Effects of guanidine on synaptic transmission in the spinal cord of the frog

The effects of guanidine on motoneurons of the isolated frog spinal cord were studied by adding the drug to the solution bathing the cord during intracellular recording. Guanidine (5·10–4 M) did not alter the membrane potential of motoneurons. The main effect was a marked increase of the amplitudes and frequencies of small spontaneously occurring inhibitory postsynaptic potentials. The hyperpolarizing component of postsynaptic potentials evoked by stimulation of dorsal roots was also enhanced by guanidine. Higher concentrations of guanidine (5·10–3 M) resulted in a very large and irreversible increase of the small spontaneously occurring inhibitory potentials, which now appeared in a regular, rhythmic pattern. The effects of guanidine could easily be blocked by increasing the magnesium ions (15 mM) in the bath solution. These results indicate that guanidine facilitates the release of an inhibitory transmitter in afferent terminals of the frog spinal cord either by a direct action on these terminals or indirectly by an action on nerve endings impinging on inhibitory interneurons

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions