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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.” |
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The mechanics of the neuron | Basic biophysical structure and function of the neuron | ![]() |
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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 | ![]() |
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Neuronal plasticity | computational and experimental work on synaptic and non-synaptic plasticity | ![]() |
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Sensory Systems | the visual system, efficient coding, Bayesian inference | ![]() |
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Reward Signals: how the system acquires information of relevance to its goals | dopamine, selective attention | ![]() |