| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Crook, S. M., et al.
(författare)
-
Creating, documenting and sharing network models
- 2012
-
Ingår i: Network. - 0954-898X .- 1361-6536. ; 23:4, s. 131-149
-
Forskningsöversikt (refereegranskat)abstract
- As computational neuroscience matures, many simulation environments are available that are useful for neuronal network modeling. However, methods for successfully documenting models for publication and for exchanging models and model components among these projects are still under development. Here we briefly review existing software and applications for network model creation, documentation and exchange. Then we discuss a few of the larger issues facing the field of computational neuroscience regarding network modeling and suggest solutions to some of these problems, concentrating in particular on standardized network model terminology, notation, and descriptions and explicit documentation of model scaling. We hope this will enable and encourage computational neuroscientists to share their models more systematically in the future.
|
|
| 5. |
- Salles, J, et al.
(författare)
-
Vitamin D status modulates mitochondrial oxidative capacities in skeletal muscle: role in sarcopenia
- 2022
-
Ingår i: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 5:1, s. 1288-
-
Tidskriftsartikel (refereegranskat)abstract
- Skeletal muscle mitochondrial function is the biggest component of whole-body energy output. Mitochondrial energy production during exercise is impaired in vitamin D-deficient subjects. In cultured myotubes, loss of vitamin D receptor (VDR) function decreases mitochondrial respiration rate and ATP production from oxidative phosphorylation. We aimed to examine the effects of vitamin D deficiency and supplementation on whole-body energy expenditure and muscle mitochondrial function in old rats, old mice, and human subjects. To gain further insight into the mechanisms involved, we used C2C12 and human muscle cells and transgenic mice with muscle-specific VDR tamoxifen-inducible deficiency. We observed that in vivo and in vitro vitamin D fluctuations changed mitochondrial biogenesis and oxidative activity in skeletal muscle. Vitamin D supplementation initiated in older people improved muscle mass and strength. We hypothesize that vitamin D supplementation is likely to help prevent not only sarcopenia but also sarcopenic obesity in vitamin D-deficient subjects.
|
|
| 6. |
|
|
| 7. |
- Knight, James C., et al.
(författare)
-
Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
- 2016
-
Ingår i: Frontiers in Neuroanatomy. - : Frontiers Media SA. - 1662-5129. ; 10
-
Tidskriftsartikel (refereegranskat)abstract
- SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 x 10(4) neurons and 5.1 x 10(7) plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45x more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.
|
|
| 8. |
- Romero Bermudez, Juan Pablo, et al.
(författare)
-
A High-Throughput Low-Latency Interface Board for SpiNNaker-in-the-loop Real-Time Systems
- 2023
-
Ingår i: ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023. - : Association for Computing Machinery (ACM).
-
Konferensbidrag (refereegranskat)abstract
- The Spiking Neural Network Computer Architecture (SpiNNaker) is a massively parallel computing system. As one of the most widespread platforms in the emerging field of neuromorphic engineering, SpiNNaker targets three main areas of research: computational neuroscience, computer science, and robotics. For the latter, the promise of low power computation and the potential for large scale simulations in real-time make SpiNNaker very attractive, especially for autonomous mobile applications. In this context, research groups typically use SpiNNaker's Ethernet interface to inject and extract sensori-motor signals into and from SpiNNaker. However, in cases where the data throughput increases, the on-board Ethernet port constitutes a critical bottleneck. Some groups have overcome such a problem to some extent by developing their own I/O interfaces to connect external devices - - sensors and actuators - - directly to SpiNNaker. However, such custom-developed interfaces allow only limited general applications, and they don't fully exploit the high-speed FPGA-based interconnect offered by the 48-chip SpiNNaker boards.In this manuscript, we present SPIF: a general-purpose FPGA-based SpiNNaker Peripheral Interface board that overcomes SpiNNaker's communication bottleneck by connecting to its native High-Speed Serial Links (HSSLs). We evaluate SPIF's performance in terms of event throughput and latency. Finally, we demonstrate SPIF's capabilities by feeding events from a high-resolution event camera into a real-time spiking convolutional neural network. The system can track the position of a small and extremely fast but salient stimulus in the visual field with negligibly low latency.
|
|
