-
- 주제분류
- 자연과학 >생물ㆍ화학ㆍ환경 >생명과학
-
- 등록일자
- 2009.08.27
-
- 조회수
- 3,937
-
신경 시스템의 생물학적 기본 기능은 잘 알려져 있으나, 계산적 측면에서는 알려진 부분이 많지 않으며, 일반적인 이론이 정립되어 있지 않다. 본 수업은 “Towards a general theory of neural computation based on prediction by single neurons” (Fiorillo, 2009)에 발표된 계산적 이론을 중심으로하여, 신경과학 분야의 분자, 세포, 시스템 측면에서의 분석을 제시한다.
차시별 강의
| 1. | ![]() |
Theory Part 1: the computational goal of the nervous system: probability theory and information | The course will focus on topics related to my published paper (Fiorillo, 2008): “Towards a general theory of neural computation based on prediction by single neurons.” |
![]() |
| 2. | ![]() |
The mechanics of the neuron | Basic biophysical structure and function of the neuron | ![]() |
| 3. | ![]() |
Theory Part 2: how a neuron can learn | Theory Part 2: how a neuron can learn to predict: synaptic plasticity, plasticity of non-synaptic ion channels, reinforcement learning, organization of the system | ![]() |
| 4. | ![]() |
Neuronal plasticity | computational and experimental work on synaptic and non-synaptic plasticity | ![]() |
| 5. | ![]() |
Sensory Systems | the visual system, efficient coding, Bayesian inference | ![]() |
| 6. | ![]() |
Reward Signals: how the system acquires information of relevance to its goals | dopamine, selective attention | ![]() |
연관 자료








