5,181 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
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is
widespread in animal species. Sleep is known to be beneficial to cognitive and
mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the
importance of the phenomenon, a complete understanding of its functions and
underlying mechanisms is still lacking. In this paper, we show interesting
effects of deep-sleep-like slow oscillation activity on a simplified
thalamo-cortical model which is trained to encode, retrieve and classify images
of handwritten digits. During slow oscillations,
spike-timing-dependent-plasticity (STDP) produces a differential homeostatic
process. It is characterized by both a specific unsupervised enhancement of
connections among groups of neurons associated to instances of the same class
(digit) and a simultaneous down-regulation of stronger synapses created by the
training. This hierarchical organization of post-sleep internal representations
favours higher performances in retrieval and classification tasks. The
mechanism is based on the interaction between top-down cortico-thalamic
predictions and bottom-up thalamo-cortical projections during deep-sleep-like
slow oscillations. Indeed, when learned patterns are replayed during sleep,
cortico-thalamo-cortical connections favour the activation of other neurons
coding for similar thalamic inputs, promoting their association. Such mechanism
hints at possible applications to artificial learning systems.Comment: 11 pages, 5 figures, v5 is the final version published on Scientific
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Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking
This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications
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