25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Structure-Dynamics relationship in basalganglia: Implications for brain function

In this thesis, I have used a combination of computational models such as mean field and spikingnetwork simulations to study various sub-circuits of basal ganglia. I first studied the striatum(chapter 2), which is the input nucleus of basal ganglia. The two types of Medium SpinyNeurons (MSNs), D1 and D2-MSNs, together constitute 98% of the neurons in striatum. Thecomputational models so far have treated striatum as a homogenous unit and D1 and D2 MSNs asinterchangeable subpopulations. This implied that a bias in a Go/No-Go decision is enforced viaexternal agents to the striatum (eg. cortico-striatal weights), thereby assigning it a passive role.New data shows that there is an inherent asymmetry in striatal circuits. In this work, I showedthat striatum due to its asymmetric connectivity acts as a decision transition threshold devicefor the incoming cortical input. This has significant implications on the function of striatum asan active participant in influencing the bias towards a Go/No-Go decision. The striatal decisiontransition threshold also gives mechanistic explanations for phenomena such as L-Dopa InducedDyskinesia (LID), DBS-induced impulsivity, etc. In chapter 3, I extend the mean field model toinclude all the nuclei of basal ganglia to specifically study the role of two new subpopulationsfound in GPe (Globus Pallidus Externa). Recent work shows that GPe, also earlier consideredto be a homogenous nucleus, has at least two subpopulations which are dichotomous in theiractivity with respect to the cortical Slow Wave (SWA) and beta activity. Since the data for thesesubpopulations are missing, a parameter search was performed for effective connectivities usingGenetic Algorithms (GA) to fit the available experimental data. One major result of this studyis that there are various parameter combinations that meet the criteria and hence the presenceof functional homologs of the basal ganglia network for both pathological (PD) and healthynetworks is a possibility. Classifying all these homologous networks into clusters using somehigh level features of PD shows a large variance, hinting at the variance observed among the PDpatients as well as their response to the therapeutic measures. In chapter 4, I collaborated on aproject to model the role of STN and GPe burstiness for pathological beta oscillations as seenduring PD. During PD, the burstiness in the firing patterns of GPe and STN neurons are shownto increase. We found that in the baseline state, without any bursty neurons in GPe and STN,the GPe-STN network can transition to an oscillatory state through modulating the firing ratesof STN and GPe neurons. Whereas when GPe neurons are systematically replaced by burstyneurons, we found that increase in GPe burstiness enforces oscillations. An optimal % of burstyneurons in STN destroys oscillations in the GPe-STN network. Hence burstiness in STN mayserve as a compensatory mechanism to destroy oscillations. We also propose that bursting inGPe-STN could serve as a mechanism to initiate and kill oscillations on short time scales, asseen in the healthy state. The GPe-STN network however loses the ability to kill oscillations inthe pathological state.QC 20160509</p

Existence and Control of Go/No-Go Decision Transition Threshold in the Stratium

A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity

Functionally classifying an ensemble of healthy and pathological basal ganglia network models

Our understanding of the circuitry of the basal ganglia has been refined in recent years due to thediscovery of sub-populations within nuclei previously assumed to be homogeneous or/and additional novel projectionsbetween the existing nuclei. Although, computational models can be used to understand the implications of these structural developments, the lack of data about effective connectivities pose difficulty to their use. Faced with this high degree of under-specification, one can either choose to make simplifying assumptions on the unknown parameters or strive for an unique or locally optimal solution by performing an extensive parameter fit. We propose a yet another alternative, where we perform an extensive parameter search but instead of striving for an unique solution, we embrace the uncertainty and generate a large ensemble of network configurations for both healthy and PD conditions. In order to deal with the high dimensionality of the solution space, we then re-project these solutions on a reduced space of sensible functional features. We use the recent discovery of two GPe subpopulations: arkypallidal and prototypical neurons [1] as an example of a novel structural development. The phase and firing rate relationships in [1] were used to generate many valid network models for PD and healthy conditions. These ensembles of healthy and pathological network models were then classified on the basis of dynamical properties, namely the ability to suppress activity in GPi (GS) and susceptibility to oscillations (SO). These dynamical features were used since they are known to be network analogs of PD, i.e. insufficient suppression of GPi is associated with stymied movement [2] and akinetic symptoms of PD patients have been shown to grow worse with an imposed low frequency stimulation of 10-20Hz in STN [3]. Such a functional classification reveals a distinct separation between the PD and healthy ensembles, with majority of PD networks showing insufficient suppression of GPi activity and high susceptibility to oscillations and healthy networks showing high suppression of GPi and low susceptibility to oscillations. There are however some PD and healthy networks that overlap in this functional space. We propose that such an approach where multiple network models are generated and then projected to a lower-dimensional functional space gives a better chance at understanding a complex pathology like PD which involves deficits in multiple pathways in the basal ganglia. 1.Abdi, a., Mallet, N., Mohamed, F. Y., Sharott, a., Dodson, P. D., Nakamura, K. C., et al. (2015). Prototypicand Arkypallidal Neurons in the Dopamine-Intact External Globus Pallidus. Journal of Neuroscience 35,6667–6688. doi:10.1523/JNEUROSCI.4662-14.2015 2. Boraud, T., Bezard, E., Bioulac, B., and Gross, C. E. (2000). Ratio of inhibited-to-activated pallidalneurons decreases dramatically during passive limb movement in the MPTP-treated monkey. Journal ofneurophysiology 83, 1760–17633. Timmermann, L., Wojtecki, L., Gross, J., Lehrke, R., Voges, J., Maarouf, M., et al. (2004). Ten-hertzstimulation of subthalamic nucleus deteriorates motor symptoms in Parkinson’s disease. MovementDisorders 19, 1328–1333. doi:10.1002/mds.2019