Bayesian Spiking Neurons I: Inference

By Sophie Denève. Neural Computation, 2008

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrochemistry

View Paper: https://www.semanticscholar.org/paper/49d3c27acfd5a05c88b374ca904bbc7d10f53fcd

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Citations in the corpus

Active Inference: A Process Theory (2017, background): “…comfortably with theoretical proposals and empirical work on the accumulation of evidence (Kira, Yang, & Shadlen, 2015) and the neuronal encoding of probabilities (Deneve, 2008), while rendering the softmax function a (sigmoid) activation function that converts membrane potentials to ﬁring rates.”
Action and behavior: a free-energy formulation (2010, background): “, the Bayesian brain (Helmholtz 1860/1962; Barlow 1969; Ballard et al. 1983; Mumford 1992; Dayan et al. 1995; Rao and Ballard 1998; Lee and Mumford 2003; Knill and Pouget 2004; Kersten et al. 2004; Friston and Stephan 2007; Deneve 2008).”
The graphical brain: Belief propagation and active inference (2017, background): “This formulation also has some construct validity in relation to theoretical proposals and empirical work on evidence accumulation (de Lafuente, Jazayeri, & Shadlen, 2015; Kira, Yang, & Shadlen, 2015) and the neuronal encoding of probabilities (Deneve, 2008).”
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons (2011, methodology): “It uses a population code for encoding the log-posterior estimation of the time varying hidden stimulus, which is modeled as a continuous RV instead of the binary hidden state used in [17].”
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks (2014, background): “Deneve [13, 14] proposed a model for inference and learning based on the dynamics of a single neuron.”
Probabilistic machine learning and artificial intelligence (2015, background): “…foundation for rationality in artificial intelligence systems, but also as a model for normative behaviour in humans and animals 15–18 (but see refs 19, 20 for a discussion), and much research is devoted to understanding how neural circuitry may be implementing Bayesian inference 21,22 .”
The Visual System's Internal Model of the World (2015, background): “A number of researchers have seriously explored the plausible neural implementation of BBP algorithms [42]–[44], [72], [73].”
A Neural Implementation of the Kalman Filter (2009, background): “Several authors have proposed models addressing aspects of this issue [15, 10, 9, 19, 2, 3, 16, 4, 11, 18, 17, 7, 6, 8], but as yet, there is no conclusive experimental evidence in favour of any one and the question remains open.”
Stochastic variational learning in recurrent spiking networks (2014, background): “Various abstract Bayesian models have been proposed to account for this phenomenon (Körding and Wolpert, 2004; Deneve, 2008; Nessler et al., 2009).”
The Human Kernel (2015, background): “In recent years, probabilistic modelling has become a cornerstone of machine learning approaches [2, 3, 4], with applications in neural processing [5, 6, 3, 7] and human learning [8, 9].”
Decision theory, reinforcement learning, and the brain (2008, background): “One question under active debate is the way that uncertain information, such as belief states, might be represented in the activity of populations of neurons, and support the basic computations of Bayesian decision theory such as belief updating (equation 26) and the calculation of expected values (”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “An interesting complementary approach is presented in [90,91], where a single neuron is modeled as hidden Markov model with two possible states.”
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, methodology): “Similar temporal models, sometimes called Bayesian filtering, also underlie the important contributions of [58] and [32].”