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NEU/PSY 502

502A

Syllabus
Lectures
Paper Presentation Rubric
Class 1: Introduction and History
Class 2: Perception and Constraint Satisfaction
Class 3: Associative Learning and Topography
Class 4: Sequences and Predictions
Class 5: Dynamics of Integration and Decision Making
Class 6: Cerebellum and Error-driven Learning
Class 7: Reinforcement Learning
Class 8: Dopamine and Basal Ganglia
Class 9: Basics of Motor System + Motor Cortex
Class 10: Explore/exploit and noradrenergic neuromodulation
Class 11: Distributed Representation
Class 12: Episodic Memory and Hippocampal Function
Class 13: Bayesian Inference and Neural Networks (Griffiths)
Class 14: Episodic Memory and Sleep
Class 15: Attention, Control and Prefrontal Function
Class 16: Working memory
Class 17: Representation and Capacity Limits
Class 18: Optimization and Control
Class 19: Dynamics and Geometry of Control
Class 20: Oscillations and Coherence
Class 21: ISC & BCI
Class 22: Social Cognition
Class 23: Cognitive Neuropsychology and the Origins of Computational Psychiatry
Class 24: Development

502B

Syllabus

502B Computation

Introduction
1 Primer
2 Dynamics in Perception
3 Decision Making
- 3.1 Drift Diffusion Models
4 Reinforcement Learning
- 4.1 Reinforcement Learning
5 Statistical Learning and Backpropagation
- 5.1 Rumelhart Semantic Network
6 Episodic Memory
- 6.1 Episodic Memory
- 6.2 Episodic Generalization Optimization - EGO
7 Selective Attention, Automaticity, and Control
- 7.1 Stroop Model
8 Conflict Monitoring, Effort, and Control
- 8.1 Conflict Monitoring
9 Integration, Attention, Context, and Control
- 9.1 ISC-CI Model

502B Empirical

Introduction

Repository
Open issue

.md

Class 12: Episodic Memory and Hippocampal Function

Contents

Lectures
Readings

Class 12: Episodic Memory and Hippocampal Function#

Lectures#

Readings#

Episodic memory: Clewett et al., (2019)
Episodic memory: Tulving et al., (2002)
Hippocampal representations of space: Moser et al., (2014)
Complementary learning systems: Mcclelland et al., (1995)
Complementary learning systems (extended): Oreilly et al., (2002)
CLS theory updated: Kumaran et al., (2016)

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Class 11: Distributed Representation

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Class 13: Bayesian Inference and Neural Networks (Griffiths)

Contents

Lectures
Readings

By Jonathan Cohen, Samuel Nastase, Alexander Ku & Younes Strittmatter

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