1. Vectors, Matrices, and Basic Neural
Computations
The brain is the most complex organ known to exist,
yet simple mathematical and computer programming methods
can be used to simulate many neural systems.
- Neural Systems, Neural Networks, and Brain
Function
- Using MATLAB: The Matrix Laboratory Programming
Environment
- Imitating the Solution by Gauss to the Busywork
Problem
- Operation and Habituation of the Gill Withdrawal
Reflex of Aplysia
- The Dynamics of a Single Neural Unit with
Positive Feedback
- Neural Networks: Neural Systems with Multiple
Interconnected Units
2. Recurrent Connections and Simple Neural
Circuits
Small networks with recurrent connections, forming
circuits, can shape signals in time, produce
oscillations, and simulate neural systems involved in
low-level motor control.
- The Dynamics of Two Neural Units with Feedback
in Series
- Signal Processing in the Vestibulo-Ocular Reflex
(VOR)
- The Parallel Pathway Model of Velocity Storage
in the Primate VOR
- The Positive Feedback Model of Velocity Storage
in the Primate VOR
- The Negative Feedback Model of Velocity Leakage
in the Pigeon VOR
- Oculomotor Neural Integration via Reciprocal
Inhibition
- Simulating the Insect Flight Central Pattern
Generator
3. Forward and Recurrent Lateral Inhibition
Networks with forward and recurrent laterally
inhibitory connectivity profiles can shape signals in
space and time and simulate certain forms of sensory and
motor processing.
- Simulating Edge Detection in the Early Visual
System of Limulus
- Simulating Center/Surround Receptive Fields
Using the Difference of Gaussians
- Simulating Activity Bubbles and Stable Pattern
Formation
- Separating Signals from Noise and Modeling
Target Selection in the Superior Colliculus
4. Covariation Learning and Auto-Associative
Memory
Networks with recurrent connection weights that
reflect the covariation between pattern elements can
dynamically recall those patterns and simulate certain
forms of memory.
- The Four Hebbian Learning Rules for Neural
Networks
- Simulating Memory Recall Using Recurrent Auto-Associator
Networks
- Recalling Distinct Memories Using Negative
Connections in Auto-Associators
- Synchronous versus Asynchronous Updating in
Recurrent Auto-Associators
- Graceful Degradation and Simulated Forgetting
- Simulating Storage and Recall of a Sequence of
Patterns
- Hebbian Learning, Recurrent Auto-Association,
and Models of Hippocampus
5. Unsupervised Learning and Distributed
Representations
Unsupervised learning algorithms, given only a set
of input patterns, can train neural networks to form
distributed representations of those patterns that
resemble brain maps.
- Learning through Competition to Specialize for
Specific Inputs
- Training Few Output Neurons to Represent Many
Input Patterns
- Simulating the Formation of Brain Maps using
Cooperative Mechanisms
- Modeling the Formation of Tonotopic Maps in the
Auditory System
- Simulating the Development of Orientation
Selectivity in Visual Cortex
- Modeling a Possible Multisensory Map in the
Superior Colliculus
6. Supervised Learning and Non-Uniform
Representations
Supervised learning algorithms can train neural
networks to associate patterns and simulate the
non-uniform distributed representations found in many
brain regions.
- Using the Classic Hebb Rule to Learn a Simple
Labeled Line Response
- Learning a Simple Contingency Using the
Covariation Rule
- Using the Delta rule to Learn a Complex
Contingency
- Learning Interneuronal Representations using
Back-Propagation
- Simulating Catastrophic Retroactive Interference
in Learning
- Simulating the Development of Non-Uniform
Distributed Representations
- Modeling Non-Uniform Distributed Representations
in the Vestibular Nuclei
7. Reinforcement Learning and Associative
Conditioning
Reinforcement learning algorithms can simulate
certain forms of associative conditioning and can train
networks to develop non-uniform distributed
representations.
- Learning the Labeled-Line Task via Perturbation
of One Weight at a Time
- Perturbing All Weights Simultaneously and the
Importance of Structure
- Plausible Weight Modification using Perturbative
Reinforcement Learning
- Reinforcement Learning and Non-Uniform
Distributed Representations
- Reinforcement in a Schema Model of Avoidance
Conditioning
- Exploration and Exploitation in a Model of
Avoidance Conditioning
8. Information Transmission and Unsupervised
Learning
Unsupervised learning algorithms can train neural
networks to increase the amount of information they
contain about the input and simulate the properties of
sensory neurons.
- Some Basic Concepts in Information Theory
- Measuring Information Transmission through a
Neural Network
- Maximizing Information Transmission in a Neural
Network
- Information Transmission and Competitive
Learning in Neural Networks
- Information Transmission in Self-Organized Map
Networks
- Information Transmission in Stochastic Neural
Networks
9. Probability Estimation and Supervised
Learning
Supervised learning algorithms can train neural
units and networks to estimate probabilities and
simulate the responses of neurons to multisensory
stimulation.
- Implementing a Simple Classifier as a
Three-Layered Neural Network
- Predicting Rain as an Everyday Example of
Probabilistic Inference
- Implementing a Simple Classifier Using Bayes’
Rule
- Modeling Neural Responses to Sensory Input as
Probabilistic Inference
- Modeling Multisensory Collicular Neurons as
Probability Estimators
10. Time-Series Learning and Nonlinear Signal
Processing
Supervised learning through time can train neural
networks to produce dynamic transformations and simulate
certain forms of motor control and short-term memory.
- Training Connection Weights in Nonlinear
Recurrent Neural Networks
- Training a Two-unit Network to Simulate the
Oculomotor Neural Integrator
- Velocity Storage in the Primate Vestibulo-Ocular
Reflex
- Training a Network of Linear Units to produce
Velocity Storage
- Training Networks of Nonlinear Units to Produce
Velocity Storage
- Training a Recurrent Neural Network to Simulate
Short-Term Memory
11. Temporal-Difference Learning and Reward
Prediction
Temporal-difference learning can train neural
networks to estimate the future value of a current state
and simulate the responses of neurons involved in reward
processing.
- Learning State Values using Iterative Dynamic
Programming
- Learning State Values using Least Mean Squares
- Learning State Values using the Method of
Temporal Differences
- Simulating Dopamine Neuron Responses using
Temporal Difference Learning
12. Predictor-Corrector Models and
Probabilistic Inference
Predictor-corrector models can improve perception by
combining internal expectations with sensory
observations and simulate the responses of certain
sensory neurons.
- Modeling Visual System Direction Selectivity
using Asymmetric Inhibition
- Modeling Visual Processing as Bottom-Up/Top-Down
Probabilistic Inference
- A Predictor-Corrector Model of Predictive
Tracking by Midbrain Neurons
- Training a Sigmoidal Unit to Simulate Trajectory
Prediction by Neurons
13. The Genetic Algorithm and Simulated
Evolution
The genetic algorithm simulates the process of
evolution and can be used to optimize the structure,
connectivity, and adaptability of neural systems.
- Simulating Genes and Genetic Operators
- Exploring a Simple Example of Simulated Genetic
Evolution
- Evolving the Sizes of Neural Networks to Improve
Learning
- Evolving Optimal Learning Rules for
Auto-Associative Memories
- Evolving Connectivity Profiles for
Activity-Bubble Neural Networks
14. Future Directions in Neural Systems
Modeling
In the future, neural systems models will become
increasingly complex and will span levels from molecular
interactions within units to interactions between
networks.
- Neuro-Informatics and Molecular Networks
- Enhanced Learning in Neural Networks with Smart
Synapses
- Combining Complementary Network Paradigms for
Memory Formation
- Smart Synapses and Complementary Rules in
Cerebellar Learning
- A Final Word
