The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function
By E. T. Rolls, Gustavo Deco. 2010
469 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics
View Paper: https://www.semanticscholar.org/paper/8e11ce4a65e66879add4ac068dece6884b7b046c
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In Atomic and Molecular Physics, and Optics
Citations in the corpus
- The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs (2014, unknown): “…a structured quality to brain activity (Rumsey and Abbott, 2004), constraining the naturally stochastic firing of individual pyramidal neurons (Rolls and Deco, 2010) and so providing ideal conditions for the emergence of “complexity” (Tononi et al., 1994) or “self-organized criticality”…”
- Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, unknown): “Attempts to understand the organization of computations in the brain from the perspective of traditional, mostly deterministic, models of computation, such as attractor neural networks or Turing machines, have run into problems: Experimental data suggests that neurons, synapses, and neural systems a”
- Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering (2014, unknown): “In some of our experiments, when specified, we used only 2000 ImageNet features, restricted to the whole and center windows.”
- Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Once initiated by random molecular fluctuations (94, 287, 561, 567), the noise may result at some point in organized activity (168, 200, 297, 330, 465, 564, 611) and influence or settle in attractor states (63, 125, 510) that result in mental events.”
- The cingulate cortex and limbic systems for emotion, action, and memory (2019, background): “The decision variables are applied simultaneously, and the network, after previous training with these decision variables, reaches a state where the population of neurons representing one of the decision variables has a high firing rate (Rolls and Deco 2010; Deco et al. 2013; Rolls 2016a).”
- Pattern separation, completion, and categorisation in the hippocampus and neocortex. (2016, unknown): “…frequently approximates a Poisson process, in which the spikes for a given mean firing rate occur at times that are essentially random (apart from a small effect of the refractory period), with a coefficient of variation of the interspike interval distribution (CV) near 1.0 (Rolls & Deco, 2010).”