Learning and recall of form discriminations during reversible cooling deactivation of ventral-posterior suprasylvian cortex in the cat.

Extrastriate visual cortex of the ventral-posterior suprasylvian gyrus (vPS cortex) of freely behaving cats was reversibly deactivated with cooling to determine its role in performance on a battery of simple or masked two-dimensional pattern discriminations, and three-dimensional object discriminations. Deactivation of vPS cortex by cooling profoundly impaired the ability of the cats to recall the difference between all previously learned pattern and object discriminations. However, the cats' ability to learn or relearn pattern and object discriminations while vPS was deactivated depended upon the nature of the pattern or object and the cats' prior level of exposure to them. During cooling of vPS cortex, the cats could neither learn the novel object discriminations nor relearn a highly familiar masked or partially occluded pattern discrimination, although they could relearn both the highly familiar object and simple pattern discriminations. These cooling-induced deficits resemble those induced by cooling of the topologically equivalent inferotemporal cortex of monkeys and provides evidence that the equivalent regions contribute to visual processing in similar ways.

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.