Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance

The subthresholdmembranevoltage of a neuron in active cortical tissue is a fluctuating quantity with a distribution that reflects the firing statistics of the presynaptic population. It was recently found that conductancebased synaptic drive can lead to distributions with a significant skew. Here it is demonstrated that the underlying shot noise caused by Poissonian spike arrival also skews the membrane distribution, but in the opposite sense. Using a perturbative method, we analyze the effects of shot noise on the distribution of synaptic conductances and calculate the consequent voltage distribution. To first order in the perturbation theory, the voltage distribution is a gaussian modulated by a prefactor that captures the skew. The gaussian component is identical to distributions derived using current-based models with an effective membrane time constant. The well-known effective-time-constant approximation can therefore be identified as the leading-order solution to the full conductance-based model. The higher-order modulatory prefactor containing the skew comprises terms due to both shot noise and conductance fluctuations. The diffusion approximation misses these shot-noise effects implying that analytical approaches such as the Fokker-Planck equation or simulation with filtered white noise cannot be used to improve on the gaussian approximation. It is further demonstrated that quantities used for fitting theory to experiment, such as the voltage mean and variance, are robust against these non-Gaussian effects. The effective-time-constant approximation is therefore relevant to experiment and provides a simple analytic base on which other pertinent biological details may be added

Revised distributional estimates for the recently discovered olinguito (Bassaricyon neblina), using museum and science records

In the context of global change, a necessary first step for the conservation of species is gaining a good understanding of their distributional limits. This is especially important for biodiversity hotspots with high endemism such as the Northern Andes. The olinguito (Procyonidae: Bassaricyon neblina) is a recently described, medium-sized carnivoran found in Northern Andean cloud forests. A preliminary distributional model was published along with the original description, and I here provide revised distributional estimates using updated locality records and more current ENM methods. I build ecological niche models in Maxent using occurrence data (georeferenced museum records and citizen science-derived photo-vouchers) and bioclimatic variables. Optimal models were selected via two different approaches, AICc and performance on withheld data. The occurrence data used here show climatic signals different from those for data used in the original description of the species. The AICc-optimal model aligned more closely with current knowledge of the species’ elevational limits. This model shows more extensive suitable area in northern Colombia, and highlights areas for future sampling, such as the central portion of the Western Cordillera of Colombia, mid- and northern portions of the Central Cordillera of Colombia, southwestern Colombia, and the eastern slopes of Eastern Andes in Ecuador. Future conservation planning for this species should also take into account key threats, including deforestation and climate change

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

Competing synapses with two timescales: a basis for learning and forgetting

Competitive dynamics are thought to occur in many processes of learning involving synaptic plasticity. Here we show, in a game theory-inspired model of synaptic interactions, that the competition between synapses in their weak and strong states gives rise to a natural framework of learning, with the prediction of memory inherent in a timescale for `forgetting' a learned signal. Among our main results is the prediction that memory is optimized if the weak synapses are really weak, and the strong synapses are really strong. Our work admits of many extensions and possible experiments to test its validity, and in particular might complement an existing model of reaching, which has strong experimental support.Comment: 7 pages, 3 figures, to appear in Europhysics Letter

Mission possible: Bio hat Zukunft

Der Beitrag zeigt erste Ergebnisse des FiBL-Österreich/Bio-Austria-Projekts zur Wiederkäuergesundheit im Biolandbau auf. Dieses Projekt soll den Landwirten helefen, die Bioverordnung umzusetzen

Breaking Synchrony by Heterogeneity in Complex Networks

For networks of pulse-coupled oscillators with complex connectivity, we demonstrate that in the presence of coupling heterogeneity precisely timed periodic firing patterns replace the state of global synchrony that exists in homogenous networks only. With increasing disorder, these patterns persist until they reach a critical temporal extent that is of the order of the interaction delay. For stronger disorder these patterns cease to exist and only asynchronous, aperiodic states are observed. We derive self-consistency equations to predict the precise temporal structure of a pattern from the network heterogeneity. Moreover, we show how to design heterogenous coupling architectures to create an arbitrary prescribed pattern.Comment: 4 pages, 3 figure

The Impact of Muscular Strength on Cardiovascular Disease Risk Factors

The purpose of this study was to determine the associations between isokinetic leg muscular strength and cardiovascular disease (CVD) risk factor characterizations in Americans aged 50 and older. Using a publicly available dataset from the National Health and Nutrition Examination Survey (NHANES), a secondary analysis was conducted on participants (males ≥50 yrs; females ≥55 yrs; N=10,858) pooled from 1999 to 2002. CVD risk factors were determined using the American College of Sports Medicine (ACSM) cutoff values. CVD risk factor characterization was determined by creating CVD risk factor profiles (i.e., the total number of CVD risk factors an individual possesses), then separating participants into low (0-2 CVD risk factors), moderate (3-5), and high (6-8) risk groups. Muscular strength was determined by isokinetic maximal peak force (PF) of the leg extensors, both raw and normalized to body mass. Normalized, but not raw, muscular strength was shown to be significantly inversely associated with CVD risk factor characterization for both males and females (Phttps://digitalcommons.odu.edu/gradposters2022_education/1002/thumbnail.jp

Supervised Learning in Multilayer Spiking Neural Networks

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.Comment: 38 pages, 4 figure