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Computational Cognitive Science / Neuroscience

Neural Dynamics: Theory and Modeling (APMA2821V, Fall, Jones):

Our thoughts and actions are mediated by the dynamic activity of the brain’s neurons. This course will use mathematics and computational modeling as a tool to study neural dynamics at the level of signal neurons and in more complicated networks. We will focus on relevance to modern day neuroscience problems with a goal of linking dynamics to function. Topics will include biophysically detailed and reduced representations of neurons, bifurcation and phase plane analysis of neural activity, neural rhythms and coupled oscillator theory. Audience: advanced undergraduate or graduate students. Prerequisite: APMA 0350-0360 and Matlab programming course. Instructor permission required.

Neural Modeling Laboratory (CLPS 1491, Spring, Anderson):

Numerical simulations of cognitively oriented nervous system models. Discussion of parallel, distributed, associative models: construction, simulation, implications, and use. Graduate, Undergraduate

Computing as Done in Brains and Computers (CLPS 0050A, Fall, Anderson):

Brains and computers compute in different ways. We will discuss the software and hardware of brains and computers and with introduction to the way brains are organized, the way computers are organized, and why they are good at such different things. We will talk about our current research, the Ersatz Brain Project, an attempt to design a first-class second-class brain. Undergraduate

Computational Cognitive Neuroscience (CLPS 1492, Fall, Frank):

Begins with basic biological and computational properties of individual neurons and networks of neurons, including attractor dynamics, inhibition, learning mechanisms (spike-timing dependent plasticity, etc). Explores the properties of particular brain networks (visual cortex, hippocampus, parietal cortex, frontal cortex, basal ganglia) and how they interact to solve computational trade-offs across a range of cognitive phenomena iincluding: visual object recognition, attention, various forms of learning (reinforcement learning, unsupervised statistical learning, supervised learning), memory (working memory, episodic memory), elementary aspects of language and cognitive control. Uses graphical neural network software, involves lab component and hands-on training. Graduate, Undergraduate

Computational Cognitive Science (CLPS 1291, Spring, Serre):

Covers pattern recognition and connectionists networks as well as Bayesian probabilistic models, and illustrates how they have been applied in several key areas in cognitive science, including visual perception and attention, object and face recognition, learning and memory as well as decision-making and reasoning. Graduate, Undergraduate

Computational vision (CLPS 1520, Fall, Serre):

A detailed introduction to computational models of biological and machine vision summarizing traditional approaches and providing experience with state-of-the-art methods. Topics include fundamentals of image processing, visual perception (surfaces, color, depth, texture and motion) as well as object recognition and scene understanding. Graduate, Undergraduate

Mechanisms of motivated decision making (CLPS 1470, Spring, Frank):

Considers the factors and mechanisms involved in motivated decision making, as informed by cognitive, neuroscientific, and computational modeling approaches.  Readings span a range of populations (e.g., rodents, monkeys, healthy humans, patients with acquired brain damage), and methods (e.g., behavioral, genetic, pharmacological and neuroimaging studies, electrophysiological recordings). Computational models will be prominently featured as a means for formalizing decision making theories across multiple levels of analysis, some focusing on high-level cognitive computations and others on neural mechanisms. Graduate, Undergraduate

Functional Magnetic Resonance Imaging: Theory and Practice (CLPS 1490, Spring, Badre):

This course will train students in the practice and use of functional magnetic resonance imaging (fMRI) as a cognitive neuroscience methodology. Topics covered include MRI physics, the physiological basis of the BOLD signal, experimental design, data collection, statistical analysis, and inference. A practical component of the course includes the opportunity to collect and analyze fMRI data at the Brown MRF. Graduate, Undergraduate