NEURBIO 220: Neural Coding, Computation, and Dynamics is taught in Fall Quarter by Professor Bruce McNaughton.


Dr. Bruce McNaughton

Prerequisite: At least one upper-division course in the field of Neuroscience or one upper-division course in Cognitive Science or Machine Learning.

Concurrent with BIO SCI N174.

Course Objective:

The course covers the fundamental theoretical principles and biological mechanisms underlying how brains acquire, assimilate, store and retrieve information, compute adaptive responses to external inputs, and how knowledge is extracted from experience to generate an internal model of the world leading to successful prediction of the outcome of events and actions: how brains become intelligent.

Lecture Topics

# Topic
1 Neural computation and computational neuroscience: two sides of a coin. Levels of analysis and levels of approximation. Fundamental concepts of neural signaling.
2 Principles of associative memory in neuronal networks I; the learning matrix, associative retrieval (pattern completion), constraints on capacity.
3 Principles of associative memory in neuronal networks II; Hebb-Marr networks, the role of inhibition, pattern separation, sequence learning and the synaptic matrix symmetry problem. Attractor dynamics and the continuous attractor concept.
4 Acquiring and making sense of neural data: basic principles of electrophysiological recording and optical imaging of neural activity and standard analytical approaches. Transmembrane vs extracellular current flow patterns and resulting voltage (potential) changes. The interpretation of LFP (EEG) signals and neuronal spike time-series.
5 Principles of neural coding: rate, population, and spike timing codes; coincidence detection, sequence detection. Bayesian decoding of neural signals.
6 Teaching brains to compute: representing the world with neurons; the classic problem of neural network theory and its solution; `hidden layers` and conjunctive coding.
7 Neural feature detectors and how they are created (BCM rule, competitive learning).
8 Maps in the brain; Modular information processing: the cortical column; Hierarchical Information Processing.
9 Passive and ‘active’ properties of membrane cylinders and trees (i.e., axons and dendrites).
10 Neurotransmitter release and synaptic potentials.
11 Synaptic morphology and configurations; networks.
12 Synaptic plasticity I: mechanisms of association, Hebb`s hypothesis, LTP /LTD, spike-timing dependent plasticity.
13 Synaptic plasticity II:
14 The hippocampus I: anatomy and the neural basis of the cognitive map.
15 The hippocampus II: the indexing theory and memory consolidation. Sleep and memory consolidation.
16 Oscillations in the brain; cortical desynchronization, slow waves, up-down states, hippocampal theta rhythm sharp-waves & ripples, and spike timing relationships.

Download Sample Syllabus