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SummaryKv3 potassium currents mediate rapid repolarization of action potentials (AP), supporting fast spikes and high repetition rates. Of the four Kv3 gene family members, Kv3.1 and Kv3.3 are highly expressed in the auditory brainstem and we exploited this to test for subunit-specific roles at the calyx of Held presynaptic terminal. Deletion of Kv3.3 (but not Kv3.1) increased presynaptic AP duration and facilitated transmitter release, which in turn enhanced short-term depression during high frequency transmission. The response to sound was delayed in the Kv3.3KO, with higher spontaneous and lower evoked firing, thereby reducing signal-to-noise ratio. Computational modelling showed that the enhanced EPSC and short-term depression in the Kv3.3KO reflected increased vesicle release probability and accelerated activity-dependent vesicle replenishment. We conclude that Kv3.3 is the presynaptic ‘delayed rectifier’, enabling short duration, precisely timed APs to maintain transmission at high frequencies and during sustained synaptic activity.

Aim: Modelling African great ape distribution has until now focused on current or past conditions, while future scenarios remain scarcely explored. Using an ensemble forecasting approach, we predicted changes in taxon-specific distribution under future scenarios of climate, land use and human populations for (1) areas outside protected areas (PAs) only (assuming complete management effectiveness of PAs), (2) the entire study region and (3) interspecies range overlap. Location: Tropical Africa. Methods: We compiled occurrence data (n = 5,203) on African apes from the IUCN A.P.E.S. database and extracted relevant climate-, habitat- and human-related predictors representing current and future (2050) conditions to predict taxon-specific range change under a best- and a worst-case scenario, using ensemble forecasting. Results: The predictive performance of the models varied across taxa. Synergistic interactions between predictors are shaping African ape distribution, particularly human-related variables. On average across taxa, a range decline of 50% is expected outside PAs under the best scenario if no dispersal occurs (61% in worst scenario). Otherwise, an 85% range reduction is predicted to occur across study regions (94% worst). However, range gains are predicted outside PAs if dispersal occurs (52% best, 21% worst), with a slight increase in gains expected across study regions (66% best, 24% worst). Moreover, more than half of range losses and gains are predicted to occur outside PAs where interspecific ranges overlap. Main Conclusions: Massive range decline is expected by 2050, but range gain is uncertain as African apes will not be able to occupy these new areas immediately due to their limited dispersal capacity, migration lag and ecological constraints. Given that most future range changes are predicted outside PAs, Africa's current PA network is likely to be insufficient for preserving suitable habitats and maintaining connected ape populations. Thus, conservation planners urgently need to integrate land use planning and climate change mitigation measures at all decision-making levels both in range countries and abroad.

The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator. In order to solve such an issue, we provide a learning objective, learning a maximal informative generator while maintaining bounded the network capacity: the Variational InfoMax (VIM). The contribution of the VIM derivation is twofold: an objective learning both an optimal inference and generative model and the explicit definition of the network capacity, an estimation of the network robustness.

AimModelling African great ape distribution has until now focused on current or past conditions, whilst future scenarios remain scarcely explored. Using an ensemble forecasting approach, we predicted changes in taxon-specific distribution under future scenarios of climate, land-use and human population changes.LocationSub-Saharan AfricaMethodsWe compiled occurrence data on African ape populations from the IUCN A.P.E.S. database and extracted relevant human-, climate- and habitat-related predictors representing current and future (2050) conditions to predict taxon-specific distribution under a best- and a worst-case scenario, using ensemble forecasting. Given the large effect on model predictions, we further tested algorithm sensitivity by considering default and non-default modelling options. The latter included interactions between predictors and polynomial terms in correlative algorithms.ResultsThe future distributions of gorilla and bonobo populations are likely to be directly determined by climate-related variables. In contrast, future chimpanzee distribution is influenced mostly by anthropogenic variables. Both our modelling approaches produced similar model accuracy, although a slight difference in the magnitude of range change was found for Gorilla beringei beringei, G. gorilla diehli, and Pan troglodytes schweinfurthii. On average, a decline of 50% of the geographic range (non-default; or 55% default) is expected under the best scenario if no dispersal occurs (57% non-default or 58% default in worst scenario). However, new areas of suitable habitat are predicted to become available for most taxa if dispersal occurs (81% or 103% best, 93% or 91% worst, non-default and default, respectively), except for G. b. beringei.Main ConclusionsDespite the uncertainty in predicting the precise proportion of suitable habitat by 2050, both modelling approaches predict large range losses for all African apes. Thus, conservation planners urgently need to integrate land-use planning and simultaneously support conservation and climate change mitigation measures at all decision-making levels both in range countries and abroad.

Key points Synapses have high energy demands which increase during intense activity. We show that presynaptic terminals can utilise extracellular glucose or lactate to generate energy to maintain synaptic transmission. Reducing energy substrates induces a metabolic stress: presynaptic ATP depletion impaired synaptic transmission through a reduction in the number of functional synaptic vesicle release sites and a slowing of vesicle pool replenishment, without a consistent change in release probability. Metabolic function is compromised in many pathological conditions (e.g. stroke, traumatic brain injury and neurodegeneration). Knowledge of how synaptic transmission is constrained by metabolic stress, especially during intense brain activity, will provide insights to improve cognition following pathological insults. Abstract The synapse has high energy demands, which increase during intense activity. Presynaptic ATP production depends on substrate availability and usage will increase during activity, which in turn could influence transmitter release and information transmission. We investigated transmitter release at the mouse calyx of Held synapse using glucose or lactate (10, 1 or 0 mm) as the extracellular substrates while inducing metabolic stress. High-frequency stimulation (HFS) and recovery paradigms evoked trains of EPSCs monitored under voltage-clamp. Whilst postsynaptic intracellular ATP was stabilised by diffusion from the patch pipette, depletion of glucose increased EPSC depression during HFS and impaired subsequent recovery. Computational modelling of these data demonstrated a reduction in the number of functional release sites and slowed vesicle pool replenishment during metabolic stress, with little change in release probability. Directly depleting presynaptic terminal ATP impaired transmitter release in an analogous manner to glucose depletion. In the absence of glucose, presynaptic terminal metabolism could utilise lactate from the aCSF and this was blocked by inhibition of monocarboxylate transporters (MCTs). MCT inhibitors significantly suppressed transmission in low glucose, implying that lactate is a presynaptic substrate. Additionally, block of glycogenolysis accelerated synaptic transmission failure in the absence of extracellular glucose, consistent with supplemental supply of lactate by local astrocytes. We conclude that both glucose and lactate support presynaptic metabolism and that limited availability, exacerbated by high-intensity firing, constrains presynaptic ATP, impeding transmission through a reduction in functional presynaptic release sites as vesicle recycling slows when ATP levels are low.

The application of deep learning techniques has led to substantial progress in solving a number of critical problems in machine vision, including fundamental problems of scene segmentation and depth estimation. Here, we report a novel deep neural network model, capable of simultaneous scene segmentation and depth estimation from a pair of binocular images. By manipulating the arrangement of binocular image pairs, presenting the model with standard left-right image pairs, identical image pairs or swapped left-right images, we show that performance levels depend on the presence of appropriate binocular image arrangements. Segmentation and depth estimation performance are both impaired when images are swapped. Segmentation performance levels are maintained, however, for identical image pairs, despite the absence of binocular disparity information. Critically, these performance levels exceed those found for an equivalent, monocularly trained, segmentation model. These results provide evidence that binocular image differences support both the direct recovery of depth and segmentation information, and the enhanced learning of monocular segmentation signals. This finding suggests that binocular vision may play an important role in visual development. Better understanding of this role may hold implications for the study and treatment of developmentally acquired perceptual impairments.

Primates are facing an impending extinction crisis, driven by extensive habitat loss, land use change and hunting. Climate change is an additional threat, which alone or in combination with other drivers, may severely impact those taxa unable to track suitable environmental conditions. Here, we investigate the extent of climate and land use/cover (LUC) change-related risks for primates. We employed an analytical approach to objectively select a subset of climate scenarios, for which we then calculated changes in climatic and LUC conditions for 2050 across primate ranges (N = 426 species) under a best-case scenario and a worst-case scenario. Generalized linear models were used to examine whether these changes varied according to region, conservation status, range extent and dominant habitat. Finally, we reclassified primate ranges based on different magnitudes of maximum temperature change, and quantified the proportion of ranges overall and of primate hotspots in particular that are likely to be exposed to extreme temperature increases. We found that, under the worst-case scenario, 74% of Neotropical forest-dwelling primates are likely to be exposed to maximum temperature increases up to 7°C. In contrast, 38% of Malagasy savanna primates will experience less pronounced warming of up to 3.5°C. About one quarter of Asian and African primates will face up to 50% crop expansion within their range. Primary land (undisturbed habitat) is expected to disappear across species' ranges, whereas secondary land (disturbed habitat) will increase by up to 98%. With 86% of primate ranges likely to be exposed to maximum temperature increases >3°C, primate hotspots in the Neotropics are expected to be particularly vulnerable. Our study highlights the fundamental exposure risk of a large percentage of primate ranges to predicted climate and LUC changes. Importantly, our findings underscore the urgency with which climate change mitigation measures need to be implemented to avert primate extinctions on an unprecedented scale.

Pyramidal cells in layer 5 of the neocortex have two distinct integration sites. These cells integrate inputs to basal dendrites in the soma while integrating inputs to the tuft in a site at the top of the apical trunk. The two sites communicate by action potentials that backpropagate to the apical site and by backpropagation-activated calcium spikes (BAC firing) that travel from the apical to the somatic site. Six key messages arise from the probabilistic information-theoretic analyses of BAC firing presented here. First, we suggest that pyramidal neurons with BAC firing could convert the odds in favour of the presence of a feature given the basal data into the odds in favour of the presence of a feature given the basal data and the apical input, by a simple Bayesian calculation. Second, the strength of the cell’s response to basal input can be amplified when relevant to the current context, as specified by the apical input, without corrupting the message that it sends. Third, these analyses show rigorously how this apical amplification depends upon communication between the sites. Fourth, we use data on action potentials from a very detailed multi-compartmental biophysical model to study our general model in a more realistic setting, and demonstrate that it describes the data well. Fifth, this form of BAC firing meets criteria for distinguishing modulatory from driving interactions that have been specified using recent definitions of multivariate mutual information. Sixth, our general decomposition can be extended to cases where, instead of being purely driving or purely amplifying, apical and basal inputs can be partly driving and partly amplifying to various extents. These conclusions imply that an advance beyond the assumption of a single site of integration within pyramidal cells is needed, and suggest that the evolutionary success of neocortex may depend upon the cellular mechanisms of context-sensitive selective amplification hypothesized here.Author summaryThe cerebral cortex has a key role in conscious perception, thought, and action, and is predominantly composed of a particular kind of neuron: the pyramidal cells. The distinct shape of the pyramidal neuron with a long dendritic shaft separating two regions of profuse dendrites allows them to integrate inputs to the two regions separately and combine the results non-linearly to produce output. Here we show how inputs to this more distant site strengthen the cell’s output when it is relevant to the current task and environment. By showing that such neurons have capabilities that transcend those of neurons with the single site of integration assumed by many neuroscientists, this ‘splitting of the neuronal atom’ offers a radically new viewpoint from which to understand the evolution of the cortex and some of its many pathologies. This also suggests that approaches to artificial intelligence using neural networks might come closer to something analogous to real intelligence, if, instead of basing them on processing elements with a single site of integration, they were based on elements with two sites, as in cortex.

We present a variation of the Autoencoder (AE) that explicitly maximizes the mutual information between the input data and the hidden representation. The proposed model, the InfoMax Autoencoder (IMAE), by construction is able to learn a robust representation and good prototypes of the data. IMAE is compared both theoretically and then computationally with the state of the art models: the Denoising and Contractive Autoencoders in the one-hidden layer setting and the Variational Autoencoder in the multi-layer case. Computational experiments are performed with the MNIST and Fashion-MNIST datasets and demonstrate particularly the strong clusterization performance of IMAE.

International audience; Previous experimental data indicates the hyperpolarization-activated cation (Ih ) current, in the inner ear, consists of two components [different hyperpolarization-activated cyclic nucleotide-gated (HCN) subunits] which are impossible to pharmacologically isolate. To confirm the presence of these two components in vestibular ganglion neurons we have applied a parameter identification algorithm which is able to discriminate the parameters of the two components from experimental data. Using simulated data we have shown that this algorithm is able to identify the parameters of two populations of non-inactivated ionic channels more accurately than a classical method. Moreover, the algorithm was demonstrated to be insensitive to the key parameter variations. We then applied this algorithm to Ih current recordings from mouse vestibular ganglion neurons. The algorithm revealed the presence of a high-voltage-activated slow component and a low-voltage-activated fast component. Finally, the electrophysiological significance of these two Ih components was tested individually in computational vestibular ganglion neuron models (sustained and transient), in the control case and in the presence of cAMP, an intracellular cyclic nucleotide that modulates HCN channel activity. The results suggest that, first, the fast and slow components modulate differently the action potential excitability and the excitatory postsynaptic potentials in both sustained and transient vestibular neurons and, second, the fast and slow components, in the control case, provide different information about characteristics of the stimulation and this information is significantly modified after modulation by cAMP.

Background. Schizophrenia is a psychiatric disorder which is characterized by delusions and hallucinations, and affects thoughts, behaviour and emotions. Major neuronal degeneration is not observed in schizophrenic patients, but abnormalities in cortical circuits are present. These abnormalities are reflected in impaired EEG gamma frequency (30–80 Hz), being crucial for many processes including sensation, perception, working memory, and attention. NMDA and GABA synaptic dysfunction is proposed as one of the possible mechanisms underlying the gamma oscillatory deficits in schizophrenia. Materials and Methods. We used a computational modeling approach to investigate the joint influence of NMDA and GABA synaptic dysfunction on gamma oscillations in cortex. We employed a computational model of a spiking neural network composed of 800 pyramidal neurons, 150 regular-spiking interneurons, and 50 fast-spiking interneurons. All cells were randomly interconnected. Network neurons received independent Poisson noise input at 4 Hz and 40 Hz drive excitatory stimulation. Fast-spiking interneuron GABA receptor-gated channel time constant was increased and NMDA receptor-gated channel synaptic conductance was decreased to represent synaptic dysfunction in schizophrenia. Results. Reducing NMDA conductance enhanced gamma power, and increasing decay time constant of GABA receptorgated channel attenuated gamma generation in a network. The effect of synaptic GABA alteration was more profound. Conclusions. NMDA and GABA synaptic dysfunction leads to the impaired gamma frequency oscillations in a spiking neural network of cortex. Computational modeling approach is a powerful tool to understand complex non-linear dynamical systems and intrinsic mechanisms of neuronal network activity in healthy and diseased brain.

KEY POINTS: Synapses have high energy demands which increase during intense activity. We show that presynaptic terminals can utilise extracellular glucose or lactate to generate energy to maintain synaptic transmission. Reducing energy substrates induces a metabolic stress: presynaptic ATP depletion impaired synaptic transmission through a reduction in the number of functional synaptic vesicle release sites and a slowing of vesicle pool replenishment, without a consistent change in release probability. Metabolic function is compromised in many pathological conditions (e.g. stroke, traumatic brain injury and neurodegeneration). Knowledge of how synaptic transmission is constrained by metabolic stress, especially during intense brain activity, will provide insights to improve cognition following pathological insults. ABSTRACT: The synapse has high energy demands, which increase during intense activity. Presynaptic ATP production depends on substrate availability and usage will increase during activity, which in turn could influence transmitter release and information transmission. We investigated transmitter release at the mouse calyx of Held synapse using glucose or lactate (10, 1 or 0 mm) as the extracellular substrates while inducing metabolic stress. High‐frequency stimulation (HFS) and recovery paradigms evoked trains of EPSCs monitored under voltage‐clamp. Whilst postsynaptic intracellular ATP was stabilised by diffusion from the patch pipette, depletion of glucose increased EPSC depression during HFS and impaired subsequent recovery. Computational modelling of these data demonstrated a reduction in the number of functional release sites and slowed vesicle pool replenishment during metabolic stress, with little change in release probability. Directly depleting presynaptic terminal ATP impaired transmitter release in an analogous manner to glucose depletion. In the absence of glucose, presynaptic terminal metabolism could utilise lactate from the aCSF and this was blocked by inhibition of monocarboxylate transporters (MCTs). MCT inhibitors significantly suppressed transmission in low glucose, implying that lactate is a presynaptic substrate. Additionally, block of glycogenolysis accelerated synaptic transmission failure in the absence of extracellular glucose, consistent with supplemental supply of lactate by local astrocytes. We conclude that both glucose and lactate support presynaptic metabolism and that limited availability, exacerbated by high‐intensity firing, constrains presynaptic ATP, impeding transmission through a reduction in functional presynaptic release sites as vesicle recycling slows when ATP levels are low.

Synaptic plasticity in hippocampal CA1 pyramidal neurons is accompanied and shaped by dendritic plasticity, the long-lasting and region-specific alteration of the biophysical properties of voltage-gated dendritic ion channels. Down-regulation of A-type potassium current \(I_{\mathrm {A}}\), observed after long-term potentiation induction, boosts the amplitude of somatic back-propagating action potentials and the associated calcium concentration in dendritic spines, increases the amplitude of the excitatory postsynaptic potentials and promotes synaptic integration. Using a detailed computational model of a CA1 pyramidal cell and a spike-timing-dependent synaptic plasticity (STDP) protocol we found that suppression of A-type potassium current \(I_{\mathrm {A}}\) leads to the increased dendritic excitability converting long-term depression to long-term potentiation in proximal synapses and supporting fast Hebbian plasticity in distal synapses on a hippocampal CA1 pyramidal neuron.

Cellular activity in the CA1 area of the hippocampus waxes and wanes at theta frequency (4-8 Hz) during exploratory behavior of rats. Perisomatic inhibition onto pyramidal cells tends to be strongest out of phase with pyramidal cell activity, whereas dendritic inhibition is strongest in phase with pyramidal cell activity. Synaptic plasticity also varies across the theta cycle, from strong long-term potentiation (LTP) to long-term depression (LTD), putatively corresponding to encoding and retrieval phases for information patterns encoded by pyramidal cell activity (Hasselmo et al. (2002a) Neural Comput 14:793-817). The mechanisms underpinning the phasic changes in plasticity are not clear, but it is likely that inhibition plays a role by affecting levels of electrical activity and calcium concentration at synapses. We explore the properties of synaptic plasticity during theta at Schaffer collateral synapses on CA1 pyramidal neurons and the influence of spatially and temporally targeted inhibition using a detailed multicompartmental model of the CA1 pyramidal neuron microcircuit and a phenomenological model of synaptic plasticity. The results suggest CA3-CA1 synapses are potentiated on one phase of theta due to high calcium levels provided by paired weak CA3 and layer III entorhinal cortex (EC) inputs even when somatic spiking is inhibited by perisomatic interneuron activity. Weak CA3 inputs alone induce lower calcium transients and result in depression of the CA3-CA1 synapses. These synapses are depressed if activated in phase with dendritic inhibition as strong CA3 inputs alone are not able to cause high calcium in this theta phase even though the CA1 pyramidal neuron shows somatic spiking. Dendritic inhibition acts as a switch that prevents LTP and promotes LTD during the retrieval phases of the theta rhythm in CA1 pyramidal cell. This may be important for not overly reinforcing recalled memories and in forgetting no longer relevant memories.

The neuromodulator nitric oxide (NO), in addition to regulating electrophysiological homeostasis, post-synaptic receptor activity, and neuronal functions through cyclic GMP, has recently been shown to modulate the metabolic pathway. NO down-regulates mitochondrial activity [1] and the subsequent increase in AMP facilitates the activation of the phosphofructokinase enzymatic reaction in the glycolytic pathway [2]. Given these results, we want to better understand the regulation of neuronal energy metabolism by NO. To do that, we have built a computational model of energy metabolism based on prior works, principally on a model elucidating the control system structures of neuronal metabolism [3] and another highlighting the metabolic role between neuronal activity and hemodynamics [4]. From the first we took the biochemical pathway model, i.e. glycolysis, mitochondrial activity and regulation by the astrocyte to neuron lactate shuttle. From the second we took a model of intracellular sodium concentration and pumping by Na/K-ATPase. We added the activity dependent glutamate cycle [5], driven by electrophysiological activity. We finally completed these models with the previously described modulation by NO. We have new experimental recordings of post synaptic currents in principal cells of the mouse medial nucleus of the trapezoid body (MNTB) in response to high frequency presynaptic stimulation. To match our model to this data we assumed the neurotransmitter released during electrophysiological activity to be proportional to the post synaptic current amplitude and so our glutamate cycle synaptic model, described above, could be related to the recorded EPSC amplitudes. The full metabolism model was then calibrated to match actual voltage clamp MNTB recordings of synapse activity, in the control case and in the presence of 5 mM 2-deoxy-D-glucose, blocking glycolysis, allowing only mitochondrial activity. Model outputs are compared between (1) the baseline (control) conditions, (2) following conditioning with evoked activity that increases NO levels, modifying individually glycolysis and mitochondrial activity, and (3) in full NO regulated conditions, in order to evaluate the individual and mixed metabolism dynamics. Results show that ATP production is slightly increased by NO upregulation of glycolysis (2.5%), significantly increased by mitochondrial inhibition by NO (12.3%) and further increased (15.6%) when both glycolysis and mitochondria are NO modulated. This model will eventually be combined with our previous postsynaptic model that shows NO modulation can reduce the cost of action potential generation in MNTB neurons [6] to build a more complete model of energy consumption during synaptic transmission.

SummaryNeuronal nitric oxide synthase (nNOS) is broadly expressed in the brain and associated with synaptic plasticity through NMDAR-mediated calcium influx. However, its physiological activation and the mechanisms by which nitric oxide (NO) influences synaptic transmission have proved elusive. Here, we exploit the unique input-specificity of the calyx of Held to characterize NO modulation at this glutamatergic synapse in the auditory pathway. NO is generated in an activity-dependent manner by MNTB principal neurons receiving a calyceal synaptic input. It acts in the target neuron and adjacent inactive neurons to modulate excitability and synaptic efficacy, inhibiting postsynaptic Kv3 potassium currents (via phosphorylation), reducing EPSCs and so increasing action potential duration and reducing transmission fidelity. We conclude that NO serves as a volume transmitter and slow dynamic modulator, integrating spontaneous and evoked neuronal firing, thereby providing an index of global activity and regulating information transmission across a population of active and inactive neurons.

Sustained activity at most central synapses is accompanied by a number of short-term changes in synaptic strength which act over a range of time scales. Here we examine experimental data and develop a model of synaptic depression at the calyx of Held synaptic terminal that combines many of these mechanisms (acting at differing sites and across a range of time scales). This new model incorporates vesicle recycling, facilitation, activity-dependent vesicle retrieval and multiple mechanisms affecting calcium channel activity and release probability. It can accurately reproduce the time course of experimentally measured short-term depression across different stimulus frequencies and exhibits a slow decay in EPSC amplitude during sustained stimulation. We show that the slow decay is a consequence of vesicle release inhibition by multiple mechanisms and is accompanied by a partial recovery of the releasable vesicle pool. This prediction is supported by patch-clamp data, using long duration repetitive EPSC stimulation at up to 400 Hz. The model also explains the recovery from depression in terms of interaction between these multiple processes, which together generate a stimulus-history-dependent recovery after repetitive stimulation. Given the high rates of spontaneous activity in the auditory pathway, the model also demonstrates how these multiple interactions cause chronic synaptic depression under in vivo conditions. While the magnitude of the depression converges to the same steady state for a given frequency, the time courses of onset and recovery are faster in the presence of spontaneous activity. We conclude that interactions between multiple sources of short-term plasticity can account for the complex kinetics during high frequency stimulation and cause stimulus-history-dependent recovery at this relay synapse.

It is well established that synaptic transmission declines at temperatures below physiological, but many in vitro studies are conducted at lower temperatures. Recent evidence suggests that temperature-dependent changes in presynaptic mechanisms remain in overall equilibrium and have little effect on transmitter release at low transmission frequencies. Our objective was to examine the postsynaptic effects of temperature. Whole-cell patch-clamp recordings from principal neurons in the medial nucleus of the trapezoid body showed that a rise from 25 degrees C to 35 degrees C increased miniature EPSC (mEPSC) amplitude from -33 +/- 2.3 to -46 +/- 5.7 pA (n=6) and accelerated mEPSC kinetics. Evoked EPSC amplitude increased from -3.14 +/- 0.59 to -4.15 +/- 0.73 nA with the fast decay time constant accelerating from 0.75 +/- 0.09 ms at 25 degrees C to 0.56 +/- 0.08 ms at 35 degrees C. Direct application of glutamate produced currents which similarly increased in amplitude from -0.76 +/- 0.10 nA at 25 degrees C to -1.11 +/- 0.19 nA 35 degrees C. Kinetic modelling of fast AMPA receptors showed that a temperature-dependent scaling of all reaction rate constants by a single multiplicative factor (Q10=2.4) drives AMPA channels with multiple subconductances into the higher-conducting states at higher temperature. Furthermore, Monte Carlo simulation and deconvolution analysis of transmission at the calyx of Held showed that this acceleration of the receptor kinetics explained the temperature dependence of both the mEPSC and evoked EPSC. We propose that acceleration in postsynaptic AMPA receptor kinetics, rather than altered presynaptic release, is the primary mechanism by which temperature changes alter synaptic responses at low frequencies.

The Izhikevich spiking neuron model is a relatively new mathematical framework which is able to represent many observed spiking neuron behaviors, excitatory or inhibitory, by simply adjusting a set of four model parameters. This model is deterministic in nature and has achieved wide applications in analytical and numerical analysis of biological neurons due largely to its biological plausibility and computational efficiency. In this work we present a stochastic version of the Izhikevich neuron, and measure its performance in transmitting information in a range of biological frequencies. The work reveals that the deterministic Izhikevich model has a wide information transmission range and is generally better in transmitting information than its stochastic counterpart.

Neuronal computation of information is metabolically expensive. In parallel to computation, activity-dependent modulation of neuronal excitability occurs to adapt neural responses and possibly optimize metabolic cost. In this study, we focus on the metabolic cost of electrophysiological activity during the modulatory effect of nitric oxide in the auditory brainstem. The model describes the glycolysis and mitochondrial activity, the main means of adenosine triphosphate synthesis, in response to electrophysiological stimulation of a neuron in the control case or in the presence of nitric oxide. The model permits the evaluation of adenosine triphosphate variation at different stages of the metabolic pathway, and shows that nitric oxide not only avoids transmission failures at high frequency stimulation but also decreases the metabolic cost of neuronal firing.

Neurite outgrowth (dendrites and axons) should be a stable, but easily regulated process to enable a neuron to make its appropriate network connections during development. We explore the dynamics of outgrowth in a mathematical continuum model of neurite elongation. The model describes the construction of the internal microtubule cytoskeleton, which results from the production and transport of tubulin dimers and their assembly into microtubules at the growing neurite tip. Tubulin is assumed to be largely synthesised in the cell body from where it is transported by active mechanisms and by diffusion along the neurite. It is argued that this construction process is a fundamental limiting factor in neurite elongation. In the model, elongation is highly stable when tubulin transport is dominated by either active transport or diffusion, but oscillations in length may occur when both active transport and diffusion contribute. Autoregulation of tubulin production can eliminate these oscillations. In all cases a stable steady-state length is reached, provided there is intrinsic decay of tubulin. Small changes in growth parameters, such as the tubulin production rate, can lead to large changes in length. Thus cytoskeleton construction can be both stable and easily regulated, as seems necessary for neurite outgrowth during nervous system development.