2,014 research outputs found
Unipolar terminal-attractor-based neural associative memory with adaptive threshold and perfect convergence
A perfectly convergent unipolar neural associative-memory system based on nonlinear dynamical terminal attractors is presented. With adaptive setting of the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal attractors, perfect convergence is achieved. This achievement and correct retrieval are demonstrated by computer simulation. The simulations are completed (1) by exhaustive tests with all of the possible combinations of stored and test vectors in small-scale networks and (2) by Monte Carlo simulations with randomly generated stored and test vectors in large-scale networks with an M/N ratio of 4 (M is the number of stored vectors; N is the number of neurons < 256). An experiment with exclusive-oR logic operations with liquid-crystal-television spatial light modulators is used to show the feasibility of an optoelectronic implementation of the model. The behavior of terminal attractors in basins of energy space is illustrated by examples
Recurrent backpropagation and the dynamical approach to adaptive neural computation
Error backpropagation in feedforward neural network models is a popular learning algorithm that has its roots in nonlinear estimation and optimization. It is being used routinely to calculate error gradients in nonlinear systems with hundreds of thousands of parameters. However, the classical architecture for backpropagation has severe restrictions. The extension of backpropagation to networks with recurrent connections will be reviewed. It is now possible to efficiently compute the error gradients for networks that have temporal dynamics, which opens applications to a host of problems in systems identification and control
Computational neural learning formalisms for manipulator inverse kinematics
An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints
On the effects of firing memory in the dynamics of conjunctive networks
Boolean networks are one of the most studied discrete models in the context
of the study of gene expression. In order to define the dynamics associated to
a Boolean network, there are several \emph{update schemes} that range from
parallel or \emph{synchronous} to \emph{asynchronous.} However, studying each
possible dynamics defined by different update schemes might not be efficient.
In this context, considering some type of temporal delay in the dynamics of
Boolean networks emerges as an alternative approach. In this paper, we focus in
studying the effect of a particular type of delay called \emph{firing memory}
in the dynamics of Boolean networks. Particularly, we focus in symmetric
(non-directed) conjunctive networks and we show that there exist examples that
exhibit attractors of non-polynomial period. In addition, we study the
prediction problem consisting in determinate if some vertex will eventually
change its state, given an initial condition. We prove that this problem is
{\bf PSPACE}-complete
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recurrent networks. Point attractors are equilibrium
states of the dynamics (up to fluctuations), determined by the synaptic
structure of the network; a `basin' of attraction comprises all initial states
leading to a given attractor upon relaxation, hence making attractor dynamics
suitable to implement robust associative memory. The initial network state is
dictated by the stimulus, and relaxation to the attractor state implements the
retrieval of the corresponding memorized prototypical pattern. In a previous
work we demonstrated that a neuromorphic recurrent network of spiking neurons
and suitably chosen, fixed synapses supports attractor dynamics. Here we focus
on learning: activating on-chip synaptic plasticity and using a theory-driven
strategy for choosing network parameters, we show that autonomous learning,
following repeated presentation of simple visual stimuli, shapes a synaptic
connectivity supporting stimulus-selective attractors. Associative memory
develops on chip as the result of the coupled stimulus-driven neural activity
and ensuing synaptic dynamics, with no artificial separation between learning
and retrieval phases.Comment: submitted to Scientific Repor
Dynamics of Neural Networks with Continuous Attractors
We investigate the dynamics of continuous attractor neural networks (CANNs).
Due to the translational invariance of their neuronal interactions, CANNs can
hold a continuous family of stationary states. We systematically explore how
their neutral stability facilitates the tracking performance of a CANN, which
is believed to have wide applications in brain functions. We develop a
perturbative approach that utilizes the dominant movement of the network
stationary states in the state space. We quantify the distortions of the bump
shape during tracking, and study their effects on the tracking performance.
Results are obtained on the maximum speed for a moving stimulus to be
trackable, and the reaction time to catch up an abrupt change in stimulus.Comment: 6 pages, 7 figures with 4 caption
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