6,836 research outputs found
Asynchronous spiking neurons, the natural key to exploit temporal sparsity
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms
Synapse elimination and learning rules co-regulated by MHC class I H2-Db.
The formation of precise connections between retina and lateral geniculate nucleus (LGN) involves the activity-dependent elimination of some synapses, with strengthening and retention of others. Here we show that the major histocompatibility complex (MHC) class I molecule H2-D(b) is necessary and sufficient for synapse elimination in the retinogeniculate system. In mice lacking both H2-K(b) and H2-D(b) (K(b)D(b)(-/-)), despite intact retinal activity and basal synaptic transmission, the developmentally regulated decrease in functional convergence of retinal ganglion cell synaptic inputs to LGN neurons fails and eye-specific layers do not form. Neuronal expression of just H2-D(b) in K(b)D(b)(-/-) mice rescues both synapse elimination and eye-specific segregation despite a compromised immune system. When patterns of stimulation mimicking endogenous retinal waves are used to probe synaptic learning rules at retinogeniculate synapses, long-term potentiation (LTP) is intact but long-term depression (LTD) is impaired in K(b)D(b)(-/-) mice. This change is due to an increase in Ca(2+)-permeable AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors. Restoring H2-D(b) to K(b)D(b)(-/-) neurons renders AMPA receptors Ca(2+) impermeable and rescues LTD. These observations reveal an MHC-class-I-mediated link between developmental synapse pruning and balanced synaptic learning rules enabling both LTD and LTP, and demonstrate a direct requirement for H2-D(b) in functional and structural synapse pruning in CNS neurons
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars
Event cameras are bio-inspired vision sensors that naturally capture the
dynamics of a scene, filtering out redundant information. This paper presents a
deep neural network approach that unlocks the potential of event cameras on a
challenging motion-estimation task: prediction of a vehicle's steering angle.
To make the best out of this sensor-algorithm combination, we adapt
state-of-the-art convolutional architectures to the output of event sensors and
extensively evaluate the performance of our approach on a publicly available
large scale event-camera dataset (~1000 km). We present qualitative and
quantitative explanations of why event cameras allow robust steering prediction
even in cases where traditional cameras fail, e.g. challenging illumination
conditions and fast motion. Finally, we demonstrate the advantages of
leveraging transfer learning from traditional to event-based vision, and show
that our approach outperforms state-of-the-art algorithms based on standard
cameras.Comment: 9 pages, 8 figures, 6 tables. Video: https://youtu.be/_r_bsjkJTH
Towards a Reconfigurable Sense-and-Stimulate Neural Interface Generating Biphasic Interleaved Stimulus
Published versio
A user-friendly system to measure electromyographic activity of dancers
A data acquisition system aided by virtual instrumentation was developed to measure electromyographic activity of the dancers. The system is composed of three main components: (1) Analog front-end with signal conditioning, (2) USB serial interface based A/D conversion, and (3) virtual instrumentation designed in LabView tools. The proposed system is able to accurately collect the magnitude of jump force and displayed the data using virtual instruments with alarm functions. The signal path is well-conditioned and processed, which makes the device suitable for feasibility studies for future research
BCI-Based Navigation in Virtual and Real Environments
A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.This work was partially supported by the Innovation, Science and Enterprise Council of the Junta de AndalucĂa (Spain), project P07-TIC-03310, the Spanish Ministry of Science and Innovation, project TEC 2011-26395 and by the European fund ERDF
Brain-Switches for Asynchronous BrainâComputer Interfaces: A Systematic Review
A brainâcomputer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance
How Noise and Coupling Induce Bursting Action Potentials in Pancreatic beta-cells
Unlike isolated beta-cells, which usually produce continuous spikes or fast
and irregular bursts, electrically coupled beta-cells are apt to exhibit robust
bursting action potentials. We consider the noise induced by thermal
fluctuations as well as that by channel gating stochasticity and examine its
effects on the action potential behavior of the beta-cell model. It is observed
numerically that such noise in general helps single cells to produce a variety
of electrical activities. In addition, we also probe coupling via gap junctions
between neighboring cells,with heterogeneity induced by noise, to find that it
enhances regular bursts.Comment: 40 pages, 10 figure
- âŠ