COSC 460 – Neural Networks

1. COSC 460 – Neural Networks Gregory Caza 17 August 2007

2. Elman (1993) • Elman, J. L. (1993). Learning and development in neural networks: the importance of starting small. Cognition 48: 71-99. • Modelling first language acquisition using a progressive training strategy.

3. Elman (1993) • Simple Recurrent Network (SRN) • context units remember the state of the hidden units at the last time step

4. Elman (1993) • input was a binary-encoded word • words are presented one at a time • output was an encoded prediction of the next word in a sentence • predictions are expected to depend on the network learning a grammatical structure

5. Elman (1993) • developmental constraints may facilitate learning • limited view provides a buffer from a complex, potentially overwhelming domain • simple network = child • complex domain = language

6. Elman (1993) • Training was performed using three different schemata: • using all training data and a fully-developed network • with the training data organized and presented with increasing complexity • beginning with a limited memory that increased throughout training

7. Elman (1993) • developmental simulation #1: incremental input • training sentences were classified as simple or complex • ratio of complex : simple increased over time

8. Elman (1993) • developmental simulation #2: incremental memory • context would be reset when memory limit was reached

9. Elman (1993) • full set: learning did not successfully complete • incremental input: low final error; good generalization • incremental memory: low final error; good generalization

10. Elman (1993) • can training with a subset construct a “foundation for future success”? • filter out “stimuli which may either be irrelevant or require prior learning to be interpreted” • solution space is constrained

11. Elman (1993) • Questions • how many sentences/epochs were used in the failed case? • what were the quantitative differences between the incremental memory/input results? • were results reproducible with different training corpora?

12. Assad et al. (2002) • Assad, C., Harmann, M. J., Paulin, M. G. (2002). Control of a simulated arm using a novel combination of cerebellar learning mechanisms. Neurocomputing 44-46: 275-283. • Control of a robot arm using dynamic state estimation.

13. Assad et al. (2002) • explore the cerebellum’s role in dynamic state estimation during movement • single-link robot arm, capable of single-plane movement and releasing a ball • ANN used to control the release time of the throw, with the goal of hitting a target at a certain height

14. Assad et al. (2002) • 6 Purkinje cells (PC) • 6 climbing fibres (CF) • 6 ascending branches (AB) • 4280 parallel fibres (PF) - 600 inhibitory; 3680 excitatory

15. Assad et al. (2002) • each excitatory PF received a radial basis function (RBF) of 2 state variables • PF-PC connections were strengthened through ‘Hebbian-like’ learning • after each trial, a binary error signal was generated based on throw accuracy • if the ball hit the target window, PF-PC connections were strengthened through ‘Hebbian-like’ learning

16. Assad et al. (2002) • the target window was initialized to be “quite large” • if a hit was recorded, the window was shrunk • if there was an error, the window was expanded

17. Assad et al. (2002) • physiological experiments demonstrate LTD between PF and CF • most cerebellar models ignore the AB input • the network suggests a possible role for LTP in cerebellar learning through the AB

18. Assad et al. (2002) • details, details! • too complicated => laying groundwork for experiments • Why does no learning take place when the target is missed? What about negative reinforcement?