Möbius-strip-like columnar functional connections are revealed in somato-sensory receptive field centroids

Receptive fields of neurons in the forelimb region of areas 3b and 1 of primary somatosensory cortex, in cats and monkeys, were mapped using extracellular recordings obtained sequentially from nearly radial penetrations. Locations of the field centroids indicated the presence of a functional system, in which cortical homotypic representations of the limb surfaces are entwined in three-dimensional Mobius-strip-like patterns of synaptic connections. Boundaries of somatosensory receptive field in nested groups irregularly overlie the centroid order, and are interpreted as arising from the superposition of learned connections upon the embryonic order. Since the theory of embryonic synaptic self-organisation used to model these results was devised and earlier used to explain findings in primary visual cortex, the present findings suggest the theory may be of general application throughout cortex, and may reveal a modular functional synaptic system, which, only in some parts of the cortex, and in some species, is manifest as anatomical ordering into columns

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

Complex methods of inquiry: structuring uncertainty

Organizational problem spaces can be viewed as complex, uncertain and ambiguous. They can also be understood as open problem spaces. As such, any engagement with them, and any effort to intervene in order to pursue desirable change, cannot be assumed to be just a matter of ‘complicatedness’. The issue is not just a need to cope with dynamics of system. It is also the perceptual ‘boundedness’ of multitudes of assumptions about scope of whole and limitations of organization as system. Furthermore, explicit attention to complexities of feedback loops is an extremely important aspect of any systemic discussion. How can we help teams of competent professionals to engage purposefully with such uncertain and ambiguous problem domains? The author suggests that we can only address this effectively through pragmatic efforts to incorporate a multitude of boundary-setting assumptions, explored as part of active (self-) reflection and practical engagement. This must be undertaken without resorting to an overly simplistic application of convergent thinking in our efforts to support problem solving. Instead, we need to pursue divergent thinking and ‘complexification’ in our effort to support problem resolving. The main contribution of this thesis is to present a collection of principles that taken together, provide support for this engagement ntervention. A core feature of this result is the framework for Strategic Systemic Thinking, which includes examples of pragmatically useful methods and tools

An investigation into tacit knowledge management at the supervisory level.

An investigation into tacit knowledge management at the supervisory level Objective: The purpose of this study was to investigate how supervisors managed tacit knowledge. Aims: The aims were to understand what tacit knowledge looked like on the shop floor, to understand "experience‟ in terms of tacit knowledge, and to describe the methods and techniques that supervisors used to manage this elusive resource as they went about the task of achieving organisational goals. Method: Qualitative data was collected using a novel iterative participant observation method, where the researcher-as-instrument was embedded as a novice (but privileged) employee for extended periods in four different case study sites. Over the course of the study, the researcher took on the role of laboratory technician, electrical engineer, manufacturing process worker, and aircraft maintenance engineer. A grounded theory approach was taken to the analysis of the various field notes, photographs, video, audio, and found objects. The methodology was augmented with specialist qualitative research software to manage the data. Results: It was found that supervisors' tacit knowledge management activities can be classified according to formal and informal behaviours that correspond with Nonaka and Takeuchi's SECI knowledge life cycle. It was also found that a worker's task related tacit knowledge has seven aspects in five levels of competency, and their experience can be described in terms of 10 categories of tacit knowledge working capital. Insights attributed to the novel method of data collection produced an unexpected finding – the Home Guard model, which describes how the value of an individual's knowledge sharing activities is related to their power distance and self-confidence. Conclusions: The findings provide empirical support for existing knowledge management theory, identify specific supervisory behaviours that support tacit knowledge management on the shop floor, and extend the knowledge management discourse with new theories about knowledge sharing behaviours that have direct application to the supervisory role

Attention is more than prediction precision [Commentary on target article]

A cornerstone of the target article is that, in a predictive coding framework, attention can be modelled by weighting prediction error with a measure of precision. We argue that this is not a complete explanation, especially in the light of ERP (event-related potentials) data showing large evoked responses for frequently presented target stimuli, which thus are predicted