A classical view of neural computation is that it can be characterized in terms of convergence to fixed-point-type attractor states (representing for instance memory patterns in Hopfield, 1982) or limit-cycle-like sequential transitions among states (mapping e.g., motor or syntactical sequences in Elman, 1990). After over three decades, is this still a valid model of how brain dynamics implements cognition? The idea that neuro-computational dynamics is mainly deterministically driven by convergence to emergent stable states in a synaptic/network noisy background has been lively debated, and recently challenged both empirically and by computational work. This question touches on the very basics of our understanding of neural computation; and hence it is one of the most exciting topics currently in systems and computational neuroscience.
This e-book comprises a comprehensive collection of recent theoretical and experimental contributions addressing the question of stable versus transient neural population dynamics, and its implications for the observed variability in neural activity, from diverse, complementary angles.
Editorial: Metastable Dynamics of Neural Ensembles
Emili Balaguer-Ballester, Ruben Moreno-Bote, Gustavo Deco, and Daniel Durstewitz
Front. Syst. Neurosci., 26 January 2018 | https://doi.org/10.3389/fnsys.2017.00099