Modeling brain function: the world of attractor neural networks, 1st Edition

By A. eBook. 1989

Subfields: Model Architectures, Neuroscience

Citations in the corpus

• Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model (2019, neutral): “This includes the attractor dynamics popular in computational neuroscience [23, 3, 40].”
• Memory formation in matter (2018, unknown): “However, this performance of the abstract model comes from a biologically and physically unsound assumption — that the values of Jij may grow unboundedly as new memories are stored (Amit, 1989; Amit and Fusi, 1994; Fusi and Abbott, 2007).”
• Evolution of networks (2001, unknown): “Structural characteristics of evolving networks 2 III A. Degree 3 III B. Shortest path 3 III C. Clustering coefficient 3 III D. Size of the giant component 4 III E.”
• Learning Attractor Dynamics for Generative Memory (2018, unknown): “A theoretically well-founded approach for robust memory retrieval is to employ attractor dynamics [3, 15, 18, 33].”
• Colloquium: Criticality and dynamical scaling in living systems (2017, unknown): “It was long-ago conjectured that the extraordinary “computational power” of living systems could be the result of collective behavior, emerging out of a large number of simple components (Amari, 1972; Carpenter and Grossberg, 2016; Grossberg, 1982; Hopfield, 1982).”
• A palimpsest memory based on an incremental Bayesian learning rule (2000, unknown): “Auto-associative attractor ANN:s have been proposed as models for biological associative memory [16,8,1,4,14].”
• Weak pairwise correlations imply strongly correlated network states in a neural population (2005, unknown): “In the Ising model, it is known that if all pairs of spins (here, cells) interact significantly, then to recover the thermodynamic limit the typical size of the interactions Jij must decrease with N (30; 36).”