Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107: 1756-1775, 2012. First published December 7, 2011; doi:10.1152/jn.00408.2011.-Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.e., membrane time constant, reversal potential, and capacitance), the spike-triggered adaptation currents, as well as the dynamics of the action potential threshold. The stochastic model that results from our maximum likelihood approach accurately predicts the spike times, the subthreshold voltage, the firing patterns, and the type of frequency-current curve. Extracting the model parameters for three cortical cell types revealed that cell types show highly significant differences in the time course of the spike-triggered currents and moving threshold, that is, in their adaptation and refractory properties but not in their passive properties. In particular, GABAergic fast-spiking neurons mediate weak adaptation through spike-triggered currents only, whereas regular spiking excitatory neurons mediate adaptation with both moving threshold and spike-triggered currents. GABAergic nonfast-spiking neurons combine the two distinct adaptation mechanisms with reduced strength. Differences between cell types are large enough to enable automatic classification of neurons into three different classes. Parameter extraction is performed for individual neurons so that we find not only the mean parameter values for each neuron type but also the spread of parameters within a group of neurons, which will be useful for future large-scale computer simulations.

Complexity and performance in simple neuron models

Wulfram Gerstner, Skander Mensi, Richard Naud

The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. In neurons, stimulated by a time-dependent current or conductance, we want to predict precisely the timing of spikes and the sub-threshold voltage. During the last years several models have been tested on this type of data but never compared with the same protocol. One of the major outcome is that, from a certain degree of complexity, all are very efficient and gave statistically indistinguishable results. We studied a class of integrate-and-fire models (IF), with each member of the class implementing a selection of possible improvements: exponential voltage non-linearity1, spike-triggered adaptation current2, spike-triggered change in conductance, moving threshold3, sub-threshold voltage-dependent currents4. Each refinement adds a new term to the equations of the IF model. This IF family is extendable and adaptable to different neuron types and is able to deal with complex neural activities (i.e. adaptation, facilitation, bursting, relative refractoriness, ...). To systematically explore the effects of a given term of the model a new fitting procedure based on linear regression of voltage change5 is used. This method is fast, robust and allows the extraction of all the models parameters from a few seconds recordings of fluctuating injected current and membrane potential with hundreds spikes without any prior knowledge. To investigate the effect of our modifications, we used as training data three different Hodgkin-Huxley-like models, and two experimental recordings of fast spiking and regular spiking cells and then evaluate each IF model on a given test set. We observe that it is possible to fit a model that can reproduce the activity of neurons with high reliability (i.e. almost 100 % of the spike time and less than 1 mV of sub-threshold voltage difference). Using this framework one can classify IF models in terms of complexity and performance and evaluate the importance of each term for different stimulation paradigms.

2010

Structure and dynamics of the neocortical microcircuit connectivity

Jean-Vincent Le Bé

The neocortex is the most computationally advanced portion of the brain. It is currently assumed to be composed of a large number of "cortical columns" – intricate arrangements of cortical neurons approximately 300-500 µm in diameter and 2-5 mm in height in humans – that might serve as the elementary computational unit of the neocortex. Understanding the computation performed by this microcircuit is one of the keys to our comprehension of the brain. The so-called cortical column is not a static entity, however, and it evolves throughout a lifetime and continually adapts to the information from its cortical environment. Despite the differences between cortical columns across the cortex, a number of common features have been identified: a laminar structure, the dynamics of connections between identified neurons or the mechanisms for these connections to be modified (that give its specificity to each microcircuit). This thesis presents the description of the differential connectivity and synaptic dynamics across cell populations and the long term neuronal rewiring in a particular neuronal population within the cortical column. Somatic whole cell recordings have been performed to probe the connectivity, synaptic dynamics and plasticity of the connections in the rat neocortex. Two populations of layer V pyramidal neurons were studied in particular: cortico-callosally projecting pyramidal cells (CCPs) and thick tufted pyramidal cells (TTCs). The first major results from this work revealed the degree of connectivity and the linear dynamics of the CCPs population when compared to the TTCs. CCPs have nearly 4 times fewer interconnections and subsequent post-synaptic potentials were less decreasing in amplitude along a pre-synaptic series of action potentials. Long term configuration of TTC networks was explored. These experiments show for the first time the emergence of new functional synaptic connections between TTCs within hours. Activation of the slice by glutamate greatly increases their rate of emergence and this work demonstrated that metabotropic glutamate receptor 5 (mGluR5) activation and action potential firing are required for new connections to be formed in this experimental protocol. Newly formed connections respond in a more linear fashion and have weaker post-synaptic influence than already existing connections. Pre-existing connections are also modified after stimulation, requiring mGluR5, action potentials as well as α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors activation. The activation of group III metabotropic glutamate receptors (mGluRs) however results in a decrease in the strength of connections. Finally, the influence of inhibitory interneurons on the activity and connectivity between TTCs was also investigated. The results of this study show that firing of inhibitory interneurons can be triggered by the input of only one pyramidal cell. They further show that stimulation of a single TTC can result in an hyperpolarization of the post-synaptic TTC mediated by an interneurone. When the pre-synaptic neuron is also directly connected to the post-synaptic neuron with an excitatory synapse, the indirect inhibitory connection serves to curtail the excitatory response. This work has provided a new insight into the dynamic nature of the cortical microcircuitry, showing that it evolves rapidly and can adapt, reconfigure and rewire itself in remarkably short time-spans. It also describes the variety of dynamics exhibited by the different types of pyramidal cells, due to either the projecting site specificity or to the action of an intermediate interneuron.

EPFL2007

Optogenetic Probing of Cortical Synaptic Circuits Measured Through in Vivo Whole-Cell Recordings in the Mouse Barrel Cortex

Céline Matéo

Although neocortex underlies higher-order brain functions, little is known about the synaptic interactions that drive neocortical microcircuit function in vivo. The neocortex is spontaneously active in vivo so I explored how such spontaneous network activity affects in vivo the interactions between excitatory and inhibitory neurons in the barrel cortex. To this aim, I have focused on the layer 2/3 microcircuit of the C2 barrel column driving sensory processing for the C2 whisker, and combined in vivo whole-cell patch-clamp recordings in anesthetised and awake head-restrained mice with optogenetic stimulation, that allows controlling the activity of genetically defined neurons with light. I expressed channelrhodopsin-2 (ChR2), a light-gated cation channel to evoke action potentials in excitatory neurons with brief light pulses. The impact of this optogenetic stimulus was measured on the membrane potential dynamics of their postsynaptic target neurons. The optogenetic stimulus drove reliable action potentials in ChR2-expressing excitatory neurons, independent of the state of ongoing spontaneous cortical activity. In contrast, I found a marked modulation of light-evoked postsynaptic responses in ChR2-non-expressing excitatory neurons by cortical activity. During spontaneously hyperpolarised periods (DOWN state), optogenetic stimulation resulted in depolarisations of postsynaptic excitatory neurons, while during depolarised periods (UP state), the response was significantly smaller and shorter. The optogenetic stimulus drove the membrane potential of postsynaptic excitatory layer 2/3 neurons towards reversal potentials, which were hyperpolarised relative to action potential threshold, leading to a reduction in firing rates. Targeting whole-cell recordings to GFP-labelled GABAergic layer 2/3 neurons with two-photon microscopy revealed that both fast-spiking and non-fast-spiking inhibitory postsynaptic neurons were rapidly driven to fire action potentials, preferentially from UP states. Such rapid activation of inhibitory neurons in the UP state is likely to be responsible for decreasing firing rate in excitatory neurons. As a result, optogenetic stimulation of a given set of excitatory neurons results in a state-dependent recruitment of inhibitory neurons that limit the spread of excitation within the layer 2/3 microcircuit. The data presented here indicates that a powerful inhibitory loop within the cortical microcircuit rapidly balances excitation with inhibition and enforces sparse coding of layer 2/3 excitatory neurons.

EPFL2011