| 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 | |
대구광역시 동구 동내로 64(동내동 1119) 우)41061
COPYRIGHT keris. all rights reserved
페이스북
트위터
카카오톡
네이버밴드
링크복사
