1. Artificial Neural Networks:
Algorithms & Hardware for Implementation
By: Nathan Hower
CSC 3990 – Computing Research Topics

2. What are Neural Networks?
-Biological vs. Artificial
-Computational models
Components of a
Neural Network
- Inputs
- Outputs
- Transition Function (weights)‏

3. What are Artificial Networks Good For Anyway?
- classification: pattern recognition
- function approximation
- clustering: data mining
- association: restoring noisy data

4. Types of Artificial Neural Networks
- Feedforward Recurrent

5. Learning Paradigms and Algorithms
-Supervised learning: sample results and actual results are compared
- Backpropagation: error is corrected backwards
-Unsupervised learning: unlabeled
samples given, data
organization is desired
-Reinforcement learning: maximize
'reward' value

6. Parallel Computing for Processing Artificial Neural Networks
- Multiprocessor computers: fast data communication
- Heterogeneous clusters: indefinitely expandable
- Beowulf clusters: best cost/performance ratio

7. Problematic Areas - Scalability
Algorithms are designed for particular scope and/or limited hardware.
- Computational power/cost
Some problems are so complex that they require expensive specially designed hardware.
- Lack of standardization
The use of artificial neural networks is recent; alternate naming conventions and multiple equally viable approaches occur.

8. Current Areas of Application
- Neurology & Neurobiology
- Economics: stock market prediction
- Image compression
- NP-complete problems
- EBAI – Studying Eclipsing Binaries with Artificial Intelligence

9. Future Work
- Brain-Computer Interface (BCI)‏: neural network learning algorithms moves burden of learning to the computer
- Mind uploading/brain simulation