Quantitative Approaches to Circuit Neuroscience and Behaviour
ABSTRACT
Background and Purpose
Understanding how neural circuits process information and support behaviour requires experimental and computational approaches to quantify neuronal activity, structure and behavioural responses. This project will apply signal processing, image analysis and machine learning to complex biological datasets within the laboratory’s broader programme of circuit neuroscience.
Students will undertake two complementary subprojects. The first will analyse in vivo two-photon imaging recordings of the mouse brain obtained using calcium and voltage indicators. It will explore methods for denoising recordings, characterising neuronal signal waveforms, and quantifying neuronal architecture and spatiotemporal patterns of activity.
The second will develop quantitative approaches to studying octopus responses to looming visual stimuli. It will combine behavioural experiments with video analysis to measure movement, posture and response dynamics, including adapting automated tracking and pose-estimation methods to the octopus’s flexible body.
Research Questions
1. How can signal-processing and machine-learning methods improve the analysis of neural and behavioural recordings while preserving biologically meaningful information?
2. Which waveform, structural and spatiotemporal features can reliably characterise neuronal activity in calcium and voltage imaging recordings?
3. How do octopuses respond to looming stimuli, and which quantitative measures best describe these responses?
4. How can automated tracking methods be adapted and validated for octopus behaviour, and do the observed responses share features with those of vertebrates?
Expected Outcomes
Each student will complete a defined subproject involving literature review, data analysis, method development and regular discussions with laboratory researchers. Expected outputs include a documented and reproducible analysis workflow, quantitative results addressing the selected research questions, and a research poster summarising the methods, findings and limitations. Students will develop practical skills in computational neuroscience, critical evaluation of analytical methods, interpretation of biological data and scientific communication. Their work will contribute to the laboratory’s capacity to quantify neural activity and behaviour across different experimental systems.
SKILLS & EXPERIENCE REQUIRED
1. Undergraduate study in biomedical sciences, neuroscience, biological sciences, engineering, computer science, mathematics, or a related discipline.
2. Interest in neuroscience, biological imaging, animal behaviour, or computational analysis.
3. Basic programming experience in Python or MATLAB is desirable.
4. Basic knowledge of statistics and quantitative data analysis is desirable.
5. Willingness to learn signal processing, image analysis, or machine-learning methods.
6. Attention to detail in data handling and research documentation.
7. Ability to work independently on defined tasks and seek guidance when needed.
8. Good communication and teamwork skills.
9. Commitment to regular project work and supervisory meetings throughout the research period.