Generation of unusually low frequency plasmaspheric hiss

It has been reported from Van Allen Probe observations that plasmaspheric hiss intensification in the outer plasmasphere, associated with a substorm injection on 30 September 2012, occurred with a peak frequency near 100 Hz, well below the typical plasmaspheric hiss frequency range, extending down to ∼20 Hz. We examine this event of unusually low frequency plasmaspheric hiss to understand its generation mechanism. Quantitative analysis is performed by simulating wave raypaths via the HOTRAY ray tracing code with measured plasma density and calculating raypath-integrated wave gain evaluated using the measured energetic electron distribution. We demonstrate that the growth rate due to substorm-injected electrons is positive but rather weak, leading to small wave gain (∼10 dB) during a single equatorial crossing. Propagation characteristics aided by the sharp density gradient associated with the plasmapause, however, can enable these low-frequency waves to undergo cyclic raypaths, which return to the unstable region leading to repeated amplification to yield sufficient net wave gain (>40 dB) to allow waves to grow from the thermal noise

New Frontiers-class Uranus Orbiter: Exploring the feasibility of achieving multidisciplinary science with a mid-scale mission

n/

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-60410