39,638 research outputs found
Evaluating performance of neural codes in model neural communication networks
Information needs to be appropriately encoded to be reliably transmitted over physical media. Similarly, neurons have their own codes to convey information in the brain. Even though it is well-known that neurons exchange information using a pool of several protocols of spatio-temporal encodings, the suitability of each code and their performance as a function of network parameters and external stimuli is still one of the great mysteries in neuroscience. This paper sheds light on this by modeling small-size networks of chemically and electrically coupled Hindmarsh-Rose spiking neurons. We focus on a class of temporal and firing-rate codes that result from neurons' membrane-potentials and phases, and quantify numerically their performance estimating the Mutual Information Rate, aka the rate of information exchange. Our results suggest that the firing-rate and interspike-intervals codes are more robust to additive Gaussian white noise. In a network of four interconnected neurons and in the absence of such noise, pairs of neurons that have the largest rate of information exchange using the interspike-intervals and firing-rate codes are not adjacent in the network, whereas spike-timings and phase codes (temporal) promote large rate of information exchange for adjacent neurons. If that result would have been possible to extend to larger neural networks, it would suggest that small microcircuits would preferably exchange information using temporal codes (spike-timings and phase codes), whereas on the macroscopic scale, where there would be typically pairs of neurons not directly connected due to the brain's sparsity, firing-rate and interspike-intervals codes would be the most efficient codes
Evaluating performance of neural codes in model neural communication networks
Information needs to be appropriately encoded to be reliably transmitted over a physical media. Similarly, neurons have their own codes to convey information in the brain. Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience. This paper sheds light into this problem considering small networks of chemically and electrically coupled Hindmarsh-Rose spiking neurons. We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange. A particularly interesting result regards the performance of the codes with respect to the way neurons are connected. We show that pairs of neurons that have the largest rate of information exchange using the interspike interval and firing-rate codes are not adjacent in the network, whereas the spiking-time and phase codes promote large exchange of information rate from adjacent neurons. This result, if possible to extend to larger neural networks, would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes (spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
Scaling Deep Learning on GPU and Knights Landing clusters
The speed of deep neural networks training has become a big bottleneck of
deep learning research and development. For example, training GoogleNet by
ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training
process, the current deep learning systems heavily rely on the hardware
accelerators. However, these accelerators have limited on-chip memory compared
with CPUs. To handle large datasets, they need to fetch data from either CPU
memory or remote processors. We use both self-hosted Intel Knights Landing
(KNL) clusters and multi-GPU clusters as our target platforms. From an
algorithm aspect, current distributed machine learning systems are mainly
designed for cloud systems. These methods are asynchronous because of the slow
network and high fault-tolerance requirement on cloud systems. We focus on
Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original
EASGD used round-robin method for communication and updating. The communication
is ordered by the machine rank ID, which is inefficient on HPC clusters.
First, we redesign four efficient algorithms for HPC systems to improve
EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD
are faster \textcolor{black}{than} their existing counterparts (Async SGD,
Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design
Sync EASGD, which ties for the best performance among all the methods while
being deterministic. In addition to the algorithmic improvements, we use some
system-algorithm codesign techniques to scale up the algorithms. By reducing
the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x
speedup over original EASGD on the same platform. We get 91.5% weak scaling
efficiency on 4253 KNL cores, which is higher than the state-of-the-art
implementation
- …