Thermal model for optimization of vascular laser tissue soldering

Laser tissue soldering (LTS) is a promising technique for tissue fusion based on a heat-denaturation process of proteins. Thermal damage of the fused tissue during the laser procedure has always been an important and challenging problem. Particularly in LTS of arterial blood vessels strong heating of the endothelium should be avoided to minimize the risk of thrombosis. A precise knowledge of the temperature distribution within the vessel wall during laser irradiation is inevitable. The authors developed a finite element model (FEM) to simulate the temperature distribution within blood vessels during LTS. Temperature measurements were used to verify and calibrate the model. Different parameters such as laser power, solder absorption coefficient, thickness of the solder layer, cooling of the vessel and continuous vs. pulsed energy deposition were tested to elucidate their impact on the temperature distribution within the soldering joint in order to reduce the amount of further animal experiments. A pulsed irradiation with high laser power and high absorbing solder yields the best results.

Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model

Generalised epileptic seizures are frequently accompanied by sudden, reversible transitions from low amplitude, irregular background activity to high amplitude, regular spike-wave discharges (SWD) in the EEG. The underlying mechanisms responsible for SWD generation and for the apparently spontaneous transitions to SWD and back again are still not fully understood. Specifically, the role of spatial cortico-cortical interactions in ictogenesis is not well studied. We present a macroscopic, neural mass model of a cortical column which includes two distinct time scales of inhibition. This model can produce both an oscillatory background and a pathological SWD rhythm. We demonstrate that coupling two of these cortical columns can lead to a bistability between out-of-phase, low amplitude background dynamics and in-phase, high amplitude SWD activity. Stimuli can cause state-dependent transitions from background into SWD. In an extended local area of cortex, spatial heterogeneities in a model parameter can lead to spontaneous reversible transitions from a desynchronised background to synchronous SWD due to intermittency. The deterministic model is therefore capable of producing absence seizure-like events without any time dependent adjustment of model parameters. The emergence of such mechanisms due to spatial coupling demonstrates the importance of spatial interactions in modelling ictal dynamics, and in the study of ictogenesis.

A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations

Synaptic strength depresses for low and potentiates for high activation of the postsynaptic neuron. This feature is a key property of the Bienenstock–Cooper–Munro (BCM) synaptic learning rule, which has been shown to maximize the selectivity of the postsynaptic neuron, and thereby offers a possible explanation for experience-dependent cortical plasticity such as orientation selectivity. However, the BCM framework is rate-based and a significant amount of recent work has shown that synaptic plasticity also depends on the precise timing of presynaptic and postsynaptic spikes. Here we consider a triplet model of spike-timing–dependent plasticity (STDP) that depends on the interactions of three precisely timed spikes. Triplet STDP has been shown to describe plasticity experiments that the classical STDP rule, based on pairs of spikes, has failed to capture. In the case of rate-based patterns, we show a tight correspondence between the triplet STDP rule and the BCM rule. We analytically demonstrate the selectivity property of the triplet STDP rule for orthogonal inputs and perform numerical simulations for nonorthogonal inputs. Moreover, in contrast to BCM, we show that triplet STDP can also induce selectivity for input patterns consisting of higher-order spatiotemporal correlations, which exist in natural stimuli and have been measured in the brain. We show that this sensitivity to higher-order correlations can be used to develop direction and speed selectivity.

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

Synaptic regulation of spike firing in interneurons of the striatum in vivo

The striatum, the major input nucleus of the basal ganglia, is numerically dominated by a single class of principal neurons, the GABAergic spiny projection neuron (SPN) that has been extensively studied both in vitro and in vivo. Much less is known about the sparsely distributed interneurons, principally the cholinergic interneuron (CIN) and the GABAergic fast-spiking interneuron (FSI). Here, we summarize results from two recent studies on these interneurons where we used in vivo intracellular recording techniques in urethane-anaesthetized rats (Schulz et al., J Neurosci 31[31], 2011; J Physiol, in press). Interneurons were identified by their characteristic responses to intracellular current steps and spike waveforms. Spontaneous spiking contained a high proportion (~45%) of short inter-spike intervals (ISI) of <30 ms in FSIs, but virtually none in CINs. Spiking patterns in CINs covered a broad spectrum ranging from regular tonic spiking to phasic activity despite very similar unimodal membrane potential distributions across neurons. In general, phasic spiking activity occurred in phase with the slow ECoG waves, whereas CINs exhibiting tonic regular spiking were little affected by afferent network activity. In contrast, FSIs exhibited transitions between Down and Up states very similar to SPNs. Compared to SPNs, the FSI Up state membrane potential was noisier and power spectra exhibited significantly larger power at frequencies in the gamma range (55-95 Hz). Cortical-evoked inputs had faster dynamics in FSIs than SPNs and the membrane potential preceding spontaneous spike discharge exhibited short and steep trajectories, suggesting that fast input components controlled spike output in FSIs. Intrinsic resonance mechanisms may have further enhanced the sensitivity of FSIs to fast oscillatory inputs. Induction of an activated ECoG state by local ejection of bicuculline into the superior colliculus, resulted in increased spike frequency in both interneuron classes without changing the overall distribution of ISIs. This manipulation also made CINs responsive to a light flashed into the contralateral eye. Typically, the response consisted of an excitation at short latency followed by a pause in spike firing, via an underlying depolarization-hyperpolarization membrane sequence. These results highlight the differential sensitivity of striatal interneurons to afferent synaptic signals and support a model where CINs modulate the striatal network in response to salient sensory bottom-up signals, while FSIs serve gating of top-down signals from the cortex during action selection and reward-related learning.

Citizen science reveals unexpected continental-scale evolutionary change in a model organism

Organisms provide some of the most sensitive indicators of climate change and evolutionary responses are becoming apparent in species with short generation times. Large datasets on genetic polymorphism that can provide an historical benchmark against which to test for recent evolutionary responses are very rare, but an exception is found in the brown-lipped banded snail (Cepaea nemoralis). This species is sensitive to its thermal environment and exhibits several polymorphisms of shell colour and banding pattern affecting shell albedo in the majority of populations within its native range in Europe. We tested for evolutionary changes in shell albedo that might have been driven by the warming of the climate in Europe over the last half century by compiling an historical dataset for 6,515 native populations of C. nemoralis and comparing this with new data on nearly 3,000 populations. The new data were sampled mainly in 2009 through the Evolution MegaLab, a citizen science project that engaged thousands of volunteers in 15 countries throughout Europe in the biggest such exercise ever undertaken. A known geographic cline in the frequency of the colour phenotype with the highest albedo (yellow) was shown to have persisted and a difference in colour frequency between woodland and more open habitats was confirmed, but there was no general increase in the frequency of yellow shells. This may have been because snails adapted to a warming climate through behavioural thermoregulation. By contrast, we detected an unexpected decrease in the frequency of Unbanded shells and an increase in the Mid-banded morph. Neither of these evolutionary changes appears to be a direct response to climate change, indicating that the influence of other selective agents, possibly related to changing predation pressure and habitat change with effects on micro-climate.

Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Thermal right-handed neutrino self-energy in the non-relativistic regime

Recently the issue of radiative corrections to leptogenesis has been raised. Considering the "strong washout" regime, in which OPE-techniques permit to streamline the setup, we report the thermal self-energy matrix of heavy right-handed neutrinos at NLO (resummed 2-loop level) in Standard Model couplings. The renormalized expression describes flavour transitions and "inclusive" decays of chemically decoupled right-handed neutrinos. Although CP-violation is not addressed, the result may find use in existing leptogenesis frameworks.

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current--is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of +/-2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

Spindle burst like spike discharge oscillations in organotypic cultures of neonatal rat cortex

Early network oscillations and spindle bursts are typical patterns of spontaneous rhythmic activity in cortical networks of neonatal rodents in vivo and in vitro. The latter can also be triggered in vivo by stimulation of afferent inputs. The mechanisms underlying such oscillations undergo profound developmental changes in the first postnatal weeks. Their possible role in cortical development is postulated but not known in detail. We have studied spontaneous and evoked patterns of activity in organotypic cultures of slices from neonatal rat cortex grown on multielectrode arrays (MEAs) for extracellular single- and multi-unit recording. Episodes of spontaneous spike discharge oscillations at 7 - 25 Hz lasting for 0.6 - 3 seconds appeared in about half of these cultures spontaneously and could be triggered by electrical stimulation of few distinct electrodes. These oscillations usually covered only restricted areas of the slices. Besides oscillations, single population bursts that spread in a wavelike manner over the whole slice also appeared spontaneously and were triggered by electrical stimulation. In most but not all cultures, population bursts preceded the oscillations. Both population bursts and spike discharge oscillations required intact glutamatergic synaptic transmission since they were suppressed by the AMPA/kainate glutamate receptor antagonist CNQX. The NMDA antagonist d-APV suppressed the oscillations but not the population bursts, suggesting an involvement of NMDA receptors in the oscillations. These findings show that spindle burst like cortical rhythms are reproduced in organotypic cultures of neonatal cortex. The culture model thus allows investigating the role of such rhythms in cortical circuit formation. Supported by SNF grant No. 3100A0-107641/1.

Thermal right-handed neutrino production rate in the relativistic regime

The production rate of right-handed neutrinos from a Standard Model plasma at a temperature above a hundred GeV is evaluated up to NLO in Standard Model couplings. The results apply in the so-called relativistic regime, referring parametrically to a mass M ~ πT, generalizing thereby previous NLO results which only apply in the non-relativistic regime M ≫ πT. The non-relativistic expansion is observed to converge for M ≳ 15T, but the smallness of any loop corrections allows it to be used in practice already for M ≳ 4T. In the latter regime any non-covariant dependence of the differential rate on the spatial momentum is shown to be mild. The loop expansion breaks down in the ultrarelativistic regime M ≪ πT, but after a simple mass resummation it nevertheless extrapolates reasonably well towards a result obtained previously through complete LPM resummation, apparently confirming a strong enhancement of the rate at high temperatures (which facilitates chemical equilibration). When combined with other ingredients the results may help to improve upon the accuracy of leptogenesis computations operating above the electroweak scale.

Thermal 2-loop master spectral function at finite momentum

When considering NLO corrections to thermal particle production in the “relativistic” regime, in which the invariant mass squared of the produced particle is K2 ~ (πT)2, then the production rate can be expressed as a sum of a few universal “master” spectral functions. Taking the most complicated 2-loop master as an example, a general strategy for obtaining a convergent 2-dimensional integral representation is suggested. The analysis applies both to bosonic and fermionic statistics, and shows that for this master the non-relativistic approximation is only accurate for K2 ~(8πT)2, whereas the zero-momentum approximation works surprisingly well. Once the simpler masters have been similarly resolved, NLO results for quantities such as the right-handed neutrino production rate from a Standard Model plasma or the dilepton production rate from a QCD plasma can be assembled for K2 ~ (πT)2.