Contract Administration for Construction Professionals

       Coggins, J., Schwarz, S., Davies, M., and Ma, T.       Contract Administration for Construction Professionals.       LexisNexis Australia at www.lexis.com.au, 2022.       ISBN/ISSN 978-0-4093-5083-8 (Paperback, 588 pp) AUD 95, E-book, ISBN/ISSN 978-0- 9043-5084-

Computational models in the age of large datasets.

Technological advances in experimental neuroscience are generating vast quantities of data, from the dynamics of single molecules to the structure and activity patterns of large networks of neurons. How do we make sense of these voluminous, complex, disparate and often incomplete data? How do we find general principles in the morass of detail? Computational models are invaluable and necessary in this task and yield insights that cannot otherwise be obtained. However, building and interpreting good computational models is a substantial challenge, especially so in the era of large datasets. Fitting detailed models to experimental data is difficult and often requires onerous assumptions, while more loosely constrained conceptual models that explore broad hypotheses and principles can yield more useful insights.Charles A King TrustThis is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.conb.2015.01.00

New Books “By” Foucault

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Homeostatic Regulation of Intrinsic Excitability in Hippocampal Neurons

Informatics Life-Sciences InstituteThe proper functioning of nervous systems requires electrical activity to be tightly regulated. Perturbations in the intrinsic properties of neurons, and in excitatory input, are imposed throughout nervous system development as cell morphology and network activity evolve. In mature nervous systems these changes continue as a result of synaptic plasticity and external stimuli. It is therefore likely that homeostatic mechanisms exist to regulate membrane conductances that determine the excitability of individual neurons, and several mechanisms have been characterised to date. This thesis characterises a novel in vitro model for homeostatic control of intrinsic excitability. The principal finding is that cultured hippocampal neurons respond to chronic depolarisation over a period of days by attenuating their response to injected current. This effect was found to depend on the level of depolarisation and the length of treatment, and is accompanied by changes in both active and passive membrane conductances. In addition, the effect is reversible and dependent on L-type calcium channel activity. Using experimental data to parameterise a conductance-based computer model suggests that the changes in conductance properties account for the observed differences in excitability

Causes and consequences of representational drift.

The nervous system learns new associations while maintaining memories over long periods, exhibiting a balance between flexibility and stability. Recent experiments reveal that neuronal representations of learned sensorimotor tasks continually change over days and weeks, even after animals have achieved expert behavioral performance. How is learned information stored to allow consistent behavior despite ongoing changes in neuronal activity? What functions could ongoing reconfiguration serve? We highlight recent experimental evidence for such representational drift in sensorimotor systems, and discuss how this fits into a framework of distributed population codes. We identify recent theoretical work that suggests computational roles for drift and argue that the recurrent and distributed nature of sensorimotor representations permits drift while limiting disruptive effects. We propose that representational drift may create error signals between interconnected brain regions that can be used to keep neural codes consistent in the presence of continual change. These concepts suggest experimental and theoretical approaches to studying both learning and maintenance of distributed and adaptive population codes.This work is supported by the Human Frontier Science Program, ERC grant StG 716643 FLEXNEURO, and NIH grants (NS108410, NS089521, MH107620)