24,606 research outputs found
The Utility of Phase Models in Studying Neural Synchronization
Synchronized neural spiking is associated with many cognitive functions and
thus, merits study for its own sake. The analysis of neural synchronization
naturally leads to the study of repetitive spiking and consequently to the
analysis of coupled neural oscillators. Coupled oscillator theory thus informs
the synchronization of spiking neuronal networks. A crucial aspect of coupled
oscillator theory is the phase response curve (PRC), which describes the impact
of a perturbation to the phase of an oscillator. In neural terms, the
perturbation represents an incoming synaptic potential which may either advance
or retard the timing of the next spike. The phase response curves and the form
of coupling between reciprocally coupled oscillators defines the phase
interaction function, which in turn predicts the synchronization outcome
(in-phase versus anti-phase) and the rate of convergence. We review the two
classes of PRC and demonstrate the utility of the phase model in predicting
synchronization in reciprocally coupled neural models. In addition, we compare
the rate of convergence for all combinations of reciprocally coupled Class I
and Class II oscillators. These findings predict the general synchronization
outcomes of broad classes of neurons under both inhibitory and excitatory
reciprocal coupling.Comment: 18 pages, 5 figure
Mathematical analysis and simulations of the neural circuit for locomotion in lamprey
We analyze the dynamics of the neural circuit of the lamprey central pattern generator. This analysis provides insight into how neural interactions form oscillators and enable spontaneous oscillations in a network of damped oscillators, which were not apparent in previous simulations or abstract phase oscillator models. We also show how the different behavior regimes (characterized by phase and amplitude relationships between oscillators) of forward or backward swimming, and turning, can be controlled using the neural connection strengths and external inputs
Mathematical Analysis and Simulations of the Neural Circuit for Locomotion in Lamprey
We analyze the dynamics of the neural circuit of the lamprey central pattern
generator (CPG). This analysis provides insights into how neural interactions
form oscillators and enable spontaneous oscillations in a network of damped
oscillators, which were not apparent in previous simulations or abstract phase
oscillator models. We also show how the different behaviour regimes
(characterized by phase and amplitude relationships between oscillators) of
forward/backward swimming, and turning, can be controlled using the neural
connection strengths and external inputs.Comment: 4 pages, accepted for publication in Physical Review Letter
Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian
Workshop on Neural Networks, WIRN 201
Finding downbeats with a relaxation oscillator
Abstract.: A relaxation oscillator model of neural spiking dynamics is applied to the task of finding downbeats in rhythmical patterns. The importance of downbeat discovery or 'beat induction' is discussed, and the relaxation oscillator model is compared to other oscillator models. In a set of computer simulations the model is tested on 35 rhythmical patterns. The model performs well, making good predictions in 34 of 35 cases. In an analysis we identify some shortcomings of the model and relate model behavior to dynamical properties of relaxation oscillator
Pattern Formation in a Two-Dimensional Array of Oscillators with Phase-Shifted Coupling
We investigate the dynamics of a two-dimensional array of oscillators with
phase-shifted coupling. Each oscillator is allowed to interact with its
neighbors within a finite radius. The system exhibits various patterns
including squarelike pinwheels, (anti)spirals with phase-randomized cores, and
antiferro patterns embedded in (anti)spirals. We consider the symmetry
properties of the system to explain the observed behaviors, and estimate the
wavelengths of the patterns by linear analysis. Finally, we point out the
implications of our work for biological neural networks
Oscillator neural network model with distributed native frequencies
We study associative memory of an oscillator neural network with distributed
native frequencies. The model is based on the use of the Hebb learning rule
with random patterns (), and the distribution function of
native frequencies is assumed to be symmetric with respect to its average.
Although the system with an extensive number of stored patterns is not allowed
to get entirely synchronized, long time behaviors of the macroscopic order
parameters describing partial synchronization phenomena can be obtained by
discarding the contribution from the desynchronized part of the system. The
oscillator network is shown to work as associative memory accompanied by
synchronized oscillations. A phase diagram representing properties of memory
retrieval is presented in terms of the parameters characterizing the native
frequency distribution. Our analytical calculations based on the
self-consistent signal-to-noise analysis are shown to be in excellent agreement
with numerical simulations, confirming the validity of our theoretical
treatment.Comment: 9 pages, revtex, 6 postscript figures, to be published in J. Phys.
Analysis of Oscillator Neural Networks for Sparsely Coded Phase Patterns
We study a simple extended model of oscillator neural networks capable of
storing sparsely coded phase patterns, in which information is encoded both in
the mean firing rate and in the timing of spikes. Applying the methods of
statistical neurodynamics to our model, we theoretically investigate the
model's associative memory capability by evaluating its maximum storage
capacities and deriving its basins of attraction. It is shown that, as in the
Hopfield model, the storage capacity diverges as the activity level decreases.
We consider various practically and theoretically important cases. For example,
it is revealed that a dynamically adjusted threshold mechanism enhances the
retrieval ability of the associative memory. It is also found that, under
suitable conditions, the network can recall patterns even in the case that
patterns with different activity levels are stored at the same time. In
addition, we examine the robustness with respect to damage of the synaptic
connections. The validity of these theoretical results is confirmed by
reasonable agreement with numerical simulations.Comment: 23 pages, 11 figure
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
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