Neuroscience#
Notes on how the brain works, mostly from Purves’s “Neuroscience” textbook and a few courses. The first chapters go from the parts of the brain and the electrical and chemical signaling of single neurons, through the senses and the motor system, to how circuits develop and how memory is stored. Vision gets its own long chapter, from the retina to cortex and the methods used to record it.
The last two chapters turn to models and data. Computational neuroscience covers models of neurons, neural codes and learning rules, then encoding models, datasets and neuro-inspired AI; the disease chapter covers Alzheimer’s and Parkinson’s and how pathology slides are graded and analyzed with deep learning. Start with brain basics: the later chapters assume its vocabulary.
The parts of the brain, then how a neuron makes a voltage, fires, and talks to the next neuron through synapses and transmitters.
How touch, pain, sight, hearing, balance and taste get from receptors through the thalamus to cortex.
The visual system from photoreceptors and retinal circuits to V1 and beyond, with the methods used to record it and the diseases that damage it.
From spinal reflexes up through motor cortex, basal ganglia and cerebellum, plus eye movements and the autonomic system.
A history of memory research, from Cajal’s neurons to the molecules that turn a short-term memory into a long-term one.
How the nervous system builds itself, from the neural tube and morphogen gradients to axon guidance, competition, critical periods and repair.
Alzheimer’s and Parkinson’s, then how pathologists grade cancer from tissue slides and where deep learning fits in.
Models of neurons, neural codes and learning rules, then data-driven neuroscience, brain encoding models and neuro-inspired AI.