7,200 research outputs found
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.Comment: 13 figures (includes supplementary information
Synchronous Behavior of Two Coupled Electronic Neurons
We report on experimental studies of synchronization phenomena in a pair of
analog electronic neurons (ENs). The ENs were designed to reproduce the
observed membrane voltage oscillations of isolated biological neurons from the
stomatogastric ganglion of the California spiny lobster Panulirus interruptus.
The ENs are simple analog circuits which integrate four dimensional
differential equations representing fast and slow subcellular mechanisms that
produce the characteristic regular/chaotic spiking-bursting behavior of these
cells. In this paper we study their dynamical behavior as we couple them in the
same configurations as we have done for their counterpart biological neurons.
The interconnections we use for these neural oscillators are both direct
electrical connections and excitatory and inhibitory chemical connections: each
realized by analog circuitry and suggested by biological examples. We provide
here quantitative evidence that the ENs and the biological neurons behave
similarly when coupled in the same manner. They each display well defined
bifurcations in their mutual synchronization and regularization. We report
briefly on an experiment on coupled biological neurons and four dimensional ENs
which provides further ground for testing the validity of our numerical and
electronic models of individual neural behavior. Our experiments as a whole
present interesting new examples of regularization and synchronization in
coupled nonlinear oscillators.Comment: 26 pages, 10 figure
Dynamic sensor Patent
Dynamic sensor for gas pressure or density measuremen
Synchronization in dynamic neural networks
This thesis is concerned with the function and implementation of synchronization in networks of oscillators. Evidence for the existence of synchronization in cortex is reviewed and a suitable architecture for exhibiting synchronization is defined. A number of factors which affect the performance of synchronization in networks of laterally coupled oscillators are investigated. It is shown that altering the strength of the lateral connections between nodes and altering the connective scope of a network can be used to improve synchronization performance. It is also shown that complete connective scope is not required for global synchrony to occur. The effects of noise on synchronization performance are also investigated and it is shown that where an oscillator network is able to synchronize effectively, it will also be robust to a moderate level of noise in the lateral connections. Where a particular oscillator model shows poor synchronization performance, it is shown that noise in the lateral connections is capable of improving synchronization performance.
A number of applications of synchronizing oscillator networks are investigated. The use of synchronized oscillations to encode global binding information is investigated and the relationship between the form of grouping obtained and connective scope is discussed. The potential for using learning in synchronizing oscillator networks is illustrated and an investigation is made into the possibility of maintaining multiple phases in a network of synchronizing oscillators. It is concluded from these investigations that it is difficult to maintain multiple phases in the network architecture used throughout this thesis and a modified architecture capable of producing the required behaviour is demonstrated
The context dependence of network response properties in the primary visual cortex of the primate and cat
In the mammalian visual system, stimulus context was investigated with respect to the ways it influenced neuronal mean response magnitude (the average number of spikes fired per second), response temporal structure (the timing of spikes with respect to one another), and the extent to which distributed neurones fired spikes synchronous due to synaptic interaction between them. Neurones were presented with bipartite grating stimuli, in which the spatio-temporal relationship between the grating activating the excitatory receptive field and that presented to the surrounding visual space could be varied systematically. Simultaneous extracellular recordings were made of the responses of up to four single neurones separated by 750-1000Āµm, in the lateral geniculate nucleus (LGN) of the thalamus in the cat, or the primary visual cortex (V1) of non-human primates or cats. Changing context systematically influenced the activity of groups of cells. The responses of 83% of primate V1 cells to discontinuous stimuli, in which the centre/surround orientation difference was greater than 45Ā°, contained stronger oscillations at frequencies below 80Hz, than responses to continuous stimuli. Many cat and primate V1 neurones exhibited elevated response magnitudes to such stimuli. In primate V1, the strength of a cell's oscillatory discharge was dependent on stimulus configuration rather than response magnitude. In the LGN and V1, cell pairs with different orientation preferences fired synchronised responses when stimulated by specific discontinuous grating configurations. Stimulus specific synchronised LGN input, and reciprocal excitatory and inhibitory cortico-cortical connections could generate these properties of cells, and the network in which they exist. A model is proposed to account for the function significance of contour discontinuities in generating coherent neural representations of objects in the visual world. It involves response synchronisation in horizontal, feedforward and feedback interactions, within and between the LGN, V1, V2 and V4
Modeling, Simulation and Emulation of Intelligent Domotic Environments
Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirement
Locally-Stable Macromodels of Integrated Digital Devices for Multimedia Applications
This paper addresses the development of accurate and efficient behavioral models of digital integrated circuits for the assessment of high-speed systems. Device models are based on suitable parametric expressions estimated from port transient responses and are effective at system level, where the quality of functional signals and the impact of supply noise need to be simulated. A potential limitation of some state-of-the-art modeling techniques resides in hidden instabilities manifesting themselves in the use of models, without being evident in the building phase of the same models. This contribution compares three recently-proposed model structures, and selects the local-linear state-space modeling technique as an optimal candidate for the signal integrity assessment of data links. In fact, this technique combines a simple verification of the local stability of models with a limited model size and an easy implementation in commercial simulation tools. An application of the proposed methodology to a real problem involving commercial devices and a data-link of a wireless device demonstrates the validity of this approac
A deep learning integrated Lee-Carter model
In the field of mortality, the LeeāCarter based approach can be considered the milestone
to forecast mortality rates among stochastic models. We could define a āLeeāCarter model familyā
that embraces all developments of this model, including its first formulation (1992) that remains the
benchmark for comparing the performance of future models. In the LeeāCarter model, the kt parameter,
describing the mortality trend over time, plays an important role about the future mortality behavior.
The traditional ARIMA process usually used to model kt shows evident limitations to describe the future
mortality shape. Concerning forecasting phase, academics should approach a more plausible way in
order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative
approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch
the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long
Short-Term Memory architecture and integrate the LeeāCarter model to improve its predictive capacity.
The proposed approach provides significant performance in terms of predictive accuracy and also allow
for avoiding the time-chunksā a priori selection. Indeed, it is a common practice among academics to
delete the time in which the noise is overflowing or the data quality is insufficient. The strength of
the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it
into the forecasted trend, due to its own architecture enabling to take into account significant long-term
patterns
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