9,304 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
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks
We study in this paper the effect of an unique initial stimulation on random
recurrent networks of leaky integrate and fire neurons. Indeed given a
stochastic connectivity this so-called spontaneous mode exhibits various non
trivial dynamics. This study brings forward a mathematical formalism that
allows us to examine the variability of the afterward dynamics according to the
parameters of the weight distribution. Provided independence hypothesis (e.g.
in the case of very large networks) we are able to compute the average number
of neurons that fire at a given time -- the spiking activity. In accordance
with numerical simulations, we prove that this spiking activity reaches a
steady-state, we characterize this steady-state and explore the transients.Comment: 28 pages, 7 figure
Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Network
We have calculated the key characteristics of associative
(content-addressable) spatial-temporal memories based on neuromorphic networks
with restricted connectivity - "CrossNets". Such networks may be naturally
implemented in nanoelectronic hardware using hybrid CMOS/memristor circuits,
which may feature extremely high energy efficiency, approaching that of
biological cortical circuits, at much higher operation speed. Our numerical
simulations, in some cases confirmed by analytical calculations, have shown
that the characteristics depend substantially on the method of information
recording into the memory. Of the four methods we have explored, two look
especially promising - one based on the quadratic programming, and the other
one being a specific discrete version of the gradient descent. The latter
method provides a slightly lower memory capacity (at the same fidelity) then
the former one, but it allows local recording, which may be more readily
implemented in nanoelectronic hardware. Most importantly, at the synchronous
retrieval, both methods provide a capacity higher than that of the well-known
Ternary Content-Addressable Memories with the same number of nonvolatile memory
cells (e.g., memristors), though the input noise immunity of the CrossNet
memories is somewhat lower
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