A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground and Background in an Auditory Scene

Bottom-up (stimulus-driven) and top-down (attentional) processes interact when a complex acoustic scene is parsed. Both modulate the neural representation of the target in a manner strongly correlated with behavioral performance

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available

Telemedicine interventions in type 2 diabetes management: a protocol for systematic review and network meta-analysis

Introduction The consequences of type 2 diabetes mellitus (T2DM) heavily strain individuals and healthcare systems worldwide. Interventions via telemedicine have become a potential tactic to tackle the difficulties in effectively managing T2DM. However, more research is needed to determine how telemedicine interventions affect T2DM management. This study sets out to systematically analyse and report the effects of telemedicine treatments on T2DM management to gain essential insights into the potential of telemedicine as a cutting-edge strategy to improve the outcomes and care delivery for people with T2DM.Methods and analysis To uncover relevant research, we will perform a comprehensive literature search across six databases (PubMed, IEEE, EMBASE, Web of Science, Google Scholar and Cochrane Library). Each piece of data will be extracted separately, and any discrepancies will be worked out through discussion or by a third reviewer. The studies included are randomised controlled trial. We chose by predefined inclusion standards. After the telemedicine intervention, glycated haemoglobin will be the primary outcome. The Cochrane risk-of-bias approach will be used to evaluate the quality of the included studies. RevMan V.5.3.5 software and RStiduo V.4.3.1 software can be used to analyse the data, including publication bias.Ethics and dissemination Since this research will employ publicly accessible documents, ethical approval is unnecessary. The review is registered prospectively on the PROSPERO database. The study’s findings will be published in a peer-reviewed journal.PROSPERO registration number CRD42023421719