Introduction to Neuroscience

B26

Course Coordinator:

Izumi Fukunaga

Description:

This is a basic course targeted to those without neuroscience background, or those who need to refresh knowledge of key concepts to prepare for more advanced courses in Neuroscience.

This will serve as a pre-requisite for several Neuroscience courses. All neuroscience students need to pass this course before going on to other courses, unless they can demonstrate that they have already mastered the topics by passing the exam.

Assessment will be in the form of an exam at the end. This is not meant to be a stressful experience, but an opportunity for all students to demonstrate the understanding of the materials in their own words. In the exam, each lecturer will submit a short question based on the lecture content and the reading materials indicated in the course description. Each answer should be about 100 words long. Some questions may bridge lecture materials from two or more lectures. Students will be expected to answer all questions. A pass is 50%.

Students with prior knowledge but wishing to attend a part of the course will be allowed to audit.

Aim:

An introduction to neuroscience, from cellular to systems, and brief introduction to several areas of specialization available at OIST. This course is co-taught by neuroscience faculty on a rotating basis.

Course Content:

Week	Topic	Suggested textbook ref	Lecturer	Week starting on	Keywords / concepts to cover
1	Cell biology basics		Ichiro Maruyama	17-Sep	Fundamentals of cell biology in the context of neuroscience Brain ->nervous system -> neurons, cells -> constituents of cells: cell surface including sugars, cell membranes including lipids, organelles, nucleus, proteins, RNA and DNA.
2	Neurobiology concepts (building blocks - neurons, morphology)	Purves pg 1-10, Ch. 4	Gordon Arbuthnott	23-Sep	Neurons are cells too; They just don't divide Cajal and the neuron doctrine Varieties shapes and connection types Modern methods to see them. They need support from glial cells They work together in dynamic teams The layout of the architecture is the next lecture
3	Organisation of the nervous system/neuroanatomy	Purves pg 13-23; other neuroanatomy textbook	Izumi Fukunaga	30-Sep	Peripheral, central, autonomic and enteric nervous systems Forebrain, midbrain, hindbrain Cranial nerves Cortex, subcortical regions, brainstem, Spinal cord Sulci and gyri, layers, Brodmann areas Special organs (eyes, ears etc)
4	Bioelectricity	Purves Ch. 2, 3	Jeff Wickens	07-Oct	Passive electrical properties of neurons Electrical current flow in neurons Electrochemical origin of membrane potential Voltage-dependent Ion channels Ionic basis of action potentials Action potential propagation in axons and dendrites
5	Synapses	Purves Ch. 5,6	Erik De Schutter	14-Oct	electrical versus chemical synapses neurotransmission and the vesicle cycle neurotransmitter receptors: excitation and inhibition synaptic integration
	Study week (SfN annual meeting)
6	Circuits	Purves pg. 11-13	Yoe Uusisaari	28-Oct	inhibitory vs excitatory neurons (GABA / glutamate) interneurons vs projection neurons scales of circuitry: local microcircuit, mesoscale circuit, systemic circuit convergence, divergence feed-forward, feedback, recurrent signalling
	Learning and memory, Mechanisms	Purves Ch 23, 24, 30	Yoko Sugiyama	04-Nov	Synaptic plasticity (for learning), short-term and long-term potentiation and depression Studies of synaptic plasticity using Aplysia, and the hippocampus in mammals
7	Learning and memory, Behavioural aspects		Gail Tripp	11-Nov	Behavioural aspects of learning Pavlovian and instrumental learning Schedules of reinforcement
8	Evolution and Developmental neurobiology	Purves Ch 22, 23, 25	Ichiro Masai	18-Nov	Genetic program for regional patterning in the brain Neurogenesis Neuronal polarity Axon guidance Neuronal degeneration and regeneration
9	Methodology 101	Carter, Shieh	Bernd Kuhn	25-Nov	Electrophysiology Optical imaging Interaction of light and tissue Indicators Microscopes Optical stimulation fMRI
10	Introduction to theoretical and computational neuroscience	Dayan/Abbott	L1: Tomoki Fukai L2: Jun Tani	02-Dec	models of spiking neurons (e.g., conductance-based neuron models to leaky-integrate-fire and Poisson neuron models), the fundamental concepts of neural coding (rate, population, temporal codes) feedforward network models for pattern recognition such as perceptron and error back-propagation (the basis of deep learning) as well as some recurrent network possibly including long-short term memory (LSTM, used in various AI applications)
11	Machine learning basics	Dayan/Abbott	L1: Tomoki Fukai L2: Kenji Doya	09-Dec	attractor network models including Hopfield-type associative memory and working memory circuits fundamentals of reinforcement learning and the classical models of self-organizing cognitive maps through Hebbian learning and lateral inhibition
12	Exam

Course Type:

Elective

Credits:

Assessment:

Final exam, 100% (the exam may be taken at the start of the term to opt out of the course.)

Text Book:

Purves, Augustine, Fitzpatrick, Hall, Lamantia and White: Neuroscience, 5th edition

Carter, Shieh: Guide to Research Techniques in Neuroscience, 2nd edition

Dayan and Abbott: Computational Neuroscience

Reference Book:

Bertil Hille: Ion Channels of Excitable Membranes

Prior Knowledge:

no prerequisites