1,143 research outputs found
Multistable jittering in oscillators with pulsatile delayed feedback
Oscillatory systems with time-delayed pulsatile feedback appear in various
applied and theoretical research areas, and received a growing interest in the
last years. For such systems, we report a remarkable scenario of
destabilization of a periodic regular spiking regime. In the bifurcation point
numerous regimes with non-equal interspike intervals emerge simultaneously. We
show that this bifurcation is triggered by the steepness of the oscillator's
phase resetting curve and that the number of the emerging, so-called
"jittering" regimes grows exponentially with the delay value. Although this
appears as highly degenerate from a dynamical systems viewpoint, the
"multi-jitter" bifurcation occurs robustly in a large class of systems. We
observe it not only in a paradigmatic phase-reduced model, but also in a
simulated Hodgkin-Huxley neuron model and in an experiment with an electronic
circuit
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
Real-time support for high performance aircraft operation
The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown
Efficiency characterization of a large neuronal network: a causal information approach
When inhibitory neurons constitute about 40% of neurons they could have an
important antinociceptive role, as they would easily regulate the level of
activity of other neurons. We consider a simple network of cortical spiking
neurons with axonal conduction delays and spike timing dependent plasticity,
representative of a cortical column or hypercolumn with large proportion of
inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics
and it is interconnected randomly to other neurons. The network dynamics is
investigated estimating Bandt and Pompe probability distribution function
associated to the interspike intervals and taking different degrees of
inter-connectivity across neurons. More specifically we take into account the
fine temporal ``structures'' of the complex neuronal signals not just by using
the probability distributions associated to the inter spike intervals, but
instead considering much more subtle measures accounting for their causal
information: the Shannon permutation entropy, Fisher permutation information
and permutation statistical complexity. This allows us to investigate how the
information of the system might saturate to a finite value as the degree of
inter-connectivity across neurons grows, inferring the emergent dynamical
properties of the system.Comment: 26 pages, 3 Figures; Physica A, in pres
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