NEUROSCIENCE: FROM MOLECULES TO MIND - 2025/6

Module Overview

The Neuroscience module is following the Neuroscience FHEQ Level 5 which provided the bases in Neuroscience. This second module will run over one semester and will provide students with a comprehensive research-led overview of several current hot topics in Neuroscience built around 4 intricate topics: neuroendocrinology, sleep, neuroplasticity and brain disorders. Emphasis will be placed on integration of knowledge from the different areas presented in the lectures. In addition, evaluation of skills for understanding, synthesis/analysis and interpretation of scientific data will be addressed throughout all research-led lectures and assessments.

Module provider

School of Biosciences

Module Leader

SEIBT Julie (Biosciences)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

Year 2024/25: BMS2048 Neuroscience: From Neurones to Behaviour Or BMS2046 Pathology and Medicine Or BMS2047 Pharmacology: Introduction to Drug Action Or BMS3055 Advanced Pharmacology Selected Topics in Drug Action Year 2025/26: BMS2048 Neuroscience: From Neurones to Behaviour

Module content

Indicative content includes:

• Lectures will be interactive and various digital tools (e.g., Kahoot!, Poll everywhere, Surrey Learn) will be used to support student active engagement (e.g., self-testing, further prompting discussion)


• Topics include:
  
  
  
  • Introduction to neuroendocrinology (e.g., control of appetite, puberty, stress) and complex brain functions (sleep, brain plasticity), which complement, build upon and link lectures from L5 Neuroscience in L5 (BMS2048), L5 endocrinology (BMS2038) and L6 Biological Rhythms (BMS3066)
  
  
  • A more in-depth overview of brain disorders across the lifespan (neurodevelopment and neurodegenerative diseases) that were briefly introduced in L5 neuroscience.
  
  
  
  


• A coursework that has been specifically designed to develop analysis of neuroscientific data and synthesis skills in a setting that encourages interactions, discussions, and use of feedback during the entire semester.

Assessment pattern

Alternative Assessment

None

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to acquire key skills essential for research-led position (employability), such as critical thinking and synthesis of large body of information:

• Critically evaluate, analyse and interpret the intellectual and technical aspects of neuroscientific data


• Use efficiently electronic tools


• Use, reflect and integrate knowledge from the lecture content.


• Apply knowledge on methods and concepts in a new context


• Write a structured essay with a fixed number of words limit

1. One coursework which aim is to increase understanding and interpretation of neuroscientific data (50%)


2. One online exam consisting in short answers to questions that integrate concepts and knowledge across several lectures of the module (50%)

• during the tutorial sessions


• via SurreyLearn (discussion boards, updates, emails)


• by providing personalised feedback on coursework (strengths and improvement areas)


• during 1:1 meeting upon request

Module aims

• To expand the understanding of complex brain functions with emphasis on areas of expertise within active research programmes in the FHMS
• To expand the understanding of normal and abnormal functioning of the central nervous system and the underlying molecular bases
• Enable students to integrate information from multiple topics to address and interpret neuroscientific data
• To develop knowledge in analytic methods in neuroscience, with a potential for transferability to other biological disciplines

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Provide the students with knowledge via the set of lectures, whilst promoting the development of personal skills relating to the understanding and analysis of data and the interpretation of results, as well as integration of knowledge via the tutorial sessions. Tutorial sessions provide the opportunity for students to define their difficulties or points where they would value feedback which is then timely delivered by the lecturer.

The learning and teaching methods include:

• 
  A balanced blend of Lectures and Tutorials to allows students to revisit and consolidate the knowledge acquired on a weekly basis.
  


• 
  Formative assessments and class discussions during tutorials, which include quizzes, open discussion of lecture content, and the detailed discussion of assessments
  


• 
  Some captured content and online tests will provide additional material and information related to lectures.
  

• 
  Independent learning to revise lecture content, write coursework and prepare for examinations
  

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: BMS3064

Other information

Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas:

Resourcefulness & resilience: the coursework for this module is heavily based on the analytic methods used on biological science and their application to neuroscientific data. The student will thus apply and enhance previously acquired skills in data analysis, statistic and presentation and combine it with critical thinking. During the module, the student will have ample opportunities to reflect on their progress using SurreyLearn discussion board, weekly tutorials, and personalised feedback.

Digital capabilities: This module will use online polls to assess knowledge and understanding of key concepts e.g., Kahoot! PollEv, SurreyLearn. The students are expected to engage with online material and resources via SurreyLearn, which will be discuss weekly during  tutorials. Students will develop the capacity to use analysis software.enviuronement such as Excel, GraphPad or online analysis platforms. Introduction and discussion about online resources will also take place to familiarise students to available research onnline tools and database.

Employability: The module equips students with knowledge and skills required in research in general (e.g., data analysis, critical thinking, presentation of scientific results) and neuroscience in particular (e.g., methods, brain function in health and disease). Real life examples are presented whenever appropriate to allow transfer of skills. The acquisition of these research and scientific tools span over the entire semester and can be tailored to individual difficulties as many opportunities are given to students to ask questions to the lecturer team on specifics using the SurreyLearn platform, during tutorials and personalised feedback sessions.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.