1,133 research outputs found
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Precise spike timing as a means to encode information in neural networks is
biologically supported, and is advantageous over frequency-based codes by
processing input features on a much shorter time-scale. For these reasons, much
recent attention has been focused on the development of supervised learning
rules for spiking neural networks that utilise a temporal coding scheme.
However, despite significant progress in this area, there still lack rules that
have a theoretical basis, and yet can be considered biologically relevant. Here
we examine the general conditions under which synaptic plasticity most
effectively takes place to support the supervised learning of a precise
temporal code. As part of our analysis we examine two spike-based learning
methods: one of which relies on an instantaneous error signal to modify
synaptic weights in a network (INST rule), and the other one on a filtered
error signal for smoother synaptic weight modifications (FILT rule). We test
the accuracy of the solutions provided by each rule with respect to their
temporal encoding precision, and then measure the maximum number of input
patterns they can learn to memorise using the precise timings of individual
spikes as an indication of their storage capacity. Our results demonstrate the
high performance of FILT in most cases, underpinned by the rule's
error-filtering mechanism, which is predicted to provide smooth convergence
towards a desired solution during learning. We also find FILT to be most
efficient at performing input pattern memorisations, and most noticeably when
patterns are identified using spikes with sub-millisecond temporal precision.
In comparison with existing work, we determine the performance of FILT to be
consistent with that of the highly efficient E-learning Chronotron, but with
the distinct advantage that FILT is also implementable as an online method for
increased biological realism.Comment: 26 pages, 10 figures, this version is published in PLoS ONE and
incorporates reviewer comment
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.Comment: 17 pages, 8 figure
How strong is the Second Harmonic Generation in single-layer monochalcogenides? A response from first-principles real-time simulations
Second Harmonic Generation (SHG) of single-layer monochalcogenides, such as
GaSe and InSe, has been recently reported [2D Mater. 5 (2018) 025019; J. Am.
Chem. Soc. 2015, 137, 79947997] to be extremely strong with respect to bulk and
multilayer forms. To clarify the origin of this strong SHG signal, we perform
first-principles real-time simulations of linear and non-linear optical
properties of these two-dimensional semiconducting materials. The simulations,
based on ab-initio many-body theory, accurately treat the electron-hole
correlation and capture excitonic effects that are deemed important to
correctly predict the optical properties of such systems. We find indeed that,
as observed for other 2D systems, the SHG intensity is redistributed at
excitonic resonances. The obtained theoretical SHG intensity is an order of
magnitude smaller than that reported at the experimental level. This result is
in substantial agreement with previously published simulations which neglected
the electron-hole correlation, demonstrating that many-body interactions are
not at the origin of the strong SHG measured. We then show that the
experimental data can be reconciled with the theoretical prediction when a
single layer model, rather than a bulk one, is used to extract the SHG
coefficient from the experimental data.Comment: 8 pages, 4 figure
Asset pricing under uncertainty about shock propagation : [version 18 november 2013]
We analyze the equilibrium in a two-tree (sector) economy with two regimes. The output of each tree is driven by a jump-diffusion process, and a downward jump in one sector of the economy can (but need not) trigger a shift to a regime where the likelihood of future jumps is generally higher. Furthermore, the true regime is unobservable, so that the representative Epstein-Zin investor has to extract the probability of being in a certain regime from the data. These two channels help us to match the stylized facts of countercyclical and excessive return volatilities and correlations between sectors. Moreover, the model reproduces the predictability of stock returns in the data without generating consumption growth predictability. The uncertainty about the state also reduces the slope of the term structure of equity. We document that heterogeneity between the two sectors with respect to shock propagation risk can lead to highly persistent aggregate price-dividend ratios. Finally, the possibility of jumps in one sector triggering higher overall jump probabilities boosts jump risk premia while uncertainty about the regime is the reason for sizeable diffusive risk premia
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