What is a ring attractor?

Ring attractors A subtype of continuous attractors with a particular topology of the neurons (ring for 1-dimensional and torus or twisted torus for 2-dimensional networks). The observed activity of grid cells is successfully explained by assuming the presence of ring attractors in the medial entorhinal cortex.

What is an attractor state?

In principle, an attractor state is a temporarily self-sustaining state. According to various authors (Meindertsma, 2014; De Ruiter et al., 2017), indications for attractor states can already be observed on a short-term timescale, based on the pattern of short-term variability of the elements or variables.

What is a deep attractor network?

Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors. Compared with methods based on the permutation invariant training (PIT), DANs define a deep embedding space and deliver a more elaborate representation on each time-frequency (T-F) bin.

What is a continuous attractor?

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 is 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. The particular pattern a network settles to is called its ‘attractor’.

What is an example of an attractor?

Frequency: &diamf3 A point attractor is an attractor consisting of a single state. For example, a marble rolling in a smooth, rounded bowl will always come to rest at the lowest point, in the bottom center of the bowl; the final state of position and motionlessness is a point attractor.

What is attractor behavior?

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 basin of attraction?

The basin of attraction of an attracting set is the set of all the initial conditions in the phase space whose trajectories go to that attracting set.

What is Computational Neuroscience used for?

Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information.

What is strange attractor behavior?

mathematics. : the state of a mathematically chaotic system toward which the system trends : the attractor of a mathematically chaotic system Unlike the randomness generated by a system with many variables, chaos has its own pattern, a peculiar kind of order.

How is an attractor network used in neuroscience?

Attractor networks have largely been used in computational neuroscience to model neuronal processes such as associative memory and motor behavior, as well as in biologically inspired methods of machine learning. An attractor network contains a set of n nodes, which can be represented as vectors in a d -dimensional space where n > d.

What kind of pattern does an attractor network have?

Attractor network. An attractor network is a type of recurrent dynamical network, that evolves toward a stable pattern over time. Nodes in the attractor network converge toward a pattern that may either be fixed-point (a single state), cyclic (with regularly recurring states), chaotic (locally but not globally unstable) or random (stochastic).

What does the fix point of a Continuous attractor mean?

Neighboring stable states (fix points) of continuous attractors (also called continuous attractor neural networks) code for neighboring values of a continuous variable such as head direction or actual position in space.

How are cyclic attractors and chaotic attractors alike?

Cyclic attractors evolve the network toward a set of states in a limit cycle, which is repeatedly traversed. Chaotic attractors are non-repeating bounded attractors that are continuously traversed. The network state space is the set of all possible node states.