1. 

Introduction : Neurophysiology and Information 
The contents of this lecture will include intersection of neurophysiology and information. 



Introduction : Neurophysiology and Information  Lecture note 
The contents of this lecture will include intersection of neurophysiology and information. 

2. 

Discussion : “Why we have not made significant progress towards artificial intelligence” 
The contents of this lecture will include development of Artificial Intelligence and students' opinion about that. 



Discussion : “Why we have not made significant progress towards artificial intelligence” 
The contents of this lecture will include development of Artificial Intelligence and students' opinion about that. 

3. 

Information and Probability 1 
The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. 



Information and Probability 1  Lecture note 1 
The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. 



Information and Probability 1  Lecture note 2 
The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. 



Information and Probability 2 
The contents of this lecture will include (1) the definition of probability of frequentist and bayesian and (2) probability distributions related to the Maximum Entropy Principle. 



Information and Probability 3 
The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. 



Information and Probability 3  Lecture note 1 
The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. 



Information and Probability 3  Lecture note 2 
The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. 



Information and Probability 4 
The contents of this lecture will include (1) paper about "Bayesian intergration in sensorimotor learning" and (2) basic chemical concepts related on neurophysiology 

4. 

A Neuron's Membrane Voltage 1 
The contents of this lecture will include (1) the basic concepts about equilibrium potential and (2) action potential. 



A Neuron's Membrane Voltage 1  Lecture note 1 
The contents of this lecture will include (1) the basic concepts about equilibrium potential and (2) action potential. 



A Neuron's Membrane Voltage 2 
The contents of this lecture will include (1) the structures and (2) key properties of voltage gated Ion channels. 

5. 

Synaptic Transmission 
The contents of this lecture will include (1) the types and properties of synapses, (2) synaptic vesicle release, and (3) synaptic plasticity. 



Synaptic Transmission  Lecture note 
The contents of this lecture will include (1) the types and properties of synapses, (2) synaptic vesicle release, and (3) synaptic plasticity. 

6. 

Efficient Coding of Sensory Information 
The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron 



Efficient Coding of Sensory Information  Lecture note 
The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron 

7. 

Efficiency and ambiguity in an adaptive neural code 
The contents of this lecture will include (1) statistical adaptation in neural code and (2) input/output relations for information transmission. 

8. 

General Theory 
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. 



General Theory  Lecture note 
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. 



General Theory 2 
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. 



General Theory 4 
The contents of this lecture will include (1) How a neuron could select sources, (2) predicting future reward, and (3) plasticity rule. 



General Theory 3 
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. 



theory  lecture note 
The contents of this lecture will include (1) physiological principals of the retina. 



Retina 2 
The contents of this lecture will include (1) physiological principals of the retina , (2) predictive coding 

9. 

Retina 1 
The contents of this lecture will include (1) physiological principals of the retina. 

10. 

The Central Visual System 1 
The contents of this lecture will include principles of (1) the Retinofugal Projection and (2) The Lateral Geniculate Nucleus (LGN) 



The Central Visual System 2 
The contents of this lecture will include anatomy and principles of the Striate Cortex 



The Central Visual System ( lecture note) 


11. 

snaptic plasticity11 
The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity 



synaptic plasticity(lecture note) 




snaptic plasticity12 
The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity 

12. 

snaptic plasticity21 
The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) 



snaptic plasticity22 
The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) 

13. 

Reward Signal 1 




Reward Signal 2 
The contents of this lecture will include (1) dopamine's role and efficient representation, and (2) the principles of attention. 



Reward Signal 3 and Discussion 
The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. 



Reward Signal (lecture note) 


14. 

Motor System 
The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. 



Motor System(Lecture note) 

