Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

By Peter S. Dayan, L. F. Abbott. 2001

3,890 citations recorded by the source corpus.

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Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “a point process on X which has numerous applications in Bayesian nonparametric inference and neural coding (Dayan and Abbott 2005).”
A Constructive Mean-Field Analysis of Multi-Population Neural Networks with Random Synaptic Weights and Stochastic Inputs (2008, neutral): “t belongs to. We consider that each neuron i is described by its membrane potential Vi(t), and the related instantaneous ﬁring rate is deduced from it through a relation of the form νi(t) = Si(Vi(t)) [18, 22], where Si is a sigmoidal function. A single action potential from neuron j generates a post”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “It has been shown that also this problem can be solved by a variant of Expectation Maximization [84], so there is some similarity to the identification of hidden causes in our model.”
Training a Feedback Loop for Hand Pose Estimation (2015, background): “It has been shown that feedback paths in the brain and especially in the visual cortex actually consist of more neurons than the forward path [7].”
SpiNNaker: A 1-W 18-Core System-on-Chip for Massively-Parallel Neural Network Simulation (2013, background): “For a neural simulation of the scale of the human brain, composed of approximately 10 neurons each with about 10 synapses firing at the rate of about 10 Hz [8], the processing power needed is approximately 10 operations per second; only an Exascale supercomputer [9], yet to be built, would have the ”
Networks beyond pairwise interactions: structure and dynamics (2020, methodology): “[415] Peter Dayan, Laurence F Abbott, and L Abbott.”
A framework for studying the neurobiology of value-based decision making (2008, background): “Second, subject to some technical qualifications, habit systems learn to assign a value to actions that is commensurate with the expected reward that these actions generate, as long as sufficient practice is provided and the environment is sufficiently stabl”
The precision of visual working memory is set by allocation of a shared resource (2009, background): “…of population coding : because there is substantial noise in the activity of any individual neuron , the precision of the population estimate of a sensory feature is determined by the number of neurons involved in encoding it ( Dayan & Abbott , 2001 ; Seung & Sompolinsky , 1993 ; Vogels , 1990 ) .”
Structure Learning in a Sensorimotor Association Task (2010, background): “Later, Rescorla and Wagner formalized such associative learning in a very simple and powerful learning rule [5,6] that explains a vast array of experimental effects.”
Dyscalculia: From Brain to Education (2011, background): “This is analogous to the “actor-critic”model of unsupervised reinforcement learning in neuroscience, which proposes a critic element internal to the learning mechanism—not a guide that is external to it—which evaluates the informational feedback in order to construct the next action (70).”
Symbols and quantities in parietal cortex: elements of a mathematical theory of number representation and manipulation (2007, background): “…which value is represented by this population is to compute their population vector, which is the mean of the preferred values of each neuron, weighted by their current firing rate [mathematically, this is the best estimator in the maximum likelihood sense (Dayan and Abbott, 2001, pp. 106–108)].”
Fractional differentiation by neocortical pyramidal neurons (2008, methodology): “Methods The following equations comprise the standard HH model (Hodgkin and Huxley, 1952; Koch, 1999; Dayan and Abbott, 2001; Gerstner and Kistler, 2002):”