Short introduction to neural networks
This page supplements my lectures in the Methods in Computational Neuroscience summer
course at Woods Hole.
Please contact me if you find any bugs in the following:
The Hebbian paradigm
- W. James. Psychology: the briefer course (1892).
- D. O. Hebb. The organization of behavior (1949).
- D. J. Amit. The Hebbian paradigm reintegrated: local reverberations as internal
representations. Behav. Brain Sci. 18:617-26 (1995).
Linear network theory
Nonlinear network theory
- Lyapunov theory
- For symmetric networks, a Lyapunov function is known.
- J. J. Hopfield. Neurons with graded response have collective computational
properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA 81:3088-92
- A Lyapunov function is also known for a network consisting of two populations, assuming
symmetric connections within each population, and antisymmetric connections between
- Associative memory models
Network models of persistent neural activity
- delayed-match-to-sample tasks
- M. Griniasty, M. V. Tsodyks, and D. J. Amit. Conversion of temporal correlations between
stimuli to spatial correlations between attractors. Neural Comput. 5, 1-17 (1993).
- D. J. Amit, N. Brunel, and M. V. Tsodyks. Correlations of cortical Hebbian
reverberations: theory versus experiment. J. Neurosci. 14, 6435-45 (1994).
- oculomotor integrator
- head direction system
- K. Zhang. Representation of spatial orientation by the intrinsic dynamics of the
head-direction cell ensemble: a theory. J. Neurosci. 16:2112-26 (1996).
- oculomotor delayed response task
- M. Camperi and X.-J. Wang. A model of visuospatial working memory in prefrontal cortex:
recurrent network and cellular bistability. J. Comput. Neurosci. 5:383-405
- For an alternative to the attractor and amplifier ideas, see: