The Effects of NMDA Subunit Composition on Calcium Influx and Spike Timing-Dependent Plasticity in Striatal Medium Spiny Neurons

Calcium through NMDA receptors (NMDARs) is necessary for the long-term potentiation (LTP) of synaptic strength; however, NMDARs differ in several properties that can influence the amount of calcium influx into the spine. These properties, such as sensitivity to magnesium block and conductance decay kinetics, change the receptor's response to spike timing dependent plasticity (STDP) protocols, and thereby shape synaptic integration and information processing. This study investigates the role of GluN2 subunit differences on spine calcium concentration during several STDP protocols in a model of a striatal medium spiny projection neuron (MSPN). The multi-compartment, multi-channel model exhibits firing frequency, spike width, and latency to first spike similar to current clamp data from mouse dorsal striatum MSPN. We find that NMDAR-mediated calcium is dependent on GluN2 subunit type, action potential timing, duration of somatic depolarization, and number of action potentials. Furthermore, the model demonstrates that in MSPNs, GluN2A and GluN2B control which STDP intervals allow for substantial calcium elevation in spines. The model predicts that blocking GluN2B subunits would modulate the range of intervals that cause long term potentiation. We confirmed this prediction experimentally, demonstrating that blocking GluN2B in the striatum, narrows the range of STDP intervals that cause long term potentiation. This ability of the GluN2 subunit to modulate the shape of the STDP curve could underlie the role that GluN2 subunits play in learning and development

A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

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

Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues

Modeling and simulations have gained a leading position in contemporary attempts to describe, explain, and quantitatively predict the human brain’s operations. Computer models are highly sophisticated tools developed to achieve an integrated knowledge of the brain with the aim of overcoming the actual fragmentation resulting from different neuroscientific approaches. In this paper we investigate the plausibility of simulation technologies for emulation of consciousness and the potential clinical impact of large-scale brain simulation on the assessment and care of disorders of consciousness (DOCs), e.g., Coma, Vegetative State/Unresponsive Wakefulness Syndrome, Minimally Conscious State. Notwithstanding their technical limitations, we suggest that simulation technologies may offer new solutions to old practical problems, particularly in clinical contexts. We take DOCs as an illustrative case, arguing that the simulation of neural correlates of consciousness is potentially useful for improving treatments of patients with DOCs

Diffusive homeostasis retains its properties in networks with STDP.

<p><b>(A)</b> Distributions of steady-state synaptic weights after homeostasis and STDP. <b>(B)</b> (i) Distributions of steady-state firing rates after homeostasis and STDP. (ii) Distribution of inter-spike intervals (ISI) at the steady state (log scale). (iii) Distribution of the coefficient of variation (CV) of ISIs at the steady state. (iv) Average autocorrelogram of the network at the steady state, given by the rate of coincident spikes in each time bin. <b>(C)</b> Mean response linearity (measured by <i>R</i>² values) for networks with STDP and each case of homeostasis (n = 9 networks, error bars represent one standard deviation). Crosshatched bars show mean response linearity for networks with each case of homeostasis and without STDP, using weight matrices obtained by shuffling the final weight matrix of networks with STDP and without homeostasis. Surrounding plots show firing rate changes Δ<i>ν</i> of all neurons following input changes Δ<i>μ</i> for an example network in each case (black line shows linear fit).</p

Steady-state firing thresholds of diffusive and non-diffusive homeostasis.

<p><b>(A)</b> Distributions of firing thresholds after homeostasis from network simulations, receiving independent Poisson input drawn from a Gaussian distribution. <b>(B)</b> Steady-state firing thresholds plotted against external inputs received during homeostasis. <b>(C)</b> Variance of the steady-state firing rate distribution as the diffusion coefficient <i>D</i> is varied (<i>D</i> = 1000 <i>μ</i>m²s⁻¹ in panels A-B). <b>(D)</b> Variance of the steady-state firing rate distribution as the input rate width is varied, for networks with diffusive (<i>D</i> = 1000 <i>μ</i>m²s⁻¹) and non-diffusive homeostasis (input rate width = 10 Hz in panels A-B).</p

Steady-state behavior of diffusive and non-diffusive homeostasis.

<p><b>(A)</b> Schematic of the sparsely connected recurrent network model. Neurons received homogeneous random spiking input (<i>g</i>_ext). <b>(B)</b> Intracellular homeostatic signals in a model neuron. Each spike triggers calcium influx, which leads to nNOS activation and NO synthesis. <b>(C)</b> Mean population firing rates for networks with diffusive or non-diffusive homeostasis after an increase in external input (red triangle). Spatial distribution of NO concentrations at different times across the network with diffusion are shown below. (<b>D</b>–<b>E</b>) Distributions of firing rates and log firing rates (insets) after homeostasis from network simulations (D) and mean-field analysis (E), both receiving independent Poisson inputs drawn from a Gaussian distribution. <b>(F)</b> Distributions of firing rates in the mean-field analysis for low and high covariance <i>σ</i> of threshold and input rate.</p

Illustration of the effects of non-diffusive (i) and diffusive (ii) homeostasis.

<p>Non-diffusive homeostasis adjusts each neuron’s threshold (red color bar) according to its input to give identical firing rates, while diffusive homeostasis induces correlations (blue cloud) in the thresholds of neighboring neurons, thereby maintaining diverse firing rates.</p