The following are examples of relevant modules that are currently available for PhD students to audit (these may be subject to change for subsequent years):
- Imaging in Brain Diseases (Autumn)
You’ll focus on the way neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), are used to provide evidence on pathological processes in the brain.
Topics will include the basic physical principles of MRI and PET, and how are these two modalities are used to provide qualitative and quantitative information on:
You’ll also explore how changes in these domains are linked to pathological mechanisms in brain diseases, including:
neurodegenerative and neurodevelopmental diseases
brain tumours and strokes.
- Intelligence in Animals and Machines (Autumn)
The module will develop understanding of what it means for an animal or a machine to behave intelligently, and how brain and behavioural systems are adapted to enable an animal to cope effectively within its environment. We consider diverse aspects of intelligence including navigation and motor control, tool-use, language, memory and social skills. We ask how these are related to one another and how they are matched to the particular needs of animals and machines.
- Mathematics and Computational Models for Complex Systems (Autumn)
This module provides a foundation in mathematical and scientific computing techniques used in various fields, including artificial intelligence, artificial life, data science, and computational neuroscience. The topics covered also provide the necessary theoretical grounding for a number of modules in Informatics MSc courses, including Adaptive Systems and Machine Learning.
• Vectors and matrices
• Differential calculus
• Numerical integration
• Probability and hypothesis testing
• Dynamical systems theory
- Neuronal Transduction and Transmission (Autumn)
The module follows a logical progression from sensory transduction, the point of entry of information into the brain, to an analysis of neuron-to-neuron communication through both chemical and electrical synapses. Transduction mechanisms in the visual and auditory modalities are the main focus, though other sensory modalities are also discussed. An overview of synaptic physiology is provided as an introduction to a detailed analysis of pre- and post-synaptic cell and molecular mechanisms. Non-synaptic information processing will also be introduced. Finally the module considers whether there are limits to the molecular reductionism approach to the problem of how the brain works.
- Topics in Cognitive Neuroscience (Autumn)
The module introduces students to a wide variety of topics in cognitive neuroscience that are not covered by dedicated modules. Teaching is provided by active researchers and experts in cognitive neuroscience. Students will explore the field through lectures and journal clubs as well as gain opportunities to focus research interests through self-directed presentations and study topics. The aim of the course is to generate the ability to discuss and critique current cognitive neuroscience research through a general well-rounded knowledge of topics, methods and good practice. Topics covered by lectures include (but are subject to change): an introduction to methods, neurophysiology, memory, vision, emotion, embodied cognition, reward and decision-making, animal and genetic models of cognition, dementia, event-related potentials and individual-difference approaches to cognitive neuroscience.
- Science of Memory (Autumn)
Learning from and remembering experiences is critical for survival; failure of the psychobiological mechanisms underlying memory formation and retrieval can have severe and life-changing effects. In this module, students will gain knowledge of the neural basis of learning and memory and will develop an understanding of how learning and memory are impacted by, or are a feature of, various mental health conditions. Lectures may include the following topics: types of learning and memory; memory formation, persistence, and modulation; memory-related disorders and corresponding pre-clinical models.
- Drugs, Brain and Behaviour (Spring)
Drugs, Brain and Behaviour offers students an overview to the psychological, pharmacological, neurobiological and neurophysiological bases of drug use, abuse and contemporary understanding of addiction and (some mental conditions), and has a strong natural science (neuroscience) orientation. The acute and long-term effects of selected drugs of abuse on behaviour, mood, cognition and neuronal function are discussed, using empirical findings and theoretical developments from both human- and non-human subject studies on the neurobiological- and psychological basis of drug action and addiction.
The course will discuss the anatomical, neurochemical and cell-molecular mechanisms targeted by psychoactive drugs, and their distribution, regulation and integration in the broader central nervous system. The focus is on potentially addictive drugs, and the major classes are discussed, including: opiates (heroin, morphine), psychomotor stimulants (amphetamine, cocaine), sedative-hypnotics (alcohol, barbiturates, chloral hydrate), anxiolytics (benzodiazepines), marijuana, hallucinogens (LSD, mescaline), and hallucinogenic-stimulants (MDA, MDMA).
Critically, with the knowledge of the basic neurobiological and behavioural pharmacology of these drugs 'in hand', contemporary theories and understanding of mental conditions, substance abuse and addiction are considered, focusing on key concepts related to (drug) experience-dependent neuroplasticity, drug-induced neurotoxicity, associative learning, neuronal ensembles and the synaptic basis of learning and plasticity, habit formation and impulse-control. This module builds on knowledge gained in the core psychology modules C8003: Psychobiology and C8518: Brain and Behaviour. Students who are not enrolled on the BSc Psychology course at Sussex are expected to be familiar with the material covered in these modules.
- Functional Magnetic Resonance Imaging (Spring)
This module provides an advanced level of theoretical and practical knowledge in the technique of functional magnetic resonance imaging (fMRI). Topics covered include the physical and physiological basis of MRI and fMRI; different study designs in functional imaging research; stages of pre-processing and analysis of data; and interpretation of results. It is expected that students will be able to make a contribution to a real, ongoing fMRI study in terms of observing and/or participating in its execution and contributing to the analysis of the study. Students will gain hands-on experience of Statistical Parametric Mapping (SPM) software for analysing fMRI data that is invaluable for future research in this area.
- Machine Learning (Spring)
This module exposes students to advanced techniques in machine learning. A systematic treatment will be used based on the following three key ingredients: tasks, models and features. Students will be introduced to both regression and classification and concepts such as model performance, learnability and computational complexity will be emphasized. Taught techniques will include: probabilistic and non-probabilistic classification and regression methods and reinforcement learning approaches including the non-linear variants using kernel methods. Techniques for pre-processing of the data (including PCA) will be introduced. Students will be expected to be able to implement, develop and deploy the techniques to real-world problems. Prerequisite: Mathematics and Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience.
- Sensory Function and Computation (Spring)
Comparing the organisation of sensory modalities reveals common conceptual principles underlying how sensory information is processed and transformed, as well as mechanisms characteristic to each modality, which correspond to the distinct ways in which the nervous system extracts signals from different types of physical energy. This module will teach fundamental concepts in sensory coding: feature detection, adaptive representations, coding by spike rates and timing, and population coding. It will incorporate seminars as well as workshops where computer code will be introduced and used to analyse and simulate sensory coding by neurons.