Required in stage
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First term
Second term
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Next year
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Prerequisites
Taught by
Language of instruction
Duration
LevelNeural Computing (B-KUL-H02B3A)
Aims
This introductory course starts with the structure of the nervous
system, how it operates and how it develops. The basic concepts at the cellular and the systems level, in particular the visual and the visuomotor systems, are described. Also, a number of techniques are reviewed for recording from the brain, both invasively and non-invasively. Several computational models of the nervous system are discussed with focus on vision, sensory-motor integration, learning, and memory.
Previous knowledge
No specific requirements.
Content
Part I: The nervous system
neuron, action potential, synapse, channel regulation and memory, visual
system, neural maps and the development of the visual system, reaching
and grasping
Part II: Recording from the brain
invasive and non-invasive recording
Part III: Computational neuroscience
principles, Reichardt detector, models of complex motion and actions,
convolutional and self-organizing neural networks
Course material
Syllabus
Slides, transparencies, courseware
Examples and samples
Activities
0.5 ects. Neural Computing: Laboratory Sessions (B-KUL-H00G2a)
Aims
Hands-on experience is provided through two case studies:
- feedforward networks and supervised learning
- topographic maps and self-organization.
3.5 ects. Neural Computing: Lecture (B-KUL-H02B3a)
Content
See
H02B3A
Aims
See
H02B3A
Description of learning activities
Lectures
Course material
course slides, course texts
course overview
example questions for the exam
Evaluation
Evaluation : Neural Computing (B-KUL-H22B3a)
Explanation
written exam with oral defense
