SYSTEMS NEUROSCIENCE I - PERCEPTION
Introduction. Naturalism in the philosophy of science/philosophy of mind
Causality in scientific explanation-Mental causation
Introduction to the philosophy of language
Introduction to philosophy of mind - Substance dualism
Behaviourism - Identity theory
Functionalism - The representational theory of Mind
Eliminativism - The problem of Consciousness
Property dualism - Physicalist theories of consciousness
Introduction to the philosophy of science
Introduction to the phenomenological philosophy of mind
Philosophy of perception. Introduction: Direct vs indirect realism. The argument from hallucination
Direct realism: Disjunctivism, enactive perception, representationalism
Indirect realism: Sense data theory, adverbialism. Virtual reality: Philosophical aspects
Introduction to Computational Neuroscience and Matlab Primer 1: Vectors and Matrices, Variance and Covariance, Plotting, Randomness, M-files, and functions
Matlab primer 2: Basic mathematical methods: Ordinary differential equations, PCA, convolution, correlation, built-in libraries and toolboxes
The single neuron model - computational capacity and relative problems, transfer function
Attractor networks and neural oscillators
Supervised learning, gradient descent, local extrema
Other learning methods. Association learning, Hebb's rule, reinforcement learning
Problem formulation, data collection and analysis, phase-plane analysis, and mutual information, signal-to-noise ratio
Abstract neuron models FitzHugh–Nagumo model, IF, Izhikevich
Neural coding. Local/rate coding, population codes, tuning curves, Poisson neurons
Biophysical neuron models. Cable theory, HH equations, compartmental models
The Neuron simulator
Synaptic plasticity, adaptation and learning