An examination of difficulties accessing surgical care in Canada from 2005-2014: Results from the Canadian Community Health Survey.

BACKGROUND: Difficulties accessing surgical care (e.g., related to wait times, cancellations, cost, receiving a diagnosis) are understudied in Canada. Using population-based data, we studied difficulty accessing non-emergency surgical care, including (1) the incidence and annual changes in incidence, (2) types of difficulties, and (3) associated factors (e.g., sociodemographics, surgery characteristics). METHODS: Cross-sectional data from the Canadian Community Health Survey annual components were analyzed from 2005-2014. Weighted frequencies established the annual incidence of difficulty accessing surgical care, and total incidence of types of difficulties. Chi-square analyses, independent samples t-tests, and a multivariable logistic regression examined sociodemographic and surgery-related characteristics associated with difficulty accessing surgical care. RESULTS: Among individuals who required past-year non-emergency surgery between 2005-2014 (weighted n = 3,052,072), 15.6% experienced difficulty accessing surgical care. The most common difficulty was "waited too long for surgery" (58.5%). There were significant differences in the incidence of difficulty according to year (Χ2 = 83.50, p < .001) from 2005-2014. The incidence of difficulty accessing surgery varied according to sex (Χ2 = 4.02, p < .05), surgery type (Χ2 = 96.09, p < .001), party responsible for cancellation/postponement (Χ2 range: 4.36-19.01, p < .05), and waiting time (t = 10.59, p < .001). In particular, males, orthopedic surgery, and surgery cancelled by the surgeon or hospital had the highest rates of difficulty. CONCLUSION: Results provide insight into the difficulties experienced by patients accessing elective surgery, and the associated factors. These results may inform targeted healthcare interventions and resource reallocation to reduce these occurrences.

Disruptive behaviour in the operating room is under-reported: an international survey.

PURPOSE: The purpose of this study was to investigate the reporting habits of clinicians who have been exposed to disruptive behaviour in the operating room (OR) and assess their satisfaction with management's responses to this issue. METHODS: Ethics committee approval was obtained. This was a pre-specified sub-study of a larger survey examining disruptive behaviour, which was distributed to OR clinicians in seven countries. Using Likert-style questions, this study ascertained the proportion of disruptive intraoperative behaviour that clinicians reported to management, as well as their degree of satisfaction with management's responses. Binomial logistic regression identified socio-demographic, exposure-related, and behavioural predictors that a clinician would never report disruptive behaviour. RESULTS: Four thousand, seven hundred and seventy-five respondents were part of the sub-study. Disruptive behaviour was under-reported by 96.5% (95% confidence interval [CI], 95.9 to 97.0) of respondents, and never reported by 30.9% (95% CI, 29.6 to 32.2) of respondents. Only 21.0% (95% CI, 19.8 to 22.2) of respondents expressed satisfaction with management's responses. Numerous socio-demographic, exposure-related, and behavioural predictors of reporting habits were identified. Socio-demographic groups who had higher odds of never reporting disruptive behaviour included younger clinicians, clinicians without management responsibilities, both anesthesiologists and surgeons (compared with nurses), biological females, and heterosexuals (all P < 0.05). CONCLUSIONS: Disruptive behaviour was under-reported by nearly all clinicians surveyed, and only one in five were satisfied with management's responses. For healthcare systems to meaningfully address the issue of disruptive behaviour, management must create reporting systems that clinicians will use. They must also respond in ways that clinicians can rely on to affect necessary change.

Synchrony measure for a neuron driven by excitatory and inhibitory inputs and its adaptation to experimentally-recorded data.

The aim of the current work is twofold: firstly to adapt an existing method measuring the input synchrony of a neuron driven only by excitatory inputs in such a way so as to account for inhibitory inputs as well and secondly to further appropriately adapt this measure so as to be correctly utilised on experimentally-recorded data. The existing method uses the normalized pre-spike slope (NPSS) of the membrane potential, resulting from observing the slope of depolarization of the membrane potential of a neuron prior to the moment of crossing the threshold within a short period of time, to identify the response-relevant input synchrony and through it to infer the operational mode of a neuron. The first adaptation of NPSS is made such that its upper bound calculation accommodates for the higher possible slope values caused by the lower average and minimum membrane potential values due to inhibitory inputs. Results indicate that when the input spike trains arrive randomly, the modified NPSS works as expected inferring that the neuron is operating as a temporal integrator. When the input spike trains arrive in perfect synchrony though, the modified NPSS works as expected only when the level of inhibition is much higher than the level of excitation. This suggests that calculation of the upper bound of the NPSS should be a function of the ratio between excitatory and inhibitory inputs in order to be able to correctly capture perfect synchrony at a neuron's input. In addition, we effectively demonstrate a process which has to be followed when aiming to use the NPSS on real neuron recordings. This process, which relies on empirical observations of the slope of depolarisation for estimating the bounds for the range of observed interspike interval lengths, is successfully applied to experimentally-recorded data showing that through it both a real neuron's operational mode and the amount of input synchrony that caused its firing can be inferred.

Integrator or Coincidence Detector: A Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode.

In this letter, we propose a definition of the operational mode of a neuron, that is, whether a neuron integrates over its input or detects coincidences. We complete the range of possible operational modes by a new mode we call gap detection, which means that a neuron responds to gaps in its stimulus. We propose a measure consisting of two scalar values, both ranging from -1 to +1: the neural drive, which indicates whether its stimulus excites the neuron, serves as background noise, or inhibits it; the neural mode, which indicates whether the neuron's response is the result of integration over its input, of coincidence detection, or of gap detection; with all three modes possible for all neural drive values. This is a pure spike-based measure and can be applied to measure the influence of either all or subset of a neuron's stimulus. We derive the measure by decomposing the reverse correlation, test it in several artificial and biological settings, and compare it to other measures, finding little or no correlation between them. We relate the results of the measure to neural parameters and investigate the effect of time delay during spike generation. Our results suggest that a neuron can use several different modes simultaneously on different subsets of its stimulus to enable it to respond to its stimulus in a complex manner.

Integrator or coincidence detector --- what shapes the relation of stimulus synchrony and the operational mode of a neuron?

The operational mode of a neuron (i.e., whether a neuron is an integrator or a coincidence detector) is in part determined by the degree of synchrony in the firing of its pre-synaptic neural population. More specifically, it is determined by the degree of synchrony that causes the neuron to fire. In this paper, we investigate the relationship between the input and the operational mode. We compare the response-relevant input synchrony, which measures the operational mode and can be determined using a membrane potential slope-based measure [7], with the spike time distance of the spike trains driving the neuron, which measures spike train synchrony and can be determined using the multivariate SPIKE-distance metric [10]. We discover that the relationship between the two measures changes substantially based on the values of the parameters of the input (firing rate and number of spike trains) and the parameters of the post-synaptic neuron (synaptic weight, membrane leak time constant and spike threshold). More importantly, we determine how the parameters interact to shape the synchrony-operational mode relationship. Our results indicate that the amount of depolarisation caused by a highly synchronous volley of input spikes, is the most influential factor in defining the relationship between input synchrony and operational mode. This is defined by the number of input spikes and the membrane potential depolarisation caused per spike, compared to the spike threshold.

Behavioral plasticity through the modulation of switch neurons.

A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mechanisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call "switch neuron". A switch neuron regulates the flow of information in NNs by selectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some information about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate "switch modules" in order to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required.

On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron.

Biological systems are able to recognise temporal sequences of stimuli or compute in the temporal domain. In this paper we are exploring whether a biophysical model of a pyramidal neuron can detect and learn systematic time delays between the spikes from different input neurons. In particular, we investigate whether it is possible to reinforce pairs of synapses separated by a dendritic propagation time delay corresponding to the arrival time difference of two spikes from two different input neurons. We examine two subthreshold learning approaches where the first relies on the backpropagation of EPSPs (excitatory postsynaptic potentials) and the second on the backpropagation of a somatic action potential, whose production is supported by a learning-enabling background current. The first approach does not provide a learning signal that sufficiently differentiates between synapses at different locations, while in the second approach, somatic spikes do not provide a reliable signal distinguishing arrival time differences of the order of the dendritic propagation time. It appears that the firing of pyramidal neurons shows little sensitivity to heterosynaptic spike arrival time differences of several milliseconds. This neuron is therefore unlikely to be able to learn to detect such differences.

Toward nonlinear local reinforcement learning rules through neuroevolution.

We consider the problem of designing local reinforcement learning rules for artificial neural network (ANN) controllers. Motivated by the universal approximation properties of ANNs, we adopt an ANN representation for the learning rules, which are optimized using evolutionary algorithms. We evaluate the ANN rules in partially observable versions of four tasks: the mountain car, the acrobot, the cart pole balancing, and the nonstationary mountain car. For testing whether such evolved ANN-based learning rules perform satisfactorily, we compare their performance with the performance of SARSA(λ) with tile coding, when the latter is provided with either full or partial state information. The comparison shows that the evolved rules perform much better than SARSA(λ) with partial state information and are comparable to the one with full state information, while in the case of the nonstationary environment, the evolved rule is much more adaptive. It is therefore clear that the proposed approach can be particularly effective in both partially observable and nonstationary environments. Moreover, it could potentially be utilized toward creating more general rules that can be applied in multiple domains and transfer learning scenarios.

Unanticipated intra-operative finding of pulmonary artery tumour thromboembolism during radical nephrectomy and caval thrombectomy: Case report and management.

We report a case of an unanticipated intra-operative transesophageal echocardiography (TEE) finding of pulmonary artery thromboembolism in a 72-year-old woman being prepared for radical nephrectomy and caval thrombectomy. Upon intra-operative TEE to evaluate the extent of caval thrombus, we found a pulmonary artery tumour thromboembolism in an otherwise asymptomatic patient after induction and prior to surgery. A chest computed tomography confirmed a large saddle tumour thromboembolus. A multidisciplinary approach was used to facilitate radical nephrectomy with caval thrombectomy and pulmonary artery thromboembolectomy. This case shows the importance of adequate perioperative imaging and use of intra-operative TEE to evaluate the extent of disease. To our knowledge, we are the first to present a case of RCC with cava tumour thrombus in which the pulmonary artery tumour thromboembolism was detected incidentally on intraoperative TEE.

Measuring input synchrony in the Ornstein-Uhlenbeck neuronal model through input parameter estimation.

We present a method of estimating the input parameters and through them, the input synchrony, of a stochastic leaky integrate-and-fire neuronal model based on the Ornstein-Uhlenbeck process when it is driven by time-dependent sinusoidal input signal and noise. By driving the neuron using sinusoidal inputs, we simulate the effects of periodic synchrony on the membrane voltage and the firing of the neuron, where the peaks of the sine wave represent volleys of synchronised input spikes. Our estimation methods allow us to measure the degree of synchrony driving the neuron in terms of the input sine wave parameters, using the output spikes of the model and the membrane potential. In particular, by estimating the frequency of the synchronous input volleys and averaging the estimates of the level of input activity at corresponding intervals of the input signal, we obtain fairly accurate estimates of the baseline and peak activity of the input, which in turn define the degrees of synchrony. The same procedure is also successfully applied in estimating the baseline and peak activity of the noise. This article is part of a Special Issue entitled Neural Coding 2012.

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition.

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synaptic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of short-term synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level.

Distinguishing the causes of firing with the membrane potential slope.

In this letter, we aim to measure the relative contribution of coincidence detection and temporal integration to the firing of spikes of a simple neuron model. To this end, we develop a method to infer the degree of synchrony in an ensemble of neurons whose firing drives a single postsynaptic cell. This is accomplished by studying the effects of synchronous inputs on the membrane potential slope of the neuron and estimating the degree of response-relevant input synchrony, which determines the neuron's operational mode. The measure is calculated using the normalized slope of the membrane potential prior to the spikes fired by a neuron, and we demonstrate that it is able to distinguish between the two operational modes. By applying this measure to the membrane potential time course of a leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular firing at high rates, we show that the partial reset model operates as a temporal integrator of incoming excitatory postsynaptic potentials and that coincidence detection is not necessary for producing such high irregular firing.

A comparative study on filtering protein secondary structure prediction.

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

Learning optimisation by high firing irregularity.

In a network of leaky integrate-and-fire (LIF) neurons, we investigate the functional role of irregular spiking at high rates. Irregular spiking is produced by either employing the partial somatic reset mechanism on every LIF neuron of the network or by using temporally correlated inputs. In both the benchmark problem of XOR (exclusive-OR) and in a general-sum game, it is shown that irrespective of the mechanism that is used to produce it, high firing irregularity enhances the learning capability of the spiking neural network trained with reward-modulated spike-timing-dependent plasticity. These results suggest that the brain may be utilising high firing irregularity for the purposes of learning optimisation.

Multiagent reinforcement learning: spiking and nonspiking agents in the iterated Prisoner's Dilemma.

This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and nonspiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. The spiking agents are neural networks with leaky integrate-and-fire neurons trained with two different learning algorithms: 1) reinforcement of stochastic synaptic transmission, or 2) reward-modulated spike-timing-dependent plasticity with eligibility trace. The nonspiking agents use a tabular representation and are trained with Q- and SARSA learning algorithms, with a novel reward transformation process also being applied to the Q-learning agents. According to the results, the cooperative outcome is enhanced by: 1) transformed internal reinforcement signals and a combination of a high learning rate and a low discount factor with an appropriate exploration schedule in the case of non-spiking agents, and 2) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and nonspiking agents have similar behavior and therefore they can equally well be used in a multiagent interaction setting. For training the spiking agents in the case where more than one output neuron competes for reinforcement, a novel and necessary modification that enhances competition is applied to the two learning algorithms utilized, in order to avoid a possible synaptic saturation. This is done by administering to the networks additional global reinforcement signals for every spike of the output neurons that were not "responsible" for the preceding decision.