1
Introduction to the book
2
Introduction to computational neuroscience
2.1
Marr’s levels
2.2
Why computers?
2.3
What is the real value gained?
2.4
Why Python
2.5
Getting started with Python
2.6
Learning Python
3
Introduction to calculus
3.1
Derivatives
3.1.1
Practive with derivatives
3.2
Integrals
3.2.1
Practice with integrals:
4
Intro to dynamical systems
4.1
Differential equations
4.2
Euler’s method
5
Simple neuron model
5.1
Leaky integrate and fire
5.2
Quadratic integrate and fire
5.3
Izhikevich Neuron
6
Hodgkin-Huxley neuron model
6.1
neuron_func_1
6.2
neuron_func_2
6.3
neuron_func_3
6.4
neuron_func_4
6.5
neuron_func_5
6.6
neuron_func_6
6.7
neuron_func_7
6.8
Review of HH
7
Neurotransmitter release
7.1
Synaptics responses
7.2
Spikes as a change in conductance
7.3
Coupling conductance to PSP
7.4
PSPs superimpose
7.5
PSP model in the Izhikevich neuron
7.6
Chaining neurons together
8
Synpatic Plasticity
8.1
Python helper functions
8.2
Hebbian Learning 1
8.3
Hebbian Learning 2
8.4
Hebbian Learning 3
8.5
Hebbian STDP
9
Reinforcement Learning
10
Reinforcement learning rule
11
Example 1
12
Example 2
13
Example 3
13.1
Reinforcement learning framework
13.2
Estimating the state-value function
13.3
Temporal difference (TD) learning
13.3.1
TD RL model of classical conditioning
13.3.2
TD RL as the learning signal in a spiking network
13.4
TD(0) for estimating
\(v_{\pi}\)
13.4.1
TD in a simple 2-arm bandit task
13.4.2
Action selection policy
13.5
SARSA for estimating
\(Q\)
13.6
Q-learning for estimating
\(\pi\)
13.6.1
Q-learning applied to instrumental conditioning
13.6.2
Q-learning applied to instrumental conditioning 2
13.7
Dyna-Q: Model-based RL
13.7.1
Dyna-Q applied to instrumental conditioning
14
supervised learning
15
Supervised Learning
Introduction to Computational Neuroscience
9
Reinforcement Learning