Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
1994 Special Issue: Networks of anatomical areas controlling visuospatial attention
Neural Networks - Special issue: models of neurodynamics and behavior
Recurrent sampling models for the Helmholtz machine
Neural Computation
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Neuromodulation, theta rhythm and rat spatial navigation
Neural Networks - Computational models of neuromodulation
Detection of Weak Signals by Emotion-Derived Stochastic Resonance
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Anubis: Artificial neuromodulation using a bayesian inference system
Neural Computation
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Acetylcholine (ACh) plays an important role in a wide variety of cognitive tasks, such as perception, selective attention, associative learning, and memory. Extensive experimental and theoretical work in tasks involving learning and memory has suggested that ACh reports on unfamiliarity and controls plasticity and effective network connectivity. Based on these computational and implementational insights, we develop a theory of cholinergic modulation in perceptual inference. We propose that ACh levels reflect the uncertainty associated with top-down information, and have the effect of modulating the interaction between top-down and bottom-up processing in determining the appropriate neural representations for inputs. We illustrate our proposal by means of an hierarchical hidden Markov model, showing that cholinergic modulation of contextual information leads to appropriate perceptual inference.