Distributed revision of composite beliefs
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Sigma-Pi learning: on radial basis functions and cortical associative learning
Advances in neural information processing systems 2
Neural Computation
Perception as Bayesian inference
Perception as Bayesian inference
Probabilistic interpretation of population codes
Neural Computation
The effect of correlated variability on the accuracy of a population code
Neural Computation
Learning in graphical models
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Biophysiologically plausible implementations of the maximum operation
Neural Computation
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
Bayesian computation in recurrent neural circuits
Neural Computation
Learning and inference in the brain
Neural Networks - Special issue: Neuroinformatics
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
On the uniqueness of loopy belief propagation fixed points
Neural Computation
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Model for Fast Analog Computation Based on Unreliable Synapses
Neural Computation
Exact Inferences in a Neural Implementation of a Hidden Markov Model
Neural Computation
Neural Computation
Bayesian spiking neurons i: Inference
Neural Computation
Bayesian spiking neurons ii: Learning
Neural Computation
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A computational model of motor areas based on bayesian networks and most probable explanations
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Hi-index | 0.00 |
In this letter, we develop and simulate a large-scale network of spiking neurons that approximates the inference computations performed by graphical models. Unlike previous related schemes, which used sum and product operations in either the log or linear domains, the current model uses an inference scheme based on the sum and maximization operations in the log domain. Simulations show that using these operations, a large-scale circuit, which combines populations of spiking neurons as basic building blocks, is capable of finding close approximations to the full mathematical computations performed by graphical models within a few hundred milliseconds. The circuit is general in the sense that it can be wired for any graph structure, it supports multistate variables, and it uses standard leaky integrate-and-fire neuronal units. Following previous work, which proposed relations between graphical models and the large-scale cortical anatomy, we focus on the cortical microcircuitry and propose how anatomical and physiological aspects of the local circuitry may map onto elements of the graphical model implementation. We discuss in particular the roles of three major types of inhibitory neurons (small fast-spiking basket cells, large layer 2/3 basket cells, and double-bouquet neurons), subpopulations of strongly interconnected neurons with their unique connectivity patterns in different cortical layers, and the possible role of minicolumns in the realization of the population-based maximum operation.