SummarySynaptic efficacy and precision are influenced by the coupling of voltage-gated Ca2+ channels (VGCCs) to vesicles. But because the topography of VGCCs and their proximity to vesicles is unknown, a quantitative understanding of the determinants of vesicular release at nanometer scale is lacking. To investigate this, we combined freeze-fracture replica immunogold labeling of Cav2.1 channels, local [Ca2+] imaging, and patch pipette perfusion of EGTA at the calyx of Held. Between postnatal day 7 and 21, VGCCs formed variable sized clusters and vesicular release became less sensitive to EGTA, whereas fixed Ca2+ buffer properties remained constant. Experimentally constrained reaction-diffusion simulations suggest that Ca2+ sensors for vesicular release are located at the perimeter of VGCC clusters (<30 nm) and predict that VGCC number per cluster determines vesicular release probability without altering release time course. This “perimeter release model” provides a unifying framework accounting for developmental changes in both synaptic efficacy and time course.

Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate that IHT mainly functions to repolarize the membrane during an action potential, and IA functions to modulate the rate of repetitive firing. ILT is found to be responsible for the phasic discharge pattern observed in Type II cells (bushy cells). However, by adjusting the strength of ILT, both phasic and regular discharge patterns are observed, demonstrating that a critical level of ILT is necessary to produce the Type II response. Simulated Type II cells have a significantly faster membrane time constant in comparison to Type I cells (stellate cells) and are therefore better suited to preserve temporal information in their auditory nerve inputs by acting as precise coincidence detectors and having a short refractory period. Finally, we demonstrate that modulation of Ih, which changes the resting membrane potential, is a more effective means of modulating the activation level of ILT than simply modulating ILT itself. This result may explain why ILT and Ih are often coexpressed throughout the nervous system.

To act as computational devices, neurons must perform mathematical operations as they transform synaptic and modulatory input into output firing rate1. Experiments and theory suggest that neuronal firing typically represents the sum of synaptic inputs1-3, an additive operation, but multiplication of inputs is essential for many computations1. Multiplication by a constant produces a change in the slope, or gain, of the input-output relation, amplifying or scaling down the neuron's sensitivity to changes in its input. Such gain modulation occurs in vivo, during contrast invariance of orientation tuning4, attentional scaling5, translation-invariant object recognition6, auditory processing7 and coordinate transformations8,9. Moreover, theoretical studies highlight the necessity of gain modulation in several of these tasks9-11. While potential cellular mechanisms for gain modulation have been identified, they often rely on membrane noise and require restrictive conditions to work3,12-18. Because nonlinear components are used to scale signals in electronics, we examined whether synaptic nonlinearities are involved in neuronal gain modulation. We used synaptic stimulation and dynamic-clamp to investigate gain modulation in granule cells (GCs) in acute cerebellar slices. Here we show that when excitation is mediated by synapses with short-term depression (STD), neuronal gain is controlled by an inhibitory conductance in a noise-independent manner, allowing driving and modulatory inputs to be multiplied together. The nonlinearity introduced by STD transforms inhibition-mediated additive shifts in the input-output relation into multiplicative gain changes. When GCs were driven with bursts of high-frequency mossy fibre (MF) input, as observed in vivo19,20, larger inhibition-mediated gain changes were observed, as expected with greater STD. Simulations of synaptic integration in more complex neocortical neurons confirm that STD-based gain modulation can also operate in neurons with large dendritic trees. Our results establish that neurons receiving depressing excitatory inputs can act as powerful multiplicative devices even when integration of postsynaptic conductances is linear.

Acquisition, analysis and simulation of electrophysiological properties of the nervous system require multiple software packages. This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. Since its original release, NeuroMatic has been used in a wide range of scientific studies and its user base has grown considerably. NeuroMatic version 3.0 can be found at http://www.neuromatic.thinkrandom.com and https://github.com/SilverLabUCL/NeuroMatic.

1. Convergence of auditory nerve (AN) fibers onto bushy cells of the ventral cochlear nucleus (VCN) was investigated with a model that describes the electrical membrane properties of these cells. The model consists of a single compartment, representing the soma, and includes three voltage-sensitive ion channels (fast sodium, delayed-rectifier-like potassium, and low-threshold potassium). These three channels have characteristics derived from voltage clamp data of VCN bushy cells. The model also contains a leakage channel, membrane capacitance, and synaptic inputs. The model accurately reproduces the membrane rectification seen in current clamp studies of bushy cells, as well as their unique current clamp responses. 2. In this study, the number and synaptic strength of excitatory AN inputs to the model were varied to investigate the relationship between input convergence parameters and response characteristics. From 1 to 20 excitatory synaptic inputs were applied through channels in parallel with the voltage-gated channels. Each synapse was driven by an independent AN spike train; spike arrivals produced brief (approximately 0.5 ms) conductance increases. The number (NS) and conductance (AE) of these inputs were systematically varied. The input spike trains were generated as a renewal point process that accurately models characteristics of AN fibers (refractoriness, adaptation, onset latency, irregularity of discharge, and phase locking). Adaptation characteristics of both high and low spontaneous rate (SR) AN fibers were simulated. 3. As NS and AE vary over the ranges 1-20 and 3-80 nS, respectively, the full range of response types seen in VCN bushy cells are produced by the model, with AN inputs typical of high-SR AN fibers. These include primarylike (PL), primarylike-with-notch (Pri-N), and onset-L (On-L). In addition, Onset responses, whose association with bushy cells in uncertain, and "dip" responses, which are not seen in the VCN, are produced. Dip responses occur with large NS and/or AE, and are due to depolarization block. When the AN inputs have the adaptation characteristics of low-SR AN fibers, PL--but not Pri-N or On-L responses--are produced. This suggests that neurons showing Pri-N and On-L responses must receive high-SR AN inputs. 4. The regularity of discharge of the model is compared with that of VCN bushy cells, using a measure derived from the mean and standard deviation of interspike intervals. Regularity is an important constraint on the model because the regularity of VCN bushy cells is the same as that of their AN inputs.(ABSTRACT TRUNCATED AT 400 WORDS)