Table of Contents
What is an attractor neural network?
In general, an attractor network is a network of nodes (i.e., neurons in a biological network), often recurrently connected, whose time dynamics settle to a stable pattern. That pattern may be stationary, time-varying (e.g. cyclic), or even stochastic-looking (e.g., chaotic).
What is a continuous attractor network?
A continuous attractor network (or continuous-attractor neural network, CANN) is an attractor network possessing one or more quasicontinuous sets of attractors that in the limit of an infinite number of neuronal units N merge into continuous attractor(s).
What are attractor dynamics?
In the mathematical field of dynamical systems, an attractor is a set of states toward which a system tends to evolve, for a wide variety of starting conditions of the system. System values that get close enough to the attractor values remain close even if slightly disturbed.
What is an attractor point?
Attractors are points that act like virtual magnets – either attracting or repelling other objects. In Grasshopper, any geometry referenced from Rhino or created withinGrasshopper can be used as an attractor.
What is two point attractor?
Basically what point attractor (or repeller) definition does is that it uses the distance from attractor/repeller point to grid points, as a values for circle radii. It does not need to be circle radii, it can be anything else, in my example below it’s radii of a hexagon.
What is the disadvantage of Hopfield network?
A major disadvantage of the Hopfield network is that it can rest in a local minimum state instead of a global minimum energy state, thus associating a new input pattern with a spurious state.
Is hopfield unsupervised learning?
The learning algorithm of the Hopfield network is unsupervised, meaning that there is no “teacher” telling the network what is the correct output for a certain input.
What is Continuous attractor neural network (Cann)?
A continuous attractor neural network (CANN) model. ( A) An illustration of a one-dimensional CANN, which encodes a continuous variable (e.g. orientation or direction) x in the region of (- π,π] with the periodic condition. Neurons are aligned in the network according to their preferred stimuli.
What are some examples of attractor networks?
Let’s look at two examples of attractor networks. The first we will look at is the Hopfield network, an artificial neural network. The second we will look at is a spiking neural network from [3] (Wang 2002). Hopfield networks [2] (Hopfield 1982 ) are recurrent neural networks using binary neuron.
What are recurrent neural networks with attractor states?
These are recurrent neural networks with attractor states; these states and the dynamics governing an attractor networks evolution between attractor states endow these networks with powerful computational properties. Some attractor networks are useful models of neural circuits.
What is attractor space in neural networks?
Dynamical systems theory provides us with two levels of description: network space: The full state of the neural network, which is quite large and unwieldy. attractor space: A reduced space of the full neural network. Only includes points on the attractors. Let’s look at two examples of attractor networks.