Reverse-engineering Drosophila's retinal networks
Lead Research Organisation:
University of Sheffield
Department Name: Automatic Control and Systems Eng
Abstract
Vision is the most important sense of many living organisms. Even in the dimmest habitats animals have functional eyes, which allow them to extract useful optical information from the environment and to respond rapidly and appropriately to changing events and conditions. The anatomical structure, molecular signalling cascades and ultimately the performance of an organism's visual system, measured in terms of speed, sensitivity, dynamic range and robustness, has been tuned to suit its lifestyle by the content of the photic stimuli as well as other environmental factors. Vision of invertebrate species was the subject of extensive research which led to the discovery of important fundamental principles that apply to all senses. Drosophila visual system is an ideal model to investigate the mechanisms underlying early neural processing with a wealth of detailed knowledge from tens of years of genetic, anatomical, physiological and behavioural studies. The ultrastructure of the first visual synaptic layer in Drosophila has been fully described from electron-micrograph sections. A vast array of molecular genetic methods combined with biochemical analysis and intracellular recording techniques have converged on Drosophila's visual system providing unprecedented experimental tractability. These methodologies have made possible the identification of elements of its signalling cascades and offered unique insight into the associated regulatory mechanisms. The signal-processing capability of fly photoreceptors is prodigious, outperforming human engineered image sensors in many respects. They are exquisitely sensitive, being able to respond to single photon events. Weak input signals embedded in noise can be selectively amplified and filtered to provide efficient and reliable sensing of physiologically relevant stimuli. A fascinating functional attribute of photoreceptors, that is yet to be replicated in an engineering sensing device, is their ability to light adapt, i.e. adjust the amplification gain, according to both past and on-going light events, using many layers of positive- and negative-feedback control. This allows them to operate over a wide environmental range. They can reliably respond to the absorption of single photons under dark-adapted conditions, but can also adjust the gain to operate in bright daylight conditions. This project aims to develop, using mathematical tools and techniques borrowed from control and systems engineering, a detailed mathematical model the early vision system that will allow us to understand the role of different molecular components that are instrumental in converting light into electrical signals and the adaptation rules that fly photoreceptors must obey in order to operate reliably in dark as well as in full shinshine.
Technical Summary
This project aims to identify and characterize quantitatively, within a control and systems engineering framework, the regulatory mechanisms that underpin the robust operation of retinal circuits, the 'tuning' strategies by which these adapt to changes in their visual environment and the information theoretic goals that govern network adaptation. In order to accomplish these aims, we will develop a detailed biophysical model of the R-LMC-R visual processing module in Drosophila, using a data-driven reverse-engineering approach that we pioneered, which combines nonlinear system identification and frequency response techniques with conventional reductionist methods rooted in molecular biology.
Planned Impact
Beneficiaries The immediate beneficiary of knowledge arising from this research is anticipated to be its end-users in the wide scientific community, from biological sciences to engineering to applied mathematics. They will profit from increased understanding of the visual system of invertebrates. More specifically, the following groups have been identified as potential beneficiaries: Group A. Scientists researching sense systems, particularly vision Group B. Scientists, practitioners, companies involved in drug discovery/synthetic Biology Group C: Mathematical modellers, computational biologists Group D: Academics and engineers involved in the development of artificial eyes Communications and Engagement 1.Publications in peer-review journals and conferences (Groups A,B,C,D) 2.Development of a website detailing both the ultrastructure of te laminar cartridge and a on-line simulation model of the adaptive retinal network. (Groups A,B,D) 3.Development of a modular simulation model in matlab of Drosophila's phototransduction cascade that can be shared with other research groups and can be easily upgradable by other researchers.(Groups A,B,C) 4.Make contacts with research groups and companies involved in drug discovery and synthetic biology. (Groups B) 5.Make contacts with research groups, companies involved in robotic vision (Group D) 6.Disseminate results at workshops organized by the Sheffield Synthetic Biology Network (Group B)
Publications
Florescu D
(2019)
Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines.
in Neural computation
Song Z
(2021)
Multiscale 'whole-cell' models to study neural information processing - New insights from fly photoreceptor studies.
in Journal of neuroscience methods
Kemppainen J
(2022)
High-speed imaging of light-induced photoreceptor microsaccades in compound eyes.
in Communications biology
Kemppainen J
(2022)
Binocular mirror-symmetric microsaccadic sampling enables Drosophila hyperacute 3D vision.
in Proceedings of the National Academy of Sciences of the United States of America
| Description | We developed two complementary mathematical models of the R1-R6 photoreceptors. The first model (Model A), which was developed using nonlinear system identification methods, provides a control theoretic view of the photoreceptor. The model incorporates a gain control model which for the first time allows us to describe accurately the dynamics of the photoreceptor responses during adaptation and to characterize the contribution of different molecular mechanisms of adaptation that were revealed by previous studies. The second model (Model B) is a biophysically realistic photoreceptor model which characterizes in detail the major biochemical reactions involved in the phototransduction cascade within a single photoreceptor microvillus. The quantum bumps generated by ~30,000 of such semi-autonomous microvilli (sampling units) are integrated to generate a macroscopic current response which is fed into a detailed Hodgkin-Huxley-type model of photoreceptor's plasma-membrane, which incorporates a suite of voltage-sensitive potassium channels. Key findings based on Model A 1.Using the concept of Output Frequency Response Functions we demonstrate the role of nonlinearity in photoreceptor adaptation to different light intensity levels. 2.We showed that photoreceptors exploit nonlinearity to detect congruent phase structures embedded in the purely temporal stimuli. This demonstrates for the first time that in Drosophila the process of edge detection starts at the level of photoreceptor. It naturally raises the possibility that this mechanism has been conserved in higher organisms, including humans. 3.We have shown that fly photoreceptors are tuned so that Gaussian White Noise stimuli do not elicit nonlinear responses which suggest that the purely nonlinear response encodes key information received from the environment. We proposed a simple circuit that allows separating the purely nonlinear response from the overall photoreceptor response. 4. We have performed rate-distortion analysis for the linear, nonlinear and total photoreceptor responses to sequences of i.i.d. Bernoulli distributed pulses with different levels of additive white Gaussian noise, using the Hamming distance between the encoded and original pulse sequence as the distortion measure. We have shown that the nonlinear encoding of pulses is more efficient and robust than the linear encoding. Specifically, we have shown that for all input noise levels the nonlinear encoding of the pulse sequence by photoreceptors achieves lower distortion whilst requiring much lower bit rates compared with the linear encoding, leading to a significant improvement in the overall efficiency of the photoreceptor encoding i.e. the combined linear and nonlinear encoding outperforms linear encoding. In all three cases analysed the gap to the optimal performance theoretically attainable decreases monotonically as the input noise level increases. This shows that fly photoreceptors are tuned to perform efficiently in noisy environments. and highlights the key role played by nonlinear transductions in encoding efficiently and robustly the behaviourally relevant features. Key findings based on Model B 1.We have demonstrated that microvilli availability contributes to fast adaptation. Essentially our results imply that the dynamics of fast adaptation depends directly on the rhabdomere structure; that is, how many microvilli participate in the response. Thus, for the first time, sensory encoding of naturalistic stimuli is understood through simple adaptive sampling principles, set by the dynamic availability and variable response waveforms of the microvilli population. 2. We have shown that the stochasticity of the phototransduction cascade helps extend the light intensity range in which a photoreceptor can operate reliably as each microvillus produces bumps independently by stochastic reactions and its bump rate is set by its own negative feedback. 3.We demonstrated why and how the above findings predict the ultrastructure and information transfer of other fly photoreceptors. These results establish a new information theoretical framework of how phototransduction evolved and operates in rhabdomeric photoreceptors, crucially advancing our understanding of how sensory neurons sample and process information through stochastic computations. |
| Exploitation Route | The quantitative understanding of the phototransduction cascade in Drosophila could have major impact in drug discovery. It is worth emphasising that rhodopsin belongs to a large family of G-protein-coupled receptors (GPCRs) and that signal transduction in photoreceptors uses a generic, modular signalling design which is successfully used by eukaryotic cells, to detect and transduce into biologically relevant intracellular messages, all manner of external stimuli, including hormones, neurotransmitters, neuromodulators, odors, and light. GPCRs are particularly important pharmacologically, as they constitute largest gene family targeted for drug discovery. More recently, GPCRs have emerged as ideal candidates for engineering synthetic signalling systems. In this context, the models developed, the quantitative characterization of the regulatory mechanisms, the effects of modulations of these mechanisms by genetic manipulation on the overall system performance and the understanding of the underlying principles that governed the design of this particular phototransduction cascade, can have major implications beyond visual sensing. The immediate beneficiary of knowledge arising from this research is anticipated to be its endusers in the wide scientific community, from biological sciences to engineering to applied mathematics. Specifically, this work will benefit: 1- Scientists researching sense systems, particularly vision will profit from increased understanding of the visual system of invertebrates given that many fundamental principles that applied other senses were first discovered from studies in invertebrate vision. 2- Engineers involved in the development of miniaturized artificial compound eyes for robotic vision. We have actually started building what will be the most accurate model of a complete compound eye incorporating thousand of photoreceptors.The model can serve as a basis for the next generation of artificial compound eyes that are being built for miniaturized robotic applications. 3-The findings of this work would lead to new, efficient algorithms for coding and processing information streams. In computer vision the detection of congruent phases for feature detection requires complex algorithms, whereas by evolution, nature designed photoreceptors to perform this task as a relatively simple nonlinear filtering operation. An algorithm for designing such filters mathematically is currently not available. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology |
| URL | http://www.dflybrain.org |
| Description | Our photoreceptor model has been used to develop and simulate a model of Parkinson's disease as part of the Fruit Fly Brain Observatory. The photoreceptor model has been used subsequently to develop a full simulation model of the fly retina and lamina that allows exploring in a realistic setting fly models of Parkinson's disease that exhibit retinal degeneration. |
| First Year Of Impact | 2016 |
| Sector | Healthcare |
| Impact Types | Societal |
| Description | Microsoft Azure for Research |
| Amount | $5,000 (USD) |
| Funding ID | CRM:0518115 |
| Organisation | Microsoft Corporation |
| Sector | Public |
| Country | United States |
| Start | 01/2017 |
| End | 12/2018 |
| Description | Open Science Prize |
| Amount | $80,000 (USD) |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 04/2016 |
| End | 12/2016 |
| Description | Responsive Mode |
| Amount | £615,105 (GBP) |
| Funding ID | BB/K010123/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2013 |
| End | 04/2016 |
| Title | FlyBrainLab |
| Description | FlyBrainLab is an interactive computing platform for studying the function of executable circuits constructed from fruit fly brain data. FlyBrainLab is designed with three main capabilities in mind: (i) 3D exploration and visualization of fruit fly brain data, (ii) creation of executable circuits directly from the explored and visualized fly brain data, and (iii) interactive exploration of the functional logic of the devised executable circuits. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Too early to quantify |
| URL | https://flybrainlab.fruitflybrain.org/ |
| Title | Method for modelling adaptation in sensory systems |
| Description | Adaptation is a fundamental characteristic of sensory processing. It enables sensory neurones to map efficiently the extensive range of environmental signals onto their limited dynamic range in order to prevent saturation and to maximize the amount of information collected. We developed a novel two-step approach for identifying both the nonlinear dynamical model and the time evolution of the gain of a self-adaptive sensory system based on experimental data. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2012 |
| Provided To Others? | Yes |
| Impact | We have demonstrated that the processing of visual stimuli at photoreceptor level involves nonlinear transformations which extract and encode efficiently the biologically relevant higher-order statistical properties of natural stimuli. In particular, we have shown that photoreceptors detect and encode local phase correlations, which occur at the location of an edge or line, as well as long-range phase correlations, which characterize symmetry and texture properties of natural images. |
| URL | http://www.dflybrain.org/GainControlModel.html |
| Title | Biophysical model of fly photoreceptor |
| Description | In fly photoreceptors, light is focused onto a photosensitive waveguide, the rhabdomere, consisting of tens of thousands of microvilli. Each microvillus is capable of generating elementary responses, quantum bumps, in response to single photons using a stochastically operating phototransduction cascade. Whereas much is known about the cascade reactions, less is known about how the concerted action of the microvilli population encodes light changes into neural information and how the ultrastructure and biochemical machinery of photoreceptors of flies and other insects evolved in relation to the information sampling and processing they perform. To answer these questions, we generated biophysically realistic fly photoreceptor models, which accurately simulate the encoding of visual information. The photoreceptor model consists of four modules: 1. Random photon absorption model. 2. Stochastic differential equations model for the phototransduction cascade in a single microvillus. 3. Model of the light induced current generated by 30,000 microvilli. 4. A Hodgkin-Huxley model of the photoreceptor cell membrane |
| Type Of Material | Computer model/algorithm |
| Provided To Others? | No |
| Impact | Understanding the impact of microvilli numbers on the visual performance of different insects including fruit flies, blow flies and hunter flies. |
| URL | http://www.dflybrain.org/BiophysicalModel.html |
| Title | Empirical photoreceptor model |
| Description | We derived the most accurate photoreceptor model from the electrophysiological recordings using nonlinear system identification, optimization and control theory. The photoreceptor model consists of a nonlinear dynamical model with variable input gain, complemented by a dynamic gain control model with three adaptation time-scales, which captures the full dynamic range of adaptation observed in photoreceptors. Compared with previous models of fly photoreceptorsour model predicts accurately neural responses to arbitrary stimuli over the entire environmental range of light intensities, apart from the very dark L-4 stimuli range for which the responses are dominated by noise. Moreover, the structure of the model allows us to derive analytically the higher-order frequency response functions. The model was validated extensively using data recordings obtained from different photoreceptor cells, using naturalistic as well as white noise stimuli. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2014 |
| Provided To Others? | Yes |
| Impact | We have demonstrated that the processing of visual stimuli at photoreceptor level involves nonlinear transformations which extract and encode efficiently the biologically relevant higher-order statistical properties of natural stimuli. In particular, we have shown that photoreceptors detect and encode local phase correlations, which occur at the location of an edge or line, as well as long-range phase correlations, which characterize symmetry and texture properties of natural images. We speculate that human photoreceptors implement similar nonlinear processing of the visual stimuli to detect phase congruency, which explains not only why neurons in the primary visual cortex can reliably signal phase congruence but also how the phase congruency information is extracted from the visual stimuli. We demonstrated that photoreceptors implement a time-invariant nonlinear filter which is tuned to respond linearly to non-informative white noise stimuli and nonlinearly to naturalistic stimuli sequences exhibiting local and global temporal phase correlations. An important conclusion of our analysis is that photoreceptors do not need to adapt dynamically to the higher-order statistics of the stimuli. We demonstrated that photoreceptors implement a time-invariant nonlinear filter which is tuned to respond linearly to non-informative white noise stimuli and nonlinearly to naturalistic stimuli sequences exhibiting local and global temporal phase correlations. This appears to be an optimal strategy for processing visual stimuli since it does not require dynamic adaptation to stimuli statistics beyond the mean and variance, thus minimizing the energetic costs associated with phototransduction. To ensure robustness to noise, the nonlinear encoding mechanisms implemented by fly photoreceptors appear to be optimized by evolution and natural selection to maximize the sensitivity to behaviorally significant higher order statistical features of the stimuli and to minimise sensitivity to non-informative random phase stimuli. This explains the differences in coding naturalistic and white noise signals and also why the models derived using responses to white noise stimuli fail to capture the key nonlinear transformations performed by photoreceptors. |
| URL | http://www.dflybrain.org/GainControlModel.html |
| Description | Bionet |
| Organisation | Columbia University |
| Department | Electrical Engineering |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | We developed, and refined models of the retina for simulation on the Neurokernel platform. |
| Collaborator Contribution | Developed the initial open source simulation platform Neurokernel. Provided tools and resources for our group to use. |
| Impact | Open Science Prize Application This is a multi-disciplinary collaboration to develop models and a software platform for simulating the entire brain of the fruit fly: biology, computer science, control and systems engineering |
| Start Year | 2015 |
| Description | NVIDIA |
| Organisation | NVIDIA |
| Country | Global |
| Sector | Private |
| PI Contribution | Evaluation of new GPU architectures |
| Collaborator Contribution | Training and technical support. In-kind contribution of £30K |
| Impact | None yet |
| Start Year | 2015 |
| Description | Oxford |
| Organisation | University of Oxford |
| Department | Department of Physiology, Anatomy and Genetics |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We are developing modelling and simulation tools to make experimentally plausible and accurate models of the fruit fly brain. |
| Collaborator Contribution | They support the drosophila brain project with data, evaluation & testing of the software, and to help validate models. |
| Impact | No outputs yet. Multi-disciplinary: biology, systems engineering, control engineering |
| Start Year | 2015 |
| Title | High Order Frequency Response Analysis Toolbox |
| Description | Matlab toolbox for computing Higher Order Frequency Response Functions |
| Type Of Technology | Software |
| Year Produced | 2013 |
| Open Source License? | Yes |
| Impact | We use it to characterise the nonlinear transformations performed by photoreceptors |
| URL | http://www.dflybrain.org/FrequencyDomainMethods.html |
| Title | System Identification Toolbox for Adaptive Sensory Systems |
| Description | Matlab toolbox |
| Type Of Technology | Software |
| Year Produced | 2012 |
| Impact | Development of the most acsurate photoreceptor model |
| URL | http://www.dflybrain.org/GainControlModel.html |
| Description | Atlanta Talk: Nonlinear Mechanisms for Phase Congruency Detection in Fly Photoreceptors |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited Talk: Workshop: Methods of System Identification for Studying Information Processing in Sensory Systems Computational Neuroscience Conference CNS 2012, Decatur, Atlanta, USA. New project partner https://med.stanford.edu/profiles/thomas-clandinin |
| Year(s) Of Engagement Activity | 2012 |
| URL | http://www.cnsorg.org/cns-2012-atlantadecatur |
| Description | CNS2013 Paris |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | Yes |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Engaging talk with Prof Aurel Lazar, Columbia University and Prof Simon Laughlin, Cambridge University. New links established with Labs in Stanford |
| Year(s) Of Engagement Activity | 2013 |
| URL | http://www.cnsorg.org/cns-2013-paris |
| Description | Cosyne 2013 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This work help establish a research partnership with the Bionet Group at Columbia University. The aim of the collaboration is to develop a collaborative modelling and simulation platform for the entire fruit fly brain. no actual impacts realised to date |
| Year(s) Of Engagement Activity | 2013 |
| URL | http://www.cosyne.org/c/index.php?title=Cosyne_13 |
| Description | Cosyne 2017 Computational Neuroscience Conference |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | We had the opportunity to demonstrate the Fruit Fly Brain Observatory prototype- our entry for the final of the Open Science Prize (http://fruitflybrain.org/) |
| Year(s) Of Engagement Activity | 2017 |
| Description | Cosyne2014 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Q&A;Discussions Unknown |
| Year(s) Of Engagement Activity | 2014 |
| URL | http://cosyne.org/cosyne14/Cosyne2014_program_book.pdf |
| Description | Cosyne2016 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Undergraduate students were invited for the first time to attend |
| Year(s) Of Engagement Activity | 2016 |
| Description | Fly Brain Hackathon 2016 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Workshop organised at Columbia University in New York aimed at providing training and introducing participants to the modelling, simulation and visualisation tools developed as part of this project. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.bionet.ee.columbia.edu/hackathons/ffbh/2016 |
| Description | Fruit Fly Brain Hackathon 2017 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | The 2nd Fruit Fly Brain Hackathon (FFBO 2017) and the first to feature the Fruit Fly Brain Observatory (FFBO) and its key components NeuroNLP and NeuroGFX. The former allows for exploring fruit fly brain data using plain English queries, and the latter facilitates the modeling and execution of such brain circuits. Brief tutorials will be given on the usage of the FFBO as well as developing new tools/features in FFBO. The hackathon is aimed at three main groups of participants: neurobiologists, modelers and software engineers. The goal of the hackathon is to bring together these three groups of participants to develop, use and improve the FFBO platform towards developing executable models of the fruit fly brain. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://www.bionet.ee.columbia.edu/hackathons/ffbh/2017 |
| Description | Fruit Fly Brain Hackathon 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | We are pleased to announce the 3rd Fruit Fly Brain Hackathon (FFBH 2018). This hackathon will be focused on open neurophysiology data (electrophysiology, imaging, etc.) of, but not limited to, the fruit fly brain. The hackathon will be focused on the following topics: Promoting Neurodata Without Board (NWB) as a standard format for storing and sharing neurophysiology data in general, and extend it to standardize the storage and distribution of fruit fly brain electrophysiology and calcium imaging data, Queryable neurophysiology data: organizing neurophysiology data and metadata in a database to make them easily queryable, and developing comprehensive querying tools accordingly, Incorporating queried neurophysiology data into computational study. The goals are to: create a common language for sharing open neurophysiology data, speed up pattern search in the data that can often be tedious and inefficient to do manually, drive the creation of computational models and the validation of them using open data. The hackathon is aimed at three main groups of participants: neurobiologists, modelers and software engineers. We welcome researchers working on the fruit fly brain as well as those working on other model organisms to participate and broaden the discussion in the hackathon. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://www.bionet.ee.columbia.edu/hackathons/ffbh/2018 |
| Description | Off the Shelf Festival |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | Yes |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Here is an email received afterwards TERENCE SAYLES 23 Oct Excellent work by you and your team! My own efforts over a 43 year period (I am 68yrs old) were mainly connected with measurement and control in local process industry and included 22 yrs lecturing in F.E. up to HNC level 3. (Maths, Electrical & Electronics, some computing). My academic studies included C&G's(Instrumentation, Measurement, Control), HNC(Electrical, Electronics, Control), BSc(Hons) Engineering Systems & Control, BA(Open) Technology, Mathematics, PGCE Teaching Cert. Although no longer active in the 'field' I still have a keen interest in the subjects and also related applications in biological/social apps.Eg. the recent report of Professor Raisman's work in transplanting olfactory cells to successfully repair a severed spine, in fact better than he anticipated. Another example of the nervous system adapting to help itself? Perhaps in the future I may see your teams projects on a TV 'Horizon' program or similar, they would surely have a wide public interest. Felicitations, Terence G. Sayles C.Eng. M.Inst.MC. None identified so far. |
| Year(s) Of Engagement Activity | 2014 |
| URL | http://www.welcometosheffield.co.uk/visit/off-the-shelf |
| Description | Open Data Science Symposium: How Open Data and Open Science are Transforming Biomedical Research |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Funding agencies and biomedical research organizations are increasingly embracing Open Science research paradigms characterized by new models of collaboration, new modes of data sharing, and new requirements for making data publicly available. The growing availability of open biomedical data and cloud-based infrastructure is creating unprecedented opportunities for using biomedical Big Data in novel ways to help solve pressing public health and biomedical research challenges. This Symposium featured the Six Team Finalists of the Open Science Prize, an international prize competition led by the NIH Big Data to Knowledge (BD2K) Initiative, in partnership with the Howard Hughes Medical Institute and the UK-based Wellcome Trust, to support the development of innovative tools, products, and services that utilize Open Data. Our Fruit Fly Brain Observatory prototype was one of the Six Team Finalists (https://www.openscienceprize.org/res/p/finalists/). At this event, the six projects were made available the Open Science Prize website for the general public to vote. Public voting comments comments on our Fruit Fly Brain Observatory are summarized below: • Brain is the final frontier for sure. I really appreciate the effort the neuroscientists put into for better understanding brain and its function, especially for a moderate but achievable aim. • Such a fantastic competition. I certainly see that linking connectome data with functional imaging data will be the next great revolution in science, and the difficulty and significance will dwarf the findings from the genome project and its relation to gene function. While this is my opinion, I certainly believe that understanding brain function will be massively more difficult than gene function, particularly as gene function can be done one at time but brain function needs to understand multiple interconnecting circuits simultaneously just to understand one small part of the brain. This I would definitely promote the fruit fly brain observatory. • First, I think the brain researches help we human beings understand what we are, and what we can do next. Even a fruit fly's brain will tell lots of we never know before. Thus, I vote Fruit Fly as the 1st prize. • I am more fascinated with all teams. they have done wonderful job. we know invertebrate system has given us excellent opportunity to understand the brain. FFBO has given a really good step by open source platform which includes brain imaging techniques, connectivity, morphology and tracing pattern of neurons spanning throughout in brain which will permit us to comprehend the human brain function and its disease related functions that is the biggest challenge in neuroscience. • I am really convinced that the proper modelling could save time and resources in research. I liked the fact that the fruit fly observatory project could open other doors into the modelling and analysis of multiple diseases which in turn could lead to a quicker solution. • I believe that Fruit Fly Brain Observatory concept is were the future of the discovery in medicine - creating interactive prototypes/models that would help identifying roots of the problems and finding resolutions by experimenting with the model. Similarly, new drug discovered via candidates compounds modeling. • I like that the fruit fly brain observatory has very advanced technology that can allow researchers to perform genetic experiements using software. • I like the idea of mapping the brain like an electrical system. I had some trouble with the website functionality, and as an IT professional, that needs refined. I also participate in Alzheimer's research as a subject, and see value in anything that can help us defeat this awful disease and many brain diseases that have eluded us so far. • I vote for the Fruit Fly Brain Observatory since I think that this project has the potential to lead to ground breaking research and could have high impact. The project considers engineering aspects (such as modelling, simulation aspects and computational power) and medical aspects as well. • If one is to "reverse engineer" the workings of the brain, it makes a lot of sense to start with a simple organism (fruit fly) where one can study small scale neuron interactions. Furthermore, the fruit fly has been very widely studied and consolidating what is known will be very useful. I tried out the system and found it easy to use with very nice visuals. • Its really beautiful project and research. • Kudos to all who worked on these prototypes. I wish they could all win. I chose the ones that I hope will advance the efforts to prevent or cure Alzheimer's and other dementias. • Neurodegenerative diseases are more prevalent worldwide as the human population accumulates aged persons. The Drosophila model is excellent depiction of how we can address the neurodegenerative process in diseases like Alzheimer's and Parkinson's. Drosophila is an excellent model to understand the mechanism(s) of human disease pathologies. This team deserves the first prize. • Thanks for these great works on SciVis. I am glad to see people to use the power of visualization to improve human life. • Selected project will help cure mental disorder. • The data and tools of the Fruit Fly Brain Observatory are pretty awesome, extensive and forward looking! I believe it'll be a pathblazing tool for other organisms as well as they scale up in the years to come. • The fruit fly brain observatory is a very effective integration of known data and will be very useful to the international neuroscience community. • The Fruit Fly Brain Observatory is important because we don't want films like Contagion to happen in real life. We need to study brains. • The fruit fly brain observatory seems promising given that this research could unlock several mysteries to understand the human mind and fight serious degenerative diseases like Parkinson and Alzheimer. • The Fruit Fly Brain project, seems to have the potential to revolutionize research in blindness. • The human brain is the beginning and end of every human problem. To understand the brain is to understand avenues to both problems and solutions. • The most useful for my research! • Understanding how a brain works is one of the great challenge for this century. Given the complexity of the problem at hand, integrative and large scale approaches are needed to map the architecture. Fruit Fly Brain Observatory is an elegant platform attempting to address these immense challenges. By integrating massive amounts of neuroanatomy data in a common space, and making it approachable to other scientists will go a long way in contributing to the understanding of brain. • As recession hits the major markets, the funding for an important part of research (basic research) get cut down. I believe this type of modelling should continue no matter what the state of world's capital markets as this type of research is responsible for increasing the real knowledge of the mankind. • I am most interested in the Open Brain Imaging because I am battling brain cancer. I would love to see this projecs help advance neuroscience to help patients like me in the future. • There is value in all of the studies. I voted for the two that studied the brain. The brain is our key to life. It holds the secrets of mental illness, including depression, which is one of the largest causes of disability worldwide. Through learning more about the brain we will be able to provide more effective treatments, and hopefully find ways to prevent the development of mental and neurological illnesses. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://datascience.nih.gov/OpenDataScienceSymposiumCal |
| Description | Open Days ACSE |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Schools |
| Results and Impact | Presentations were given to visiting students applying to study for undergraduate degrees in Automatic Control and Systems Engineering. Around five events per year. |
| Year(s) Of Engagement Activity | 2016,2017,2018,2019 |
| Description | Talk UKACC2018 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Keynote talk at the 12th UKACC International Conference on Contro.l The scope of the conference is intended to be broad with coverage of theory and applications of control and systems engineering. The talk highlighted how existing methods and tools in control, nonlinear systems and information theory, including system identification, higher-order frequency response and rate-distortion analysis, can be combined with remarkable experimental approaches to elucidate gain adaptation mechanisms and the role of nonlinearity in early visual processing. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://control2018.group.shef.ac.uk/plenary-speakers/daniel-coca/ |
| Description | Vision in flies - Learning from biology |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other academic audiences (collaborators, peers etc.) |
| Results and Impact | On Friday 28th September 2012 the University of Sheffield held the 'Researchers' Night' event- an open evening to showcase University's research and facilities to the public. It was run as part of 'Festival of the Mind', which is a week-long festival to showcase the cultural strengths of the University of Sheffield and the City. As part of this event, one of the PDRA's on this grant gave a presentation aimed at a general audience in which he showed how engineering approaches, particularly systems and control engineering, can help us understand why biological systems are structured the way the are and what are the fundamental 'design' principles that underpin their operation. no actual impacts realised to date |
| Year(s) Of Engagement Activity | 2012 |