The focus of this chapter is on neurobiologically informed and constrained models of working memory as defined by Miller, Galanter, and Pribram (1960): the holding of goals and subgoals in mind in service of planning and executing complex behaviors. In particular, the chapter focuses on models specifically addressing critical challenges and mechanisms following from the need for rapid and selective gating of working memory contents. To start, the important computational challenges posed by the tradeoff between maintaining vs. updating are discussed, providing motivation for the rest of the chapter.After that, several seminal models that have contributed to current thinking are reviewed, including the authors’ own PBWM framework that has proven influential. Finally, several recent developments from the deep learning and neurophysiology literatures are addressed and critical questions and some directions for future progress are discussed.

This chapter aims to outline current knowledge concerning the genetic background of cardiovascular disease (CVD) and its study in ancient human remains. This is demonstrated by the application of a palaeogenetic analysis to the mummy of the Tyrolean Iceman, who presented with both arterial calcifications and a strong genetic predisposition for heart disease. Further discussion highlights how the study of ancient humans can provide new insights into the genetic background of CVD and its intersection with risk factors related to lifestyle.

Following the century-old landmark work by bacteriologist and experimental pathologist Sir Marc Armand Ruffer, who demonstrated the presence of atherosclerosis during autopsies of multiple Egyptian mummies (Ruffer, 1911), an international multidisciplinary group of physicians and scientists (the Horus Team, named for the Egyptian deity; Finch, 2011.) formed to evaluate the existence, extent and aetiology of atherosclerosis in ancient peoples. The Horus Team first described atherosclerotic calcifications on computed tomography (CT) scans in 2009 (Allam et al., 2009).

The Horus and other research teams have found that atherosclerosis is not uncommon in ancient people through the study of their mummified remains (Murphy et al., 2003; Allam et al., 2009, 2011; Thompson et al., 2013, 2014). However, some have postulated that traditional hunter-gatherers are in some ways healthier than modern people and that they had very little atherosclerotic disease (O’Keefe et al., 2010).

The higher-order thalamus (e.g., the pulvinar) is widely thought to play a critical role in its interactions with the neocortex, but identifying precisely what that role is has been somewhat challenging.Here, we describe how a computational approach to understanding the nature of learning and memory in the neocortex suggests three distinct, well-defined contributions of the thalamus: (1) attention, which is perhaps the most widely discussed function of the pulvinar, is supported by a pooled inhibition dynamic involving the thalamic reticular nucleus; (2) predictive learning, where the pulvinar serves as a kind of screen on which predictions are projected, and a temporal difference between predictions and subsequent outcomes can drive error-driven learning throughout the thalamocortical system; and (3) executive function in the circuits involving the frontal cortex, where the mediodorsal (MD) thalamus is largely similar anatomically to the pulvinar and could thus support similar attentional and predictive learning functions, whereas ventral thalamic nuclei receive inhibitory modulation from the basal ganglia, supporting a gating function to regulate action based on a strong competition of Go versus No Go informed by reinforcement learning.Taken together, these important modulatory and learning contributions of the thalamus suggest that a full computational understanding of the neocortex is significantly incomplete without an integration of the thalamic circuitry.

Fundamental knowledge about the processes that control the functioning of the biophysical workings of ecosystems has expanded exponentially since the late 1960s. Scientists, then, had only primitive knowledge about C, N, P, S, and H2O cycles; plant, animal, and soil microbialinteractions and dynamics; and land, atmosphere, and water interactions. With the advent of systems ecology paradigm (SEP) and the explosion of technologies supporting field and laboratory research, scientists throughout the world were able to assemble the knowledge base known today as ecosystem science. This chapter describes, through the eyes of scientists associated with the Natural Resource Ecology Laboratory (NREL) at Colorado State University (CSU), the evolution of the SEP in discovering how biophysical systems at small scales (ecological sites, landscapes) function as systems. The NREL and CSU are epicenters of the development of ecosystem science. Later, that knowledge, including humans as components of ecosystems, has been applied to small regions, regions, and the globe. Many research results that have formed the foundation for ecosystem science and management of natural resources, terrestrial environments, and its waters are described in this chapter. Throughout are direct and implicit references to the vital collaborations with the global network of ecosystem scientists.

In 2013, India enacted one of the most robust private enforcement regimes for securities fraud violations in the world. Unlike in most other countries, Indian shareholders can now initiate securities fraud lawsuits on their own, represent all other defrauded shareholders unless those shareholders affirmatively opt out, and collect money damages for the entire class. The only thing missing is a better financing mechanism: unlike the United States, Canada, and Australia, India does not permit contingency fees, so class action lawyers cannot front the costs of litigation in exchange for collecting a percentage of what they recover. On the other hand, the 2013 law enacted a public financing regime for securities fraud class actions and it is possible third-party financing will be permitted; these mechanisms may make up some of the loss in effectiveness caused by the lack of contingency fees.