Neural Information Processing

This subject introduces students to the basic mechanisms of information processing and learning in the brain and nervous system. The subject builds upon signals and systems modelling approaches to demonstrate the application of mathematical and computation modelling to understanding and simulating neural systems. Aspects of neural modelling that are introduced include: membrane potential, action potentials, neural coding, neural models and neural learning. The application of neural information processing is demonstrated in areas such as: electrophysiology, and neuroprostheses. Material is reinforced through MATLAB and/or NEURON based laboratories.

• 1Explain and Characterise the structure and function of the nervous system, classifying its primary components and their roles.
• 2Compute and Apply equilibrium neural properties to understand fundamental neural dynamics.
• 3Differentiate and Characterise types and properties of synapses, relating them to neural communication.
• 4Examine and Deconstruct membrane mechanisms to explain how action potentials are generated.
• 5Interpret and Analyse neural responses using point processes, such as Poisson models, to characterise neural variability.
• 6Evaluate and Appraise neural processing effectiveness using information theoretic measures to assess information transmission.
• 7Implement, Analyse, and Compare the input-output characteristics of both simplified and biologically-detailed neural models.
• 8Apply and Organise principles underlying the analysis of biological neural signals to extract meaningful information.
• 9Explain and Evaluate mechanisms of learning in the brain and nervous system, critically examining key theories.
• 10Classify and Contrast higher-order neural pathways and systems, evaluating their roles in complex neural processing.