Abstract: The basolateral amygdala (BLA) has a crucial role in emotional learning irrespective of valence1, 2, 3, 4, 5, 21, 22, 23. The BLA projection to the nucleus accumbens (NAc) is thought to modulate cue-triggered motivated behaviours4, 6, 7, 24, 25, but our understanding of the interaction between these two brain regions has been limited by the inability to manipulate neural-circuit elements of this pathway selectively during behaviour. To circumvent this limitation, we used in vivo optogenetic stimulation or inhibition of glutamatergic fibres from the BLA to the NAc, coupled with intracranial pharmacology and ex vivo electrophysiology. Here we show that optical stimulation of the pathway from the BLA to the NAc in mice reinforces behavioural responding to earn additional optical stimulation of these synaptic inputs. Optical stimulation of these glutamatergic fibres required intra-NAc dopamine D1-type receptor signalling, but not D2-type receptor signalling. Brief optical inhibition of fibres from the BLA to the NAc reduced cue-evoked intake of sucrose, demonstrating an important role of this specific pathway in controlling naturally occurring reward-related behaviour. Moreover, although optical stimulation of glutamatergic fibres from the medial prefrontal cortex to the NAc also elicited reliable excitatory synaptic responses, optical self-stimulation behaviour was not observed by activation of this pathway. These data indicate that whereas the BLA is important for processing both positive and negative affect, the glutamatergic pathway from the BLA to the NAc, in conjunction with dopamine signalling in the NAc, promotes motivated behavioural responding. Thus, optogenetic manipulation of anatomically distinct synaptic inputs to the NAc reveals functionally distinct properties of these inputs in controlling reward-seeking behaviours.

Abstract: A basic principle of human memory is that lists that can be organized into memorable 'chunks' are easier to remember. Memory span is limited to a roughly constant number of chunks and is to a large extent independent of the amount of informaton contained in each chunk. Depending on the ingenuity of the code used to integrate discrete items into chunks, one can substantially increase the number of items that can be recalled correctly. Newly developed paradigms for studying memory in non-verbal organisms allow comparison of the abilities of human and non-human subjects to memorize lists. Here I present two types of evidence that pigeons 'chunk' 5-element lists whose components (colours and achromatic geometric forms) are clustered into distinct groups. Those lists were learned twice as rapidly as a homogeneous list of colours or heterogeneous lists in which the elements are not clustered. The pigeons were also tested for knowledge of the order of two elements drawn from the 5-element lists. They responded in the correct order only to those subsets that contained a chunk boundary. Thus chunking can be studied profitably in animal subjects; the cognitive processes that allow an organism to form chunks do no presuppose linguistic competence.

Abstract: Networks of coupled dynamical systems have been used to model biological oscillators Josephson junction arrays excitable media, neural networks spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.