Benchmarking spike-based visual recognition: a dataset and evaluation

Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organisation have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarks and that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware implementations. With this dataset we hope to (1) promote meaningful comparison between algorithms in the field of neural computation, (2) allow comparison with conventional image recognition methods, (3) provide an assessment of the state of the art in spike-based visual recognition, and (4) help researchers identify future directions and advance the field

Brown Adipose Tissue Response to Cold Stimulation Is Reduced in Girls With Autoimmune Hypothyroidism

The interaction between thyroid status and brown adipose tissue (BAT) activation is complex. We assessed the effect of autoimmune hypothyroidism (ATD) in female children on BAT activation, measured using infrared thermography. Participants with ATD had lower resting (hypothyroid, 34.9 +- 0.7°C; control, 35.4 +- 0.5°C; P = 0.03) and stimulated (hypothyroid, 35.0 +- 0.6°C; control, 35.5 +- 0.5°C; P = 0.04) supraclavicular temperatures compared with controls, but there was no difference between groups in the temperature increase with stimulation. BAT activation, calculated as the relative temperature change comparing the supraclavicular temperature to a sternal reference region, was reduced in participants with ATD (hypothyroid, 0.1 +- 0.1°C; control, 0.2 +- 0.2°C; P = 0.04). Children with ATD were frequently biochemically euthyroid due to replacement therapy, but, despite this, increased relative supraclavicular temperature was closely associated with increased TSH (r = 0.7, P = 0.01) concentrations. Girls with ATD had an attenuated thermogenic response to cold stimulation compared with healthy controls, but, contrary to expectation, those with suboptimal biochemical control (with higher TSH) showed increased BAT activation. This suggests that the underlying disease process may have a negative effect on BAT response, but high levels of TSH can mitigate, and even stimulate, BAT activity. In summary, thyroid status is a complex determinant of BAT activity in girls with ATD.This work was supported by a pump-priming grant from Nottingham University Hospitals Charity (Grant PP-Law-Nov12)

Erratum to: Neuromodulation of lumbosacral spinal networks enables independent stepping after complete paraplegia (Nature Medicine, (2018), 24, 11, (1677-1682), 10.1038/s41591-018-0175-7)

© 2018, Springer Nature America, Inc. In the version of this article originally published, Dimitry G. Sayenko’s affiliations were not correct. The following affiliation for this author was missing: Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Research Institute, Houston, TX, USA. This affiliation has been added for the author, and the rest of the affiliations have been renumbered accordingly. The error has been corrected in the HTML and PDF versions of this article

Neuromodulation of lumbosacral spinal networks enables independent stepping after complete paraplegia

© 2018, The Author(s), under exclusive licence to Springer Nature America, Inc. Spinal sensorimotor networks that are functionally disconnected from the brain because of spinal cord injury (SCI) can be facilitated via epidural electrical stimulation (EES) to restore robust, coordinated motor activity in humans with paralysis1–3. Previously, we reported a clinical case of complete sensorimotor paralysis of the lower extremities in which EES restored the ability to stand and the ability to control step-like activity while side-lying or suspended vertically in a body-weight support system (BWS)4. Since then, dynamic task-specific training in the presence of EES, termed multimodal rehabilitation (MMR), was performed for 43 weeks and resulted in bilateral stepping on a treadmill, independent from trainer assistance or BWS. Additionally, MMR enabled independent stepping over ground while using a front-wheeled walker with trainer assistance at the hips to maintain balance. Furthermore, MMR engaged sensorimotor networks to achieve dynamic performance of standing and stepping. To our knowledge, this is the first report of independent stepping enabled by task-specific training in the presence of EES by a human with complete loss of lower extremity sensorimotor function due to SCI