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Introduction : Neurophysiology and Information |
The contents of this lecture will include intersection of neurophysiology and information. |
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Introduction : Neurophysiology and Information - Lecture note |
The contents of this lecture will include intersection of neurophysiology and information. |
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2. |
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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. |
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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. |
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3. |
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Information and Probability 1 |
The contents of this lecture will include (1) what the computational goal, (2) the definitions of information and probability. |
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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. |
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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. |
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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. |
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Information and Probability 3 |
The contents of this lecture will include (1) the definition of Bayesian probability and theorem (2) a neuron's infromaition. |
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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. |
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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. |
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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 |
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4. |
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A Neuron's Membrane Voltage 1 |
The contents of this lecture will include (1) the basic concepts about equilibrium potential and (2) action potential. |
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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. |
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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. |
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5. |
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Synaptic Transmission |
The contents of this lecture will include (1) the types and properties of synapses, (2) synaptic vesicle release, and (3) synaptic plasticity. |
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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. |
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6. |
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Efficient Coding of Sensory Information |
The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron |
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Efficient Coding of Sensory Information - Lecture note |
The contents of this lecture will include (1) Information Theory and (2) Efficient coding in neuron |
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7. |
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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. |
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8. |
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General Theory |
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. |
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General Theory - Lecture note |
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. |
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General Theory 2 |
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. |
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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. |
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General Theory 3 |
The contents of this lecture will include a general theory of neural computation based on prediction by single neurons. |
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theory - lecture note |
The contents of this lecture will include (1) physiological principals of the retina. |
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9. |
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Retina 1 |
The contents of this lecture will include (1) physiological principals of the retina. |
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Retina 2 |
The contents of this lecture will include (1) physiological principals of the retina , (2) predictive coding |
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10. |
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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) |
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The Central Visual System 2 |
The contents of this lecture will include anatomy and principles of the Striate Cortex |
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The Central Visual System ( lecture note) |
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11. |
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snaptic plasticity1-1 |
The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity |
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synaptic plasticity(lecture note) |
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snaptic plasticity1-2 |
The contents of this lecture will include (1) Concepts of Neural connectivity and (2) Synaptic rearrangement depend on activity |
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12. |
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snaptic plasticity2-1 |
The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) |
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snaptic plasticity2-2 |
The contents of this lecture will include (1) mechanisms of synaptic plasticity and (2) Spiking Timing Dependent Plasticity(STDP) |
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13. |
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Reward Signal 1 |
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Reward Signal 2 |
The contents of this lecture will include (1) dopamine's role and efficient representation, and (2) the principles of attention. |
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Reward Signal 3 and Discussion |
The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. |
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Reward Signal (lecture note) |
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14. |
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Motor System |
The contents of this lecture will include (1) the principles of attention, and (2) STDP with Dopamine signaling. |
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Motor System(Lecture note) |
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