Calcium, synaptic plasticity and intrinsic homeostasis in purkinje neuron models.

We recently reproduced the complex electrical activity of a Purkinje cell (PC) with very different combinations of ionic channel maximum conductances, suggesting that a large parameter space is available to homeostatic mechanisms. It has been hypothesized that cytoplasmic calcium concentrations control the homeostatic activity sensors. This raises many questions for PCs since in these neurons calcium plays an important role in the induction of synaptic plasticity. To address this question, we generated 148 new PC models. In these models the somatic membrane voltages are stable, but the somatic calcium dynamics are very variable, in agreement with experimental results. Conversely, the calcium signal in spiny dendrites shows only small variability. We demonstrate that this localized control of calcium conductances preserves the induction of long-term depression for all models. We conclude that calcium is unlikely to be the sole activity-sensor in this cell but that there is a strong relationship between activity homeostasis and synaptic plasticity.

Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.

The increase in available computational power and the higher quality of experimental recordings have turned the tuning of neuron model parameters into a problem that can be solved by automatic global optimization algorithms. Neurofitter is a software tool that interfaces existing neural simulation software and sophisticated optimization algorithms with a new way to compute the error measure. This error measure represents how well a given parameter set is able to reproduce the experimental data. It is based on the phase-plane trajectory density method, which is insensitive to small phase differences between model and data. Neurofitter enables the effortless combination of many different time-dependent data traces into the error measure, allowing the neuroscientist to focus on what are the seminal properties of the model.We show results obtained by applying Neurofitter to a simple single compartmental model and a complex multi-compartmental Purkinje cell (PC) model. These examples show that the method is able to solve a variety of tuning problems and demonstrate details of its practical application.

Complex parameter landscape for a complex neuron model.

The electrical activity of a neuron is strongly dependent on the ionic channels present in its membrane. Modifying the maximal conductances from these channels can have a dramatic impact on neuron behavior. But the effect of such modifications can also be cancelled out by compensatory mechanisms among different channels. We used an evolution strategy with a fitness function based on phase-plane analysis to obtain 20 very different computational models of the cerebellar Purkinje cell. All these models produced very similar outputs to current injections, including tiny details of the complex firing pattern. These models were not completely isolated in the parameter space, but neither did they belong to a large continuum of good models that would exist if weak compensations between channels were sufficient. The parameter landscape of good models can best be described as a set of loosely connected hyperplanes. Our method is efficient in finding good models in this complex landscape. Unraveling the landscape is an important step towards the understanding of functional homeostasis of neurons.

Perinatal maturation of the respiratory rhythm generator in mammals: from experimental results to computational simulation.

The survival of neonatal mammals requires a correct function of the respiratory rhythm generator (RRG), and therefore, the processes that control its prenatal maturation are of vital importance. In humans, lambs and rodents, foetal breathing movements (FBMs) occur early during gestation, are episodic, sensitive to bioamines, central hypoxia and inputs from CNS upper structures, and evolve with developmental age. In vitro, the foetal rodent RRG studied in preparations where the upper CNS structures are lacking continuously produces a rhythmic command, which is sensitive to hypoxia and bioaminergic inputs. The rhythm is slow with variable periods 4 days before birth. It becomes faster 2 days before birth, similar to the postnatal rhythm. Compelling evidence suggests that a region of the RRG called the preBötzinger complex (PBC) contains respiratory pacemaker neurones which play a primary role in perinatal rhythmogenesis. Although the RRG functions during early gestation, no pacemakers are found in the putative PBC area and its electrical stimulation and lesion do not affect the early foetal rhythm. To know whether the early foetal and perinatal rhythms originate from either pacemaker neurones or network connection properties, and to know which maturational processes might explain the appearance of PBC pacemakers and the rhythm increase during perinatal development, we computationally modelled maturing RRG. Our model shows that both network noise and persistent sodium conductance are crucial for rhythmogenesis and that a slight increase in the persistent sodium conductance can solve the pacemaker versus network dilemma in a noisy network.

Biophysically detailed modelling of microcircuits and beyond.

Realistic bottom-up modelling has been seminal to understanding which properties of microcircuits control their dynamic behaviour, such as the locomotor rhythms generated by central pattern generators. In this article of the TINS Microcircuits Special Feature, we review recent modelling work on the leech-heartbeat and lamprey-swimming pattern generators as examples. Top-down mathematical modelling also has an important role in analyzing microcircuit properties but it has not always been easy to reconcile results from the two modelling approaches. Most realistic microcircuit models are relatively simple and need to be made more detailed to represent complex processes more accurately. We review methods to add neuromechanical feedback, biochemical pathways or full dendritic morphologies to microcircuit models. Finally, we consider the advantages and challenges of full-scale simulation of networks of microcircuits.