Foot shock facilitates reward seeking in an experience-dependent manner.

Animals organize reward seeking around aversive events. An abundance of research shows that foot shock, as well as a shock-associated cue, can elicit freezing and suppress reward seeking. Yet, there is evidence that experience can flip the effect of foot shock to facilitate reward seeking. Here we examined cue suppression, foot shock suppression and foot shock facilitation of reward seeking in a single behavioural setting. Male Long Evans rats received fear discrimination consisting of danger, uncertainty, and safety cues. Discrimination took place over a baseline of rewarded nose poking. With limited experience (1-2 sessions), all cues and foot shock suppressed reward seeking. With continued experience (10-16 sessions), suppression became specific to shock-associated cues, foot shock briefly suppressed, then facilitated reward seeking. Our results provide a means of assessing positive properties of foot shock, and may provide insight into maladaptive behaviour around aversive events.

Scaling of differentiation in networks: nervous systems, organisms, ant colonies, ecosystems, businesses, universities, cities, electronic circuits, and Legos.

Nodes in networks are often of different types, and in this sense networks are differentiated. Here we examine the relationship between network differentiation and network size in networks under economic or natural selective pressure, such as electronic circuits (networks of electronic components), Legos (networks of Lego pieces), businesses (networks of employees), universities (networks of faculty), organisms (networks of cells), ant colonies (networks of ants), and nervous systems (networks of neurons). For each of these we find that (i) differentiation increases with network size, and (ii) the relationship is consistent with a power law. These results are explained by a hypothesis that, because nodes are costly to build and maintain in such "selected networks", network size is optimized, and from this the power-law relationship may be derived. The scaling exponent depends on the particular kind of network, and is determined by the degree to which nodes are used in a combinatorial fashion to carry out network-level functions. We find that networks under natural selection (organisms, ant colonies, and nervous systems) have much higher combinatorial abilities than the networks for which human ingenuity is involved (electronic circuits, Legos, businesses, and universities). A distinct but related optimization hypothesis may be used to explain scaling of differentiation in competitive networks (networks where the nodes themselves, rather than the entire network, are under selective pressure) such as ecosystems (networks of organisms).