Event-driven simulation of spiking neurons with stochastic dynamics

We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics

Homogeneous and Narrow Bandwidth of Spike Initiation in Rat L1 Cortical Interneurons

The cortical layer 1 (L1) contains a population of GABAergic interneurons, considered a key component of information integration, processing, and relaying in neocortical networks. In fact, L1 interneurons combine top\u2013down information with feed-forward sensory inputs in layer 2/3 and 5 pyramidal cells (PCs), while filtering their incoming signals. Despite the importance of L1 for network emerging phenomena, little is known on the dynamics of the spike initiation and the encoding properties of its neurons. Using acute brain tissue slices from the rat neocortex, combined with the analysis of an existing database of model neurons, we investigated the dynamical transfer properties of these cells by sampling an entire population of known \u201celectrical classes\u201d and comparing experiments and model predictions. We found the bandwidth of spike initiation to be significantly narrower than in L2/3 and 5 PCs, with values below 100 cycle/s, but without significant heterogeneity in the cell response properties across distinct electrical types. The upper limit of the neuronal bandwidth was significantly correlated to the mean firing rate, as anticipated from theoretical studies but not reported for PCs. At high spectral frequencies, the magnitude of the neuronal response attenuated as a power-law, with an exponent significantly smaller than what was reported for pyramidal neurons and reminiscent of the dynamics of a \u201cleaky\u201d integrate-and-fire model of spike initiation. Finally, most of our in vitro results matched quantitatively the numerical simulations of the models as a further contribution to independently validate the models against novel experimental data

FFAs and QT intervals in obese women with visceral adiposity: Effects of sustained weight loss over 1 year

We evaluated 66 obese patients grouped by waist-to-hip ratio (WHR) into group A (WHR > 0.85, n = 30) and group B (WHR ≤ 0.85, n = 36), before and after 1 yr of diet-induced weight loss compared with 25 nonobese women. Before diet, the longest values of QT intervals and the highest levels of FFA and catecholamines were in group A (P < 0.01). In obese women (both groups), the corrected QT (QTc); interval correlated with plasma FFA (P < 0.01) and catecholamine (P < 0.02) concentrations. After 1 yr of diet, at the same levels of body weight reduction, the decrement of the QTc interval (P < 0.02), FFA (P < 0.01) and catecholamine (P < 0.02) levels were significantly greater in-group A than group B. In multivariate analysis, the decline of the QTc interval after weight loss was associated with changes in plasma FFA independently of changes in WHR and plasma catecholamines. Our data suggest that the QTc interval is tightly correlated with plasma FFA levels; shortening of cardiac repolarization times in the course of long-lasting weight reduction may reduce the risk of ventricular electrical instability, especially in women with abdominal adiposity

Fattori di emissione dalla combustione di legna e pellet in piccoli apparecchi domestici

The aim of this study was to report emission factors of pollutants (i.e., carbon monoxide, nitrogen oxides, non-methane hydrocarbons, particulate matter, polycyclic aromatic hydrocarbons, dioxins) from biomass burning residential heating appliances. The influence of several factors such as biomass type, appliance and combustion cycle was investigated. Four manually fed (6-11 kW) firewood burning and two automatic wood pellets (8.8-25 kW) appliances were tested under real-world operating conditions in order to determine the actual environmental performance of the appliance. The experimental EFs were also compared with the values proposed by the European emission inventory guidebook used in the local inventory in order to evaluate their representativeness of real world emissions. The composite macropollutant EFs for manually fed appliances are: for CO 5858 g GJ-1, for NOx 122 g GJ-1, NMHC 542 g GJ-1, PM 254 g GJ-1, whereas emissions are much lower for automatic pellets appliances: CO 219 g GJ-1, for NOx 66 g GJ-1, NMHC 5 g GJ-1, PM 85 g GJ-1. The open fireplace appears to have very high emission factors, however traditional and advanced stoves show the highest overall CO EFs. Especially for the advanced stove real-world emissions are far worse than those measured under cycles used for type testing of residential solid fuel appliances. No great difference is observed for different firewood types in batch working appliances, diversely the quality of the pellets is observed to influence directly the emission performance of the automatic appliances. Benzo(b)fluoranthene is the PAH with the highest contribution (110 mg GJ-1 for manual appliances and 2 mg GJ-1 for automatic devices) followed by benzo(a)pyrene (77 mg GJ-1 for manual appliances and 0,8 mg GJ-1 for automatic devices)

Multidimensional Functional Profiling of Human Neuropathogenic FOXG1 Alleles in Primary Cultures of Murine Pallial Precursors

FOXG1 is an ancient transcription factor gene mastering telencephalic development. A number of distinct structural FOXG1 mutations lead to the “FOXG1 syndrome”, a complex and heterogeneous neuropathological entity, for which no cure is presently available. Reconstruction of primary neurodevelopmental/physiological anomalies evoked by these mutations is an obvious pre-requisite for future, precision therapy of such syndrome. Here, as a proof-of-principle, we functionally scored three FOXG1 neuropathogenic alleles, FOXG1G224S, FOXG1W308X, and FOXG1N232S, against their healthy counterpart. Specifically, we delivered transgenes encoding for them to dedicated preparations of murine pallial precursors and quantified their impact on selected neurodevelopmental and physiological processes mastered by Foxg1: pallial stem cell fate choice, proliferation of neural com-mitted progenitors, neuronal architecture, neuronal activity, and their molecular correlates. Briefly, we found that FOXG1G224S and FOXG1W308X generally performed as a gain-and a loss-of-function-allele, respectively, while FOXG1N232S acted as a mild loss-of-function-allele or phenocopied FOXG1WT . These results provide valuable hints about processes misregulated in patients heterozygous for these mutations, to be re-addressed more stringently in patient iPSC-derivative neuro-organoids. Moreover, they suggest that murine pallial cultures may be employed for fast multidimensional profiling of novel, human neuropathogenic FOXG1 alleles, namely a step propedeutic to timely delivery of therapeutic precision treatments

Non-monotonic current-to-rate response function in a novel integrate-and-fire model neuron

A novel integrate-and-fire model neuron is proposed to account for a non-monotonic f-I response function, as experimentally observed. As opposed to classical forms of adaptation, the present integrate- and-fire model the spike-emission process incorporates a state - dependent inactivation that makes the probability of emitting a spike decreasing as a function of the mean depolarization level instead of the mean firing rate. \ua9 Springer-Verlag Berlin Heidelberg 2002

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s