In NTVA,filtering (selection of objects) changes the number of cortical neurons in which an object is represented so that this number increases with the behavioural importance of the object. Another mechanism of selection, pigeonholing (selection of features), scales the level of activation in neurons coding for a particular feature. By these mechanisms, behaviourally important objects and features are likely to win the competition to become encoded into visual short-term memory (VSTM). The VSTM system is conceived as a feedback mechanism that sustains activity BAY 63-2521 in the neurons that have won the

attentional competition. NTVA accounts both for a wide range of attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates of single cells in the primate visual system. (C) 2010 Elsevier Ltd. All rights reserved.”
“We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the interplay between evolvability and robustness of model gene regulatory networks with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006) we find that networks

with SFin topologies, that is SF topology for incoming nodes and ER topology for outgoing nodes, are GW4064 mw significantly more evolvable towards specific oscillatory targets than networks with ER topology for both incoming and outgoing nodes. Similar results are found for networks with SFboth and SFout topologies. The functionality of the SFout topology, which most closely resembles the structure of biological gene networks (Babu et al., 2004), is compared to the ER topology in further detail through an extension to multiple target outputs, with either an oscillatory or a non-oscillatory nature. For multiple oscillatory targets of the same length, the differences between SFout and ER networks are enhanced, but for non-oscillatory targets both types of networks show fairly similar evolvability. We find that SF networks generate oscillations much more mafosfamide easily than ER networks do, and this may explain why SF networks are more evolvable

than ER networks are for oscillatory phenotypes. In spite of their greater evolvability, we find that networks with SFout topologies are also more robust to mutations (mutational robustness) than ER networks. Furthermore, the SFout topologies are more robust to changes in initial conditions (environmental robustness). For both topologies, we find that once a population of networks has reached the target state, further neutral evolution can lead to an increase in both the mutational robustness and the environmental robustness to changes in initial conditions. (C) 2010 Elsevier Ltd. All rights reserved.”
“Visual short-term memory (VSTM) is limited in capacity. Therefore, it is important to encode only visual information that is most likely to be relevant to behaviour.