the ability to learn arbitrary data, such as images with pixel values drawn randomly from a Gaussian distribution. We investigated whether this behaviour is due to the lear...

This report describes the work completed during a week long data study group hosted by the Alan Turing Institute. The challenge was provided by Rothamsted Research and looks at predicting soil and plant physicochemical properties from soil infrared (IR) spectra. Three datasets were explored and modelled using a combination of established and more r...

Containers are greatly improving computational science by packaging software and data dependencies. In a scholarly context, transparency and support of reproducibility are the largest drivers for using these containers. It follows that choices that are made with respect to building containers can make or break a workflow’s reproducibility. The buil...

Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology (‘Cardiac Chaste’), discrete cell-based model...

Clinical classification is essential for estimating disease prevalence in a population but is difficult, often requiring complex investigations. The widespread availability of population level genetic data makes novel genetic stratification techniques a highly attractive alternative. We propose a generalizable mathematical framework for determining...

eMethods 1: VOC breath model refinement eMethods 2: STARD 2015 list eMethods 3: Optimisation of Bag materials eMethods 4: Effect of ambient room air upon analysis of trace VOCs eMethods 5: Human factor analysis of breath bag sampling eMethods 6: Detection limit of SIFT-MS identified as 1ppbv eMethods 7: GC-MS analysis eMethods 8: Comparison of pres...

Importance Early esophagogastric cancer (OGC) stage presents with nonspecific symptoms. Objective The aim of this study was to determine the accuracy of a breath test for the diagnosis of OGC in a multicenter validation study. Design, Setting, and Participants Patient recruitment for this diagnostic validation study was conducted at 3 London hosp...

Background: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrob...

This paper describes the design and operation of a system which can be used as a Visual to Auditory Sensory Substitution Device (SSD), as well as the front-end of a real-time retinal prosthesis (RP) or Vision Augmentation (VA) system. Such systems consist of three components: a sensory block to capture the visual scene, a processing block to manage...

We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built co...

Neuroscientists are accumulating data at unprecedented rates through large-scale initiatives such as the Human Brain Project (www.humanbrainproject.eu), the Allen Institute (www.alleninstitute.org) and the Japan Brain/MINDS Project (http://brainminds.jp/en/) which deposit their findings into publicly available databases. Gaining insight from this d...

With biological systems it is often hard to adequately sample the entire input space. With sensory neural systems this can be a particularly acute problem, with very high dimensional natural inputs and typically sparse spiking outputs. Here we present an information theory based approach to analyse spiking data of an early sensory pathway, demonstr...

Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we presen...

Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behaviour. An array of opsins have been genetically isolated from several families of organism, including algae and bacteria, with a wide range of temporal and spectral properties. In an effort to develop more effective and tailored opsins, hy...

This demonstration shows a new type of front end for a Retinal Prosthesis/Vision Augmentation (RP/VA) System, as well as a Visual to Auditory Sensory Substitution Device (SSD). Each system serves to process visual scenes then present them in a simplified form (augmented with auditory signals) to assist visually impaired people. Both systems consist...

Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and tempo...

Learning to recognise objects and faces is an important and challenging problem tackled by the primate ventral visual system. One major difficulty lies in recognising an object despite profound differences in the retinal images it projects, due to changes in view, scale, position and other identity-preserving transformations. Several models of the...

Over successive stages, the ventral visual system of the primate brain develops neurons that respond selectively to particular objects or faces with translation, size and view invariance. The powerful neural representations found in Inferotemporal cortex form a remarkably rapid and robust basis for object recognition which belies the difficulties f...

We show how hand-centred visual representations could develop in the primate posterior parietal and premotor cortices during visually guided learning in a self-organizing neural network model. The model incorporates trace learning in the feed-forward synaptic connections between successive neuronal layers. Trace learning encourages neurons to learn...

This thesis aims to understand the learning mechanisms which underpin the process of visual object recognition in the primate ventral visual system. The computational crux of this problem lies in the ability to retain specificity to recognize particular objects or faces, while exhibiting generality across natural variations and distortions in the v...

The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible le...