This paper presents a comprehensive overview of modelling, simulation and implementation of neural networks, taking into account that two aims have emerged in this area: the improvement of our ...understanding of the behaviour of the nervous system and the need to find inspiration from it to build systems with the advantages provided by nature to perform certain relevant tasks. The development and evolution of different topics related to neural networks is described (simulators, implementations, and real-world applications) showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems. The paper also shows how, over time, artificial neural networks have contributed to fundamental concepts at the birth and development of other disciplines such as Computational Neuroscience, Neuro-engineering, Computational Intelligence and Machine Learning. A better understanding of the human brain is considered one of the challenges of this century, and to achieve it, as this paper goes on to describe, several important national and multinational projects and initiatives are marking the way to follow in neural-network research.
Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response ...patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.
We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. ...The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental setup (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and RT operation. The RT supervisor module provides for incremental countermeasures that dynamically slow down or speed up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e., STDP mechanisms, spike propagation, and neural updates) to cope with the RT constraints imposed by the real robot operation. This neurorobotic experimental setup is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This paper underlies the need for a two-stage learning process to facilitate VOR acquisition.
The Radio to GeV Afterglow of GRB 221009A Laskar, Tanmoy; Alexander, Kate D.; Margutti, Raffaella ...
Astrophysical journal. Letters,
03/2023, Letnik:
946, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Abstract
GRB 221009A (
z
= 0.151) is one of the closest known long
γ
-ray bursts (GRBs). Its extreme brightness across all electromagnetic wavelengths provides an unprecedented opportunity to study a ...member of this still-mysterious class of transients in exquisite detail. We present multiwavelength observations of this extraordinary event, spanning 15 orders of magnitude in photon energy from radio to
γ
-rays. We find that the data can be partially explained by a forward shock (FS) from a highly collimated relativistic jet interacting with a low-density, wind-like medium. Under this model, the jet’s beaming-corrected kinetic energy (
E
K
∼ 4 × 10
50
erg) is typical for the GRB population. The radio and millimeter data provide strong limiting constraints on the FS model, but require the presence of an additional emission component. From equipartition arguments, we find that the radio emission is likely produced by a small amount of mass (≲6 × 10
−7
M
⊙
) moving relativistically (Γ ≳ 9) with a large kinetic energy (≳10
49
erg). However, the temporal evolution of this component does not follow prescriptions for synchrotron radiation from a single power-law distribution of electrons (e.g., in a reverse shock or two-component jet), or a thermal-electron population, perhaps suggesting that one of the standard assumptions of afterglow theory is violated. GRB 221009A will likely remain detectable with radio telescopes for years to come, providing a valuable opportunity to track the full lifecycle of a powerful relativistic jet.
Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher ...brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.
Abstract
Recent studies have proposed that the nuclear millimeter continuum emission observed in nearby active galactic nuclei (AGNs) could be created by the same population of electrons that gives ...rise to the X-ray emission that is ubiquitously observed in accreting black holes. We present the results of a dedicated high-spatial-resolution (∼60–100 mas) Atacama Large Millimeter/submillimeter Array (ALMA) campaign on a volume-limited (<50 Mpc) sample of 26 hard X-ray (>10 keV) selected radio-quiet AGNs. We find an extremely high detection rate (25/26 or
94
−
6
+
3
%
), which shows that nuclear emission at millimeter wavelengths is nearly ubiquitous in accreting SMBHs. Our high-resolution observations show a tight correlation between the nuclear (1–23 pc) 100 GHz and the intrinsic X-ray emission (1
σ
scatter of 0.22 dex). The ratio between the 100 GHz continuum and the X-ray emission does not show any correlation with column density, black hole mass, Eddington ratio, or star formation rate, which suggests that the 100 GHz emission can be used as a proxy of SMBH accretion over a very broad range of these parameters. The strong correlation between 100 GHz and X-ray emission in radio-quiet AGNs could be used to estimate the column density based on the ratio between the observed 2–10 keV (
F
2
–
10
keV
obs
) and 100 GHz (
F
100 GHz
) fluxes. Specifically, a ratio
log
(
F
2
–
10
keV
obs
/
F
100
GHz
)
≤
3.5
strongly suggests that a source is heavily obscured (
log
(
N
H
/
cm
−
2
)
≳
23.8
). Our work shows the potential of ALMA continuum observations to detect heavily obscured AGNs (up to an optical depth of one at 100 GHz, i.e.,
N
H
≃ 10
27
cm
−2
), and to identify binary SMBHs with separations <100 pc, which cannot be probed by current X-ray facilities.
Depth information has been used in computer vision for a wide variety of tasks. Since active range sensors are currently available at low cost, high-quality depth maps can be used as relevant input ...for many applications. Background subtraction and video segmentation algorithms can be improved by fusing depth and color inputs, which are complementary and allow one to solve many classic color segmentation issues. In this paper, we describe one fusion method to combine color and depth based on an advanced color-based algorithm. This technique has been evaluated by means of a complete dataset recorded with Microsoft Kinect, which enables comparison with the original method. The proposed method outperforms the others in almost every test, showing more robustness to illumination changes, shadows, reflections and camouflage.
Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating ...large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.
In Schmidt et al. the authors quantified the contributions that reflexes, and CPGs have on highly dynamic compliant movements and assessed the biomimetic robotic legs stability and energy efficiency ...under different environmental influences. In order to guarantee fast online learning of the control parameters without the knowledge of a dynamic model of a real UAV system, a neural control method based on three-neuron network is presented in Jaiton et al.. ...tools like NeuroVis (Srisuchinnawong et al.) will facilitate understanding embodied dynamics of neural information processes, boosts efficient neural technology development.
Context. Very long baseline interferometry (VLBI) observations at 86 GHz (wavelength, λ = 3 mm) reach a resolution of about 50 μas, probing the collimation and acceleration regions of relativistic ...outflows in active galactic nuclei (AGN). The physical conditions in these regions can be studied by performing 86 GHz VLBI surveys of representative samples of compact extragalactic radio sources. Aims. To extend the statistical studies of compact extragalactic jets, a large global 86 GHz VLBI survey of 162 compact radio sources was conducted in 2010–2011 using the Global Millimeter VLBI Array (GMVA). Methods. The survey observations were made in a snapshot mode, with up to five scans per target spread over a range of hour angles in order to optimize the visibility coverage. The survey data attained a typical baseline sensitivity of 0.1 Jy and a typical image sensitivity of 5 mJy beam−1, providing successful detections and images for all of the survey targets. For 138 objects, the survey provides the first ever VLBI images made at 86 GHz. Gaussian model fitting of the visibility data was applied to represent the structure of the observed sources and to estimate the flux densities and sizes of distinct emitting regions (components) in their jets. These estimates were used for calculating the brightness temperature (Tb) at the jet base (core) and in one or more moving regions (jet components) downstream from the core. These model-fit-based estimates of Tb were compared to the estimates of brightness temperature limits made directly from the visibility data, demonstrating a good agreement between the two methods. Results. The apparent brightness temperature estimates for the jet cores in our sample range from 2.5 × 109 K to 1.3 × 1012 K, with the mean value of 1.8 × 1011 K. The apparent brightness temperature estimates for the inner jet components in our sample range from 7.0 × 107 K to 4.0 × 1011 K. A simple population model with a single intrinsic value of brightness temperature, T0, is applied to reproduce the observed distribution. It yields T0 = (3.77−0.14+0.10) × 1011 K T 0 = ( 3 . 77 − 0.14 + 0.10 ) × 10 11 K $ T_{\mathrm{0}} = (3.77^{+0.10}_{-0.14}) \times 10^{11} {\rm K} $ for the jet cores, implying that the inverse Compton losses dominate the emission. In the nearest jet components, T0 = (1.42−0.19+0.16) × 1011 K T 0 = ( 1 . 42 − 0.19 + 0.16 ) × 10 11 K $ T_{\mathrm{0}} = (1.42^{+0.16}_{-0.19}) \times 10^{11} {\rm K} $ is found, which is slightly higher than the equipartition limit of ∼5 × 1010 K expected for these jet regions. For objects with sufficient structural detail detected, the adiabatic energy losses are shown to dominate the observed changes of brightness temperature along the jet.
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