Motor learning by field approximation.

We investigated how human subjects adapt to forces perturbing the motion of their ams. We found that this kind of learning is based on the capacity of the central nervous system (CNS) to predict and therefore to cancel externally applied perturbing forces. Our experimental results indicate: (i) that the ability of the CNS to compensate for the perturbing forces is restricted to those spatial locations where the perturbations have been experienced by the moving arm. The subjects also are able to compensate for forces experienced at neighboring workspace locations. However, adaptation decays smoothly and quickly with distance from the locations where disturbances had been sensed by the moving limb. (ii) Our experiments also how that the CNS builds an internal model of the external perturbing forces in intrinsic (muscles and / or joints) coordinates.

Oxytocin releases atrial natriuretic peptide by combining with oxytocin receptors in the heart

Previous studies indicated that the central nervous system induces release of the cardiac hormone atrial natriuretic peptide (ANP) by release of oxytocin from the neurohypophysis. The presence of specific transcripts for the oxytocin receptor was demonstrated in all chambers of the heart by amplification of cDNA by the PCR using specific oligonucleotide primers. Oxytocin receptor mRNA content in the heart is 10 times lower than in the uterus of female rats. Oxytocin receptor transcripts were demonstrated by in situ hybridization in atrial and ventricular sections and confirmed by competitive binding assay using frozen heart sections. Perfusion of female rat hearts for 25 min with Krebs–Henseleit buffer resulted in nearly constant release of ANP. Addition of oxytocin (10−6 M) significantly stimulated ANP release, and an oxytocin receptor antagonist (10−7 and 10−6 M) caused dose-related inhibition of oxytocin-induced ANP release and in the last few minutes of perfusion decreased ANP release below that in control hearts, suggesting that intracardiac oxytocin stimulates ANP release. In contrast, brain natriuretic peptide release was unaltered by oxytocin. During perfusion, heart rate decreased gradually and it was further decreased significantly by oxytocin (10−6 M). This decrease was totally reversed by the oxytocin antagonist (10−6 M) indicating that oxytocin released ANP that directly slowed the heart, probably by release of cyclic GMP. The results indicate that oxytocin receptors mediate the action of oxytocin to release ANP, which slows the heart and reduces its force of contraction to produce a rapid reduction in circulating blood volume.

Formation of temporal-feature maps by axonal propagation of synaptic learning

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 μs. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.