Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Rule-based grapheme-to-phoneme method for the Greek

This paper describes a trainable method for generating letter to sound rules for the Greek language, for producing the pronunciation of out-of-vocabulary words. Several approaches have been adopted over the years for grapheme-to-phoneme conversion, such as hand-seeded rules, finite state transducers, neural networks, HMMs etc, nevertheless it has been proved that the most reliable method is a rule-based one. Our approach is based on a semi-automatically pre-transcribed lexicon, from which we derived rules for automatic transcription. The efficiency and robustness of our method are proved by experiments on out-of-vocabulary words which resulted in over than 98% accuracy on a word-base criterion.