Volker Steuber

Synaptic depression enables neuronal gain control

To act as computational devices, neurons must perform mathematical operations as they transform synaptic and modulatory input into output firing rate. Experiments and theory indicate that neuronal firing typically represents the sum of synaptic inputs, an additive operation, but multiplication of inputs is essential for many computations. Multiplication by a constant produces a change in the slope, or gain, of the input–output relationship, amplifying or scaling down the sensitivity of the neuron to changes in its input. Such gain modulation occurs in vivo, during contrast invariance of orientation tuning, attentional scaling, translation-invariant object recognition, auditory processing and coordinate transformations. Moreover, theoretical studies highlight the necessity of gain modulation in several of these tasks. Although potential cellular mechanisms for gain modulation have been identified, they often rely on membrane noise and require restrictive conditions to work. 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 the dynamic-clamp technique to investigate gain modulation in granule cells in acute slices of rat cerebellum. 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 relationship into multiplicative gain changes. When granule cells were driven with bursts of high-frequency mossy fibre input, as observed in vivo, larger inhibition-mediated gain changes were observed, as expected with greater STD. Simulations of synaptic integration in more complex neocortical neurons suggest 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.

neuroConstruct: a tool for modeling networks of neurons in 3D space

Summary Conductance-based neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard many 3D anatomical features of the brain. Here, we describe a new software application, neuroConstruct, that facilitates the creation, visualization, and analysis of networks of multicompartmental neurons in 3D space. A graphical user interface allows model generation and modification without programming. Models within neuroConstruct are based on new simulator-independent NeuroML standards, allowing automatic generation of code for NEURON or GENESIS simulators. neuroConstruct was tested by reproducing published models and its simulator independence verified by comparing the same model on two simulators. We show how more anatomically realistic network models can be created and their properties compared with experimental measurements by extending a published 1D cerebellar granule cell layer model to 3D.

Structural features of a close homologue of L1 (CHL1) in the mouse: a new member of the L1 family of neural recognition molecules

We have identified a close homologue of L1 (CHL1) in the mouse. CHL1 comprises an N‐terminal signal sequence, six immunoglobulin (Ig)‐like domains, 4.5 fibronectin type III (FN)‐like repeats, a transmembrane domain and a C‐terminal, most likely intracellular domain of ˜100 amino acids. CHL1 is most similar in its extracellular domain to chicken Ng‐CAM (˜40% amino acid identity), followed by mouse L1, chicken neurofascin, chicken Nr‐CAM, Drosophila neuroglian and zebrafish L1.l (37‐28% amino acid identity), and mouse F3, rat TAG‐1 and rat BIG‐1 (˜27% amino acid identity). The similarity with other members of the Ig superfamily [e.g. neural cell adhesion molecule (N‐CAM), DCC, HLAR, rse] is 16‐11%. The intracellular domain is most similar to mouse and chicken Nr‐CAM, mouse and rat neurofascin (˜60% amino acid identity) followed by chicken neurofascin and Ng‐CAM, Drosophila neuroglian and zebrafish L1.l and L1.2 (˜40% amino acid identity). Besides the high overall homology and conserved modular structure among previously recognized members of the L1 family (mouse/human L1/rat NILE; chicken Ng‐CAM; chicken/mouse Nr‐CAM; Drosphila neuroglian; zebrafish L1.l and L1.2; chicken/mouse neurofascin/rat ankyrin‐binding glycoprotein), criteria characteristic of L1 were identified with regard to the number of amino acids between positions of conserved amino acid residues defining distances within and between two adjacent Ig‐like domains and FN‐like repeats. These show a collinearity in the six Ig‐like domains and four adjacent FN‐like repeats that is remarkably conserved between L1 and molecules containing these modules (designated the L1 family cassette), including the GPI‐linked forms of the F3 subgroup (mouse F3/chicken F1l/human CNTN1; rat BIG‐l/mouse PANG; rat TAG‐l/mouse TAX‐l/chicken axonin‐1). The colorectal cancer molecule (DCC), previously introduced as an N‐CAM‐like molecule, conforms to the L1 family cassette. Other structural features of CHL l shared between members of the L1 family are a high degree of N‐glycosidically linked carbohydrates (˜20% of its molecular mass), which include the HNK‐1 carbohydrate structure, and a pattern of protein fragments comprising a major 185 kDa band and smaller fragments of 165 and 125 kDa. As for the other L1 family members, predominant expression of CHL l is observed in the nervous system and at later developmental stages. In the central nervous system CHL l is expressed by neurons, but, in contrast to L1, also by glial cells. Our findings suggest a common ancestral L1‐like molecule which evolved via gene duplication to generate a diversity of structurally and functionally distinct yet similar molecules.

PATTERNS AND PAUSES IN PURKINJE CELL SIMPLE SPIKE TRAINS: EXPERIMENTS, MODELING AND THEORY

We review our recent experimental and modeling results on how cerebellar Purkinje cells encode information in their simple spike trains and present a theory of the function of pauses and regular spiking patterns. The regular spiking patterns were discovered in extracellular recordings of simple spikes in awake and anesthetized rodents, where it was shown that more than half of the spontaneous activity consists of short epochs of regular spiking. These periods of regular spiking are interrupted by pauses, which can be tightly synchronized among nearby Purkinje cells, while the spikes in the regular patterns are not. Interestingly, pauses are affected by long-term depression of the parallel fiber synapses. Both in modeling and slice experiments it was demonstrated that long-term depression causes a decrease in the duration of pauses, leading to an increase of the spike output of the neuron. Based on these results we propose that pauses in the simple spike train form a temporal code which can lead to a rebound burst in the target deep cerebellar nucleus neurons. Conversely, the regular spike patterns may be a rate code, which presets the amplitude of future rebound bursts.

Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70xa0mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.

Cerebellar output controls generalized spike-and-wave discharge occurrence

Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike‐and‐wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures.

A Biophysical Model of Synaptic Delay Learning and Temporal Pattern Recognition in a Cerebellar Purkinje Cell

It has been suggested that information in the brain is encoded in temporal spike patterns which are decoded by a combination of time delays and coincidence detection. Here, we show how a multi-compartmental model of a cerebellar Purkinje cell can learn to recognise temporal parallel fibre activity patterns by adapting latencies of calcium responses after activation of metabotropic glutamate receptors (mGluRs). In each compartment of our model, the mGluR signalling cascade is represented by a set of differential equations that reflect the underlying biochemistry. Phosphorylation of the mGluRs changes the concentration of receptors which are available for activation by glutamate and thereby adjusts the time delay between mGluR stimulation and voltage response. The adaptation of a synaptic delay as opposed to a weight represents a novel non-Hebbian learning mechanism that can also implement the adaptive timing of the classically conditioned eye-blink response.

The Open Source Brain Initiative: enabling collaborative modelling in computational neuroscience

While an increasing number of biophysically detailed neuronal models (featuring (semi-) realistic morphologies and voltage and ligand gated conductances) are being shared across the community through resources like ModelDB, these usually only represent a snapshot of the model at the time of publication, in a format specific to the original simulator used. Models are constantly evolving however, to take account of new experimental findings and to address new research questions, both by the original modellers, and by other researchers who help provide quality control/debugging of original scripts and convert the model (components) for use in other simulators. This crucial part of the model life cycle is not well addressed with currently available infrastructure. The Open Source Brain (OSB) repository is being developed to provide a central location for researchers to collaboratively develop models which can be run across multiple simulators and can interact with the range of other applications in the NeuroML “ecosystem”. NeuroML [1] is a simulator independent language for expressing detailed single cell and network models, which is supported by an increasing number of applications for generating, visualising, simulating and analysing such models as well as by databases providing the base components (e.g. reconstructed morphologies, ion channels) for use in the models (http://www.neuroml. org/tool_support). The OSB repository differs from existing model databases which have traditionally concentrated on frozen, published models. The cell, ion channel, synapse and network models in this repository develop over time to ensure they reflect best practices in neurophysiological modelling and allow continuously improving, bug-free simulations. Multiple views of the model elements are available to encourage feedback from modellers, theoreticians and experimentalists. Links can be made to previous versions of the models in ModelDB, and deep links will be used to ensure cross referencing to other neuroinformatics resources such as NeuroMorpho and NeuroLex. The system is based around a Mercurial version control repository with models organised into projects illustrating a number of neurophysiologically relevant aspects of the cell and network behaviour. The history is recorded of all changes to each project by contributors who can be distributed worldwide. There is close integration with the application neuroConstruct [2], allowing the models to be examined with a 3D graphical user interface, and scripts automatically generated for use on a number of widely used neuronal simulators. A number of models are already available in the repository, including cell and network models from the cerebellum, detailed cortical and hippocampal pyramidal cell models and a 3D version of a single column thalamocortical network model [3]. While most of the models available are conversions of existing published models, some have been developed during original research projects using the tools and formats discussed here [4]. The repository is currently in alpha stage of development and is being tested with a small number of labs. The resource can be accessed at http://opensourcebrain.org:8080. This work has been primarily funded by the Wellcome Trust

STD-Dependent and Independent Encoding of Input Irregularity as Spike Rate in a Computational Model of a Cerebellar Nucleus Neuron

Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

2013 Special Issue: Modeling the generation of output by the cerebellar nuclei

Functional aspects of network integration in the cerebellar cortex have been studied experimentally and modeled in much detail ever since the early work by theoreticians such as Marr, Albus and Braitenberg more than 40xa0years ago. In contrast, much less is known about cerebellar processing at the output stage, namely in the cerebellar nuclei (CN). Here, input from Purkinje cells converges to control CN neuron spiking via GABAergic inhibition, before the output from the CN reaches cerebellar targets such as the brainstem and the motor thalamus. In this article we review modeling studies that address how the CN may integrate cerebellar cortical inputs, and what kind of signals may be transmitted. Specific hypotheses in the literature contrast rate coding and temporal coding of information in the spiking output from the CN. One popular hypothesis states that post-inhibitory rebound spiking may be an important mechanism by which Purkinje cell inhibition is turned into CN output spiking, but this hypothesis remains controversial. Rate coding clearly does take place, but in what way it may be augmented by temporal codes remains to be more clearly established. Several candidate mechanisms distinct from rebound spiking are discussed, such as the significance of spike time correlations between Purkinje cell pools to determine CN spike timing, irregularity of Purkinje cell spiking as a determinant of CN firing rate, and shared brief pauses between Purkinje cell pools that may trigger individual CN spikes precisely.