26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types.

GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we demonstrate that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or cancelled

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data

For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ('in-vivo-like') input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a 'high-throughput' model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available

Anatomical and synaptic properties.

<p>(A) Laminar structure of a single network column. Arrow widths represent relative strength of connections (black: excitatory, gray: inhibitory), i.e. the product of connection probability and synaptic peak conductance. (B) Left panel: Distribution of three different short-term plasticity types over different combinations of pre- and postsynaptic neuron types. Arrows from or to one of the shaded blocks (rather than from or to a single neuron type) denote connection types that are identical for all excitatory (PC) or inhibitory (IN) neurons. Where all three types are drawn, they are randomly distributed over all synapses between these two neuron types according to the probabilities given in the figure. Right panel: Illustration of the postsynaptic potential in response to a series of presynaptic spikes for three types of short-term synaptic plasticity for excitatory (E1 to E3) and inhibitory synapses (I1 to I3).</p

Short-term synaptic plasticity.

<p>Short-term synaptic plasticity.</p

Effect of synaptic parameter changes on network activity.

<p>(A) Maximum of the Kolmorov-Smirnov test statistics (<i>D</i>_KS) comparing the three experimental and simulated distributions (black) and standard deviation of the simulated membrane potential (gray) for different GABA_A reversal potentials. Each data point is the mean ± SEM over several values of input currents. The black line denotes the <i>D</i>_KS limit of 0.4 above which differences become significant (p ≤ 0.05), and the gray line marks the average of the experimentally observed standard deviations (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004930#pcbi.1004930.g004" target="_blank">Fig 4A</a>). (B) <i>D</i>_KS values as a function of percent change in overall synaptic peak conductances between pyramidal cells (E) and interneurons (I). The dotted line denotes the critical <i>D</i>_KS value of 0.4 (see above). (C) <i>D</i>_KS values for different values of the standard deviation of the synaptic peak conductances using either the original log-normal distribution (gray curve) or a Gaussian distribution with the same mean and standard deviation (black curve). As above, the dotted line marks the critical <i>D</i>_KS value of 0.4. In all figures, each data point shows the mean ± SEM over the <i>D</i>_KS values for a number of different input currents.</p

Dependence of network behavior on the magnitude of synaptic background inputs.

<p>(A) Maximum of the Kolmogorov-Smirnov test statistic (<i>D</i>_KS) comparing the experimental and respective simulated distributions for the mean ISI, C_<i>V</i>, and cross-correlation as a function of input currents into excitatory (<i>I</i>_ex) and inhibitory (<i>I</i>_inh) neurons in layer 2/3. <i>D</i>_KS values within the blackly delineated area have <i>p</i> values larger than 0.05 for each of the three tests. The insets show the three individual <i>D</i>_KS values as a function of one of these input currents alone (for <i>I</i>_inh = 200 pA in the left and <i>I</i>_ex = 400 pA in the lower inset, indicated by the white dotted lines). <i>D</i>_KS values above 0.4 (green lines) correspond to significant (<i>p</i> = 0.05) deviations from experiments in the given distribution. The red asterisk indicates the parameter set used for the simulations presented in the previous figures. (B) Fraction of neurons emitting at least 10 spikes during a 30 sec simulation period for the same currents used in Panel A. The blackly delineated area was copied from Panel A and superimposed on the current graph. (C) Ratio of the number of spiking pyramidal cells between layers 5 and 2/3 as a function of the input current ratio into pyramidal cells in layers 2/3 and 5. Each data point represents the mean ± SEM over three different ratios of input currents into interneurons in layers 2/3 and 5 and a number of and values.</p

Synaptic parameters.

<p>Synaptic parameters.</p