B26
Course Coordinator: 
Izumi Fukunaga
Introduction to Neuroscience
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: 
2
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