Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Multiplying with synapses and neurons
Single neuron computation
Statistically efficient estimation using population coding
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
Stationary Bumps in Networks of Spiking Neurons
Neural Computation
Interactivist-constructivist foundations for embodying attention
Journal of Experimental & Theoretical Artificial Intelligence
Population models of temporal differentiation
Neural Computation
A network of spiking neurons that can represent interval timing: mean field analysis
Journal of Computational Neuroscience
Neural affective decision theory: Choices, brains, and emotions
Cognitive Systems Research
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Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biological systems: static (point, line, ring, and plane) attractors, cyclic attractors, and chaotic attractors. We discuss these attractors in the context of the neural systems that they have been posited to help explain: eye control, working memory, and head direction; locomotion (specifically swimming); and olfaction, respectively. We then demonstrate how to introduce control into these models. The addition of control shows how attractor networks can be used as subsystems in larger neural systems, demonstrates how a much larger class of networks can be related to attractor networks, and makes it clear how attractor networks can be exploited for various information processing tasks in neurobiological systems.