Disfacilitation and active inhibition in the neocortex during the natural sleep-wake cycle: An intracellular study

Earlier extracellular recordings during natural sleep have shown that, during slow-wave sleep (SWS), neocortical neurons display long-lasting periods of silence, whereas they are tonically active and discharge at higher rates during waking and sleep with rapid eye movements (REMs). We analyzed the nature of long-lasting periods of neuronal silence in SWS and the changes in firing rates related to ocular movements during REM sleep and waking using intracellular recordings from electrophysiologically identified neocortical neurons in nonanesthetized and nonparalyzed cats. We found that the silent periods during SWS are associated with neuronal hyperpolarizations, which are due to a mixture of K+ currents and disfacilitation processes. Conventional fast-spiking neurons (presumably local inhibitory interneurons) increased their firing rates during REMs and eye movements in waking. During REMs, the firing rates of regular-spiking neurons from associative areas decreased and intracellular traces revealed numerous, short-lasting, low-amplitude inhibitory postsynaptic potentials (IPSPs), that were reversed after intracellular chloride infusion. In awake cats, regular-spiking neurons could either increase or decrease their firing rates during eye movements. The short-lasting IPSPs associated with eye movements were still present in waking; they preceded the spikes and affected their timing. We propose that there are two different forms of firing rate control: disfacilitation induces long-lasting periods of silence that occur spontaneously during SWS, whereas active inhibition, consisting of low-amplitude, short-lasting IPSPs, is prevalent during REMs and precisely controls the timing of action potentials in waking.

Models of natural language understanding.

This paper surveys some of the fundamental problems in natural language (NL) understanding (syntax, semantics, pragmatics, and discourse) and the current approaches to solving them. Some recent developments in NL processing include increased emphasis on corpus-based rather than example- or intuition-based work, attempts to measure the coverage and effectiveness of NL systems, dealing with discourse and dialogue phenomena, and attempts to use both analytic and stochastic knowledge. Critical areas for the future include grammars that are appropriate to processing large amounts of real language; automatic (or at least semi-automatic) methods for deriving models of syntax, semantics, and pragmatics; self-adapting systems; and integration with speech processing. Of particular importance are techniques that can be tuned to such requirements as full versus partial understanding and spoken language versus text. Portability (the ease with which one can configure an NL system for a particular application) is one of the largest barriers to application of this technology.

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.