Dynamics of Action Potential Initiation in the GABAergic Thalamic Reticular Nucleus In Vivo

Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold

Video-calls to reduce loneliness and social isolation within care environments for older people: an implementation study using collaborative action research

Background  Older people in care may be lonely with insufficient contact if families are unable to visit. Face-to-face contact through video-calls may help reduce loneliness, but little is known about the processes of engaging people in care environments in using video-calls. We aimed to identify the barriers to and facilitators of implementing video-calls for older people in care environments.  Methods  A collaborative action research (CAR) approach was taken to implement a video-call intervention in care environments. We undertook five steps of recruitment, planning, implementation, reflection and re-evaluation, in seven care homes and one hospital in the UK. The video-call intervention ‘Skype on Wheels’ (SoW) comprised a wheeled device that could hold an iPad and handset, and used Skype to provide a free video-call service. Care staff were collaborators who implemented the intervention within the care-setting by agreeing the intervention, recruiting older people and their family, and setting up video-calls. Field notes and reflective diaries on observations and conversations with staff, older people and family were maintained over 15 months, and analysed using thematic analysis.  Results  Four care homes implemented the intervention. Eight older people with their respective social contacts made use of video-calls. Older people were able to use SoW with assistance from staff, and enjoyed the use of video-calls to stay better connected with family. However five barriers towards implementation included staff turnover, risk averseness, the SoW design, lack of family commitment and staff attitudes regarding technology.  Conclusions  The SoW intervention, or something similar, could aid older people to stay better connected with their families in care environments, but if implemented as part of a rigorous evaluation, then co-production of the intervention at each recruitment site may be needed to overcome barriers and maximise engagement

devitocodes/devito: v4.2.3

Synopsis Performance optimizations in the symbolic layer and generated code for x86, GPU and MPI. Various minor correctness and performance bug fixes. Improvements to application developer API. Added new tutorial notebooks. Increased test coverage - particularly for MPI and GPU's.Synopsis Performance optimizations in the symbolic layer and generated code for x86, GPU and MPI. Various minor correctness and performance bug fixes. Improvements to application developer API. Added new tutorial notebooks. Increased test coverage - particularly for MPI and GPU's.4.2.

opesci/devito: Devito-4.0

Tensor algebra support (#873): VectorFunction and VectorTimeFunction (2nd order) TensorFunction and TensorTimeFunction Full support for FD and related operations (derivatives, shortcuts, solve, ...) Differential operators such as div, grad and curl FD extensions: Custom FD with user-supplied coefficients as Function (#964) Extended and more rigorous support for staggered grids (#873): True half-grid staggering (u(x + h_x/2)) Automatic evaluation at half-nodes (averaging only) Automatic staggered FD of any orderTensor algebra support (#873): VectorFunction and VectorTimeFunction (2nd order) TensorFunction and TensorTimeFunction Full support for FD and related operations (derivatives, shortcuts, solve, ...) Differential operators such as div, grad and curl FD extensions: Custom FD with user-supplied coefficients as Function (#964) Extended and more rigorous support for staggered grids (#873): True half-grid staggering (u(x + h_x/2)) Automatic evaluation at half-nodes (averaging only) Automatic staggered FD of any order4.