Centre for Computational Neuroscience and Robotics - CCNR

be.AI - Project Ideas

Supervisors have formulated project proposals that they have identified. be.AI is no longer recruiting but if you are interested in these topics and have ideas for an alternative source of funding, please contact the prospective supervisor in the first instance.

Risky decision-making: the neural computations underlying threat conflict

Supervisors: Kevin Staras, Michael Chrossley, and one of Andy Philippides, James Knight, or Thomas Nowotny

How do animals select appropriate actions when faced with conflicting threats, for example, predation and starvation? With increased hunger, a reprioritization of behaviours towards food-finding must be balanced against an elevated predation risk. The behavioural and neural mechanisms that underlie this are fascinating but completely uncharacterized. This project will combine the important simple-system model Lymnaea (Nat Commun 2016, 7:11793; Sci Adv 2018, 4:eaau9180) with state-of-the-art behavioural and neural activity assays, as well as computational modelling. This project will suit a student interested in developing skills in cellular neuroscience, cutting-edge behavioural assays, and circuit modelling approaches, to determine how animals balance risk and survival.

Apply for a PhD in Neuroscience

Understanding robot-animal interaction for autonomous navigation

Supervisors: Bao Kha Nguyen and Yanan Li

Autonomous navigation in dynamic environments has been extensively studied. Most of existing works model the robot’s environments based on the surrounding objects’ spatial locations or the temporal evolution of their movements. However, if a mobile robot navigates on an open farm surrounded by animals, these animals will likely change their movements according to the robot’s. In this sense, the bilateral adaptation between the robot and the animals needs to be investigated. This project will analyse animal behaviours and exploit learning algorithms to predict the animals’ movements in response to the robot’s. It will in turn enable the robot to effectively respond to and even affect the animals’ movements. This research will be useful for not only autonomous navigation of robots in dynamic environments but also for autonomous herding and management of animals.

Apply for a PhD in Engineering

Plasticity of visual computations

Supervisors: Prof. Leon Lagnado and Prof. Jeremy Niven

The synaptic connections that transfer information between neurons are likely to be crucial to the plasticity of computations in the brain but we have surprisingly little understanding of how. We study this problem in zebrafish, where turning decisions driven by vision are modulated by information arriving through other senses, such as smell. We use a combination of experiment (multiphoton imaging and behaviour), theory (information and signal detection) and modelling (biophysical) to understand how the plasticity of synapses in adjusts the computations that visual circuits carry out and the behaviours they drive. A number of projects can be offered depending on the applicants background e.g How does neuromodulation alter the efficiency with which synapses transfer information and how does this alter visually-driven motor decisions?

Apply for a PhD in Neuroscience

[1] Jose Moya-Diaz, Ben James, Federico Esposti, Jamie Johnston, Leon Lagnado (2021). Diurnal modulation of multivesicular release controls the efficiency of information transmission at a sensory synapse. bioRxiv doi: 10.1101/2021.09.12.459944.

[2] James, B., Darnet L., Moya-Diaz, J., Seibel, S-H., Lagnado, L. (2019). An amplitude code transmits information at a visual synapse. Nature Neuroscience, 22(7):1140-1147

Andreas M. Kist, Ruben Portugues, (2019). Optomotor Swimming in Larval Zebrafish Is Driven by Global Whole-Field Visual Motion and Local Light-Dark Transitions. Cell Reports, 29(3),659-670.

Understanding crowd psychology and safety through wearable technologies

Supervisors: Prof John Drury, Prof Daniel Roggen

Analysing and modelling the behaviour of people in crowds can help safely manage live events, improve response to emergencies, and inform the design of public spaces [1]. Wearable and mobile sensing now allows us to sense behaviour "in the wild" and in real-time [2] [3] [4] [5]. This project would bring together novel wearable and mobile sensing with concepts from group psychology to understand co-action and coordinated behaviour in pedestrian flow. The ideal student will have strong computing skills and a background in computer science, maths, physics or psychology. They will be interested in research at the interface between psychology and computing.

Apply for a PhD in Psychology

[1] Kleinmeier, Köster and Drury, Agent-based simulation of collective cooperation: from experiment to model. J. R. Soc. Interface, 2020

[2] Wirz et al. Probing crowd density through smartphones in city-scale mass gatherings. EPJ Data Science, 2(5):1-24, 2013.

[3] Wang et al. Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. IEEE Access, 2019

[4] Chavarriaga et al. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 2013

[5] Templeton, A., Drury, J., Philippides, A. (2018). Walking together: Behavioural signatures of psychological crowds. Royal Society Open Science 5, 180172.

Using AI to improve autonomy in laser-based surgical robots

Supervisors: Rodrigo Aviles and Elizabeth Rendon

Future autonomous surgical robots will have the ability to “see”, “think” and “act” without active human intervention to achieve a predetermined surgical goal safely and effectively. The “robot’s surgical skill” consists of its ability to first map its perception (i.e. sensory inputs) to an estimated environmental state, and then map that estimate to a future action ( i.e robotic output) in the most efficient way possible. Machine learning (ML) is proposed as means to control the actions of autonomous devices [1]. Appropriately trained algorithms can enable robots presented with novel yet similar data, to predict an outcome, and then achieve its task in real time based upon its “experience”. If the sensory stream is of comparable fidelity to human senses, such analytical algorithms will demonstrate superiority over human perception.

This project will investigate types of machine learning algorithms relevant to train autonomous surgical devices. Combinations of supervised, unsupervised and reinforcement ML techniques will be investigated together with the development of advanced computer vision analysis and recognition models, to produce an autonomous device with the versatility required to perform a range of soft-tissue surgical procedures [2] using laser-based ablation for tumour resection.

Apply for a PhD in Engineering

[1] Kassahun, Y., Yu, B., Tibebu, A.T., Stoyanov, D., Giannarou, S., Metzen, J.H., Vander Poorten, E., 2016. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. International Journal of Computer Assisted Radiology and Surgery 11, 553–568.

[2] Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S., 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118.

Investigating neuromorphic devices to improve sensing, vision & control in surgical robots

Supervisors: Elizabeth Rendon and Rodrigo Aviles

From Deep Blue to AlphaGo, artificial intelligence and neural networks have become an important research direction. Neuromorphic computing is a complete rethinking of computer architecture from the bottom up. The goal is to apply the latest insights from neuroscience to create integrated circuits that function like the human brain. Neuromorphic devices replicate the way neurons are organized, communicate, and learn at the hardware level. Future neuromorphic processors are defining a new model of programmable computing.

This project investigates the integration of sensory and neuromorphic devices, to meet the sensing and learning capabilities of neural systems, by which can make the systems sense external stimulus and store the relevant information [1]. Novel neuromorphic processor integrated circuits (IC) – structural design and optimisation-, will be studied to perform neurophysiological simulations based on memristors. Sensing, imaging, and control systems used in surgical robotics will be integrated [2] and run with a xx-neuron spiking network at selected frequencies over 250 kilohertz. The system will be evaluated for power consumption (energy efficiency), latency, structural design and its performance will be compared with available hardware accelerators.

Apply for a PhD in Engineering

[1] Zeng M, He Y, Zhang C, Wan Q. Neuromorphic Devices for Bionic Sensing and Perception. Front Neurosci. 2021;15:690950.

[2] Kim, W.S., Paik, J., 2021. Soft Bionic Sensors and Actuators. Advanced Intelligent Systems 3, 2100003.

Sensing-acting loops in the brain: how does sensory cortex incorporate information about actions and outcomes?

Supervisors: Dr Andre Maia Chagas, Prof Miguel Maravall

A classical view of sensory processing, which has often inspired hierarchical architectures for information processing, holds that neurons in sensory pathways up into the cerebral cortex respond to sensory features and that experience-dependent learning primarily involves refining these sensory responses. In contrast, recent discoveries from our group and others [1] have shown that learning a goal-directed task can cause neurons in “sensory” areas of the cortex to develop non-sensory responses to, e.g., the actions performed by the animal and even to whether those actions get rewarded. Thus sensory cortex is not just a “front end” filtering and passing sensory evidence to higher areas, but is actively involved in task processing. This implies that an animal does not sense the world independently of what it needs to feel to guide behaviour.

This project will explore the neuronal circuits underlying these complex responses, and how they depend on task properties. You will train mice to carry out different sensory-guided tasks altering the relationship of a sensory stimulus to potential actions. You will record and manipulate neuronal activity using state-of-the-art methods [2] and optimise your own equipment and tools to control and measure mouse behaviour. The project will emphasise the use of open hardware approaches to lab equipment development and dissemination [3]. We particularly encourage applications from students who are interested in this exciting and powerful methodological approach and have a quantitative background.

Apply for a PhD in Neuroscience

[1] Bale MR, Bitzidou M, Giusto E, Kinghorn P, Maravall M (2021) Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex. Current Biology

[2] Janiak FK, Bartel P, Bale MR, … Maravall M, Baden T (2019) Divergent excitation two photon microscopy for 3D random access mesoscale imaging at single cell resolution. bioRxiv

[3] Maia Chagas A (2018) Haves and have nots must find a better way: The case for open scientific hardware. PLoS Biol 16(9): e3000014.

Spiking machine learning

Supervisors: Thomas Nowotny and James Knight

Recently, new methods have been developed to train spiking neural networks on machine learning tasks, such as SuperSpike (feed-forward networks [1]), eProp (approximate gradient descent for recurrent networks [2]) and EventProp (exact gradient descent [3]). In this project you will use these, and other methods, in our GPU accelerated spiking neural network simulation framework GeNN [4,5] to solve challenging machine learning tasks, for instance scene understanding based on stereo event-based camera inputs.

Apply for a PhD in Informatics

[1] Friedemann Zenke, Surya Ganguli; SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. Neural Comput 2018; 30 (6): 1514–1541.

[2] Bellec, G., Scherr, F., Subramoney, A. et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nat Commun 11, 3625 (2020).

[3] Wunderlich, T.C., Pehle, C. Event-based backpropagation can compute exact gradients for spiking neural networks. Sci Rep 11, 12829 (2021).

[4] Yavuz, E., Turner, J. & Nowotny, T. GeNN: a code generation framework for accelerated brain simulations. Sci Rep 6, 18854 (2016).

[5] http://genn-team.github.io/genn/, accessed 2021-11-30

Embodied Trustworthy AI Agents

Supervisors: Dr Novi Quadrianto, Prof Andy PhilippidesProf Thomas Nowotny

Machine learning is already involved in decision-making processes that affect peoples’ lives. Efficiency can be improved, costs can be reduced, and personalization of services and products can be greatly enhanced. However, concerns are rising about how to ensure that deployment of automated systems will follow clear, useful principles and requirements of trustworthiness [1]. One example of this is algorithmic fairness in a dynamic setting [2] which has a time-varying component so is best dealt with as a situated and embodied problem. In this project, you will therefore combine our initial work on fairness, transparency, and robustness [3] with symbol grounding, embodiment and situatedness needing a physical body [4] contributing to the work of the PAL lab [5] as well as being a member of the be.AI Leverhulme centre.

Apply for a PhD in Informatics

[1] High-Level Expert Group on Artificial Intelligence, Ethics guidelines for trustworthy AI, November 2020.

[2] A. D’Amour, H. Srinivasan, J. Atwood, P. Baljekar, D. Sculley, and Y. Halpern. Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 525–534, 2020.

[3] ERC Starting grant: Bayesian Models and Algorithms for Fairness and Transparency

[4] ActiveAI - active learning and selective attention for robust, transparent and efficient AI

[5] PAL lab

Fall propensity prediction through complexity measures and wearable technologies

Supervisors: Prof. Daniel Roggen, Prof. Luc Berthouze, + clinical partner to be identified

Falls are a major problem in the elderly as well as a number of clinical populations (Parkinson's Disease for example). Falls impact both on quality of life and health, but also incur major costs to the NHS. The aim of this project is to improve risk stratification early by quantifying/sensorising current assessments through: (a) using wearable sensors (e.g., [1]) to monitor patients during daily life (i.e., in the home rather than in a clinical setting) and extract information regarding their balance; and (b) further developing our signal processing techniques (e.g., [2-3]) focused on characterising variability in postural stability and use machine learning to build a predictive model. This project would suit a student interested in research at the interface between neuroscience, mathematics and engineering.

Apply for a PhD in Informatics

[1] Oishi, N., Heimler, B., Pellatt, L., Plotnik, M., & Roggen, D. (2021, September). Detecting Freezing of Gait with Earables Trained from VR Motion Capture Data. In 2021 International Symposium on Wearable Computers (pp. 33-37).

[2] Berthouze, L., & Farmer, S. F. (2012). Adaptive time-varying detrended fluctuation analysis. Journal of neuroscience methods, 209(1):178-188.

[3] West, Farmer, Berthouze et al. (2016). The Parkinsonian subthalamic network: Measures of power, linear, and non-linear synchronization and their relationship to L-DOPA treatment and OFF state motor severity. Frontiers in Human Neuroscience 10:517.

Collective Intelligence in Animal Construction

Supervisors: Prof Paul Graham and a supervisor from Informatics

Animals demonstrate swarm intelligence in group foraging, decision making and construction. Such natural collective behaviours show many characteristics that are desirable for engineered systems such as robustness, scalability, fault tolerance and flexibility. Thus understanding natural collective behaviour is an exciting opportunity for AI and robotics, alongside the inherent biological fascination. We will examine nest construction in wood ants, whose nest mounds are large impressive structures. Unlike well-studied construction with adhesive materials, such as termite mounds built from mud or wasp nests built with wood pulp paper, wood ant nests are built from twigs litter and other loose materials. Construction with loose materials not only requires complex manipulation skills at the level of individual ants, but also collective processes to deal with stochasticity in the positions of the component parts of the nest. This multidisciplinary project will combine field observations, lab studies and modelling to provide new insights into collective construction. Applicants are encouraged if they have relevant experience in any of these areas and are keen to learn new skills.

Apply for a PhD in Biology

Mapping the origins of animal movement and motor coordination

Supervisors: Prof Claudio R. Alonso, Dr Lucia Prieto-Godino and Prof Luc Berthouze

Movement is a defining trait of animals and robotic systems, but how the neural networks in the developing animal brain manage to adopt their specific connectivities, and generate and modulate their activities to control movement, is currently unknown. In particular, we still do not know what controls the transitions between the uncoordinated motor activities in developing embryos, into the precise patterns of movement observed in fully-formed organisms. This project will exploit the simplicity and genetic accessibility of the fruit fly Drosophila melanogaster to investigate the origins of animal movement, and use computational modelling to generate testable hypotheses on circuit structure and coordination. For this we will combine modern experimental work (genetic, optogenetic, advanced microscopy, deep neural network quantitative behavioural analysis), with state-of-the-art modelling approaches (neural selection, signal processing, time-series and dynamical systems analyses). This genuinely interdisciplinary effort will thus help us define the mechanisms underlying the emergence of coordinated movement, and extract core principles for the design of effective robotic locomotion and adaptation.

Apply for a PhD in Neuroscience


[1] Picao-Osorio, J, Johnston, J., Landgraf, M., Berni, J. and Alonso, C.R. (2015) microRNA encoded behavior in Drosophila, Science 350:815-20.

[2] Issa, A.R., Picao-Osorio, J., Rito, N., Chiappe, M.E. and Alonso, C.R. (2019) A Single MicroRNA-Hox Gene Module Controls Equivalent Movements in Biomechanically Distinct Forms of Drosophila, Current Biology 29:2665-2675.e4.

[3] Prieto-Godino, L., Diegelmann, S. and Bate, M. (2012) Embryonic Origin of Olfactory Circuitry in Drosophila: Contact and Activity-Mediated Interactions Pattern Connectivity in the Antennal Lobe, PLoS Biology 10(10):e1001400.

[4] Berthouze, L. and Goldfield E.G. (2008) Assembly, tuning, and transfer of action systems in infants and robots, Infant and Child Development 17:25-42.

[5] Hartley, C, Farmer, S. and Berthouze, L. (2020) Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex, PLoS One 15(1):e0226772.

[6] Loveless. J., Garner, A., Issa, A.R., Roberts, R., Webb, B., Prieto-Godino, L., Ohyama, T. and Alonso, C.R. (2020) A physical theory of larval Drosophila behaviour BioRxiv doi.org/10.1101/2020.08.25.266163.

Mimicking Neuro-Biomechanical Nonlinear Dynamics to Control Movements in Sliding Surface Joints

Supervisors: Dr Carlo Tiseo and Dr Jimena Berni

Animals exploit the intrinsic properties of their dynamics in their interaction with the world. Many combinations between rigid and continuum mechanisms are present in nature, allowing animals to solve complex interaction problems robustly and efficiently in challenging, unknown environments. These conditions are challenging for most artificial controllers, which are often fragile and computationally onerous due to the lack of an accurate system dynamics model (i.e., body + environment). The project explores recent advantages in nonlinear model-free control to develop hybrid mechanisms (i.e., rigid + continuum dynamics) to interact with challenging external environments. Specifically, we will investigate the properties of sliding surface joints tuned by an interconnected lattice of tendons similar to the vertebrate biomechanics structure. In parallel, we will develop algorithms and controllers to replicate the patterned behaviour generated by the spinal cord to coordinate joint actuation and movement. Bringing these two aspects together, we will build robots exploiting neuro-biomechanical nonlinear dynamics to develop control continuum mechanisms.

Apply for a PhD in Engineering


[1] Pulver SR, Bayley TG, Taylor AL, Berni J, Bate M, Hedwig B. Imaging fictive locomotor patterns in larval Drosophila. Journal of Neurophysioly 114(5), 2564-77 (2015)

[2] Berni J. Genetic dissection of a regionally differentiated network for exploratory behavior in Drosophila larvae. Current Biology 25(10), 1319-26 (2015)

[3] J. Gjorgjieva, J. Berni, J.F. Evers, S. Eglen. Neural Circuits for Peristaltic Wave Propagation in Crawling Drosophila Larvae: Analysis and Modeling. Front. Comput. Neurosc., 2013,7:1-19

[4] C. Tiseo, V. Ivan, W. Merkt, I. Havoutis, M. Mistry and S. Vijayakumar, “A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 8223-8229, doi: 10.1109/ICRA48506.2021.9561377.

[5] Babarahmati, K.K., Tiseo, C., Smith, J. et al. Fractal impedance for passive controllers: a framework for interaction robotics. Nonlinear Dyn (2022). https://doi.org/10.1007/s11071-022-07754-3

Online learning of human dynamics for personalized physical human-robot interaction

Supervisors: Dr Yanan Li and Dr. Bao Kha Nguyen

In physical human-robot interaction such as in robot-assisted rehabilitation, collaborative manipulation, teleoperation, etc, learning/identification of human dynamics is essential for personalized robotic assistance but challenging [1]. While offline learning is less useful in many tasks, existing online learning approaches rely on strict conditions on input signals, e.g., repetitive trajectories or persistently exciting signals [2], which limit their use in physical human-robot interaction. This project will propose a new online learning approach based on neural networks, recursive least squares, and a novel notion of “selective memory” [3]. This approach will achieve online identification of human dynamics in tasks with random trajectories and use this information to design personalized robotic assistance. Its validity will be verified through typical applications of physical human-robot interaction, e.g., robot-assisted rehabilitation.

Apply for a PhD in Engineering


[1] Naceri, A., Schumacher, T., Li, Q., Calinon, S., & Ritter, H. (2021). Learning optimal impedance control during complex 3D arm movements. IEEE Robotics and Automation Letters, 6(2), 1248-1255.

[2] Wang, C., & Hill, D. J. (2018). Deterministic Learning Theory: For Identification, Recognition, and Conirol. CRC Press.

[3] Fei, Y., Li, J., & Li, Y. (2022). Selective Memory Recursive Least Squares: Uniformly Allocated Approximation Capabilities of RBF Neural Networks in Real-Time Learning. arXiv.2211.07909

An electronic cochlea - finding information in a noisy environment

Supervisors: Prof Peter Fussey and Dr Ediz Sohoglu

With increases in data capture across society it is increasingly important to have efficient and low power solutions to process signals and extract information. Working in a noisy environment is a common challenge, where the noise may be acoustic noise, radio frequencies or other time varying signals.

The human ear is a refined, low power system that is able to analyse a wide range of frequencies and sound levels - allowing humans to focus in on a conversation in a noisy room for example.

This project will investigate different approaches to modelling and replicating the human ear features, specifically the cochlea and the integration of the signals from each ear with themselves and into the brain [1] to provide insight into how humans can boost the signal to noise ratio of signals that would otherwise be hidden. The research will investigate analogue computing approaches, for example using FGAAs, coupled with advances in FPGAs to prototype new neuromorphic computing structures.

This work could then lead to refinements to signal processing for speech recognition [2] and [3] but also be applied to other areas where signals are hidden in the background noise [4].

Apply for a PhD in Engineering


[1] R. F. Lyon and C. Mead, "An analog electronic cochlea," in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 7, pp. 1119-1134, July 1988, doi: 10.1109/29.1639.

[2] C. Gao, S. Braun, I. Kiselev, J. Anumula, T. Delbruck and S. -C. Liu, "Real-Time Speech Recognition for IoT Purpose using a Delta Recurrent Neural Network Accelerator," 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, pp. 1-5, doi: 10.1109/ISCAS.2019.8702290.

[3] S. C. Liu, A. van Schaik, B. A. Minch and T. Delbruck, "Asynchronous Binaural Spatial Audition Sensor With 2 x 64 x 4 Channel Output," in IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 4, pp. 453-464, Aug. 2014, doi: 10.1109/TBCAS.2013.2281834.

[4] S. Mandal, S. M. Zhak and R. Sarpeshkar, "A Bio-Inspired Active Radio-Frequency Silicon Cochlea," in IEEE Journal of Solid-State Circuits, vol. 44, no. 6, pp. 1814-1828, June 2009, doi: 10.1109/JSSC.2009.2020465.

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