Albert Gidon

Reconstruction and Simulation of Neocortical Microcircuitry

UNLABELLED We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. PAPERCLIP VIDEO ABSTRACT.

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

The role of dendritic inhibition in shaping the plasticity of excitatory synapses

Using computational tools we explored the impact of local synaptic inhibition on the plasticity of excitatory synapses in dendrites. The latter critically depends on the intracellular concentration of calcium, which in turn, depends on membrane potential and thus on inhibitory activity in particular dendritic compartments. We systematically characterized the dependence of excitatory synaptic plasticity on dendritic morphology, loci and strength, as well as on the spatial distribution of inhibitory synapses and on the level of excitatory activity. Plasticity of excitatory synapses may attain three states: “protected” (unchanged), potentiated (long-term potentiation; LTP), or depressed (long-term depression; LTD). The transition between these three plasticity states could be finely tuned by synaptic inhibition with high spatial resolution. Strategic placement of inhibition could give rise to the co-existence of all three states over short dendritic branches. We compared the plasticity effect of the innervation patterns typical of different inhibitory subclasses—Chandelier, Basket, Martinotti, and Double Bouquet—in a detailed model of a layer 5 pyramidal cell. Our study suggests that dendritic inhibition plays a key role in shaping and fine-tuning excitatory synaptic plasticity in dendrites.

Contribution of Intracolumnar Layer 2/3-to-Layer 2/3 Excitatory Connections in Shaping the Response to Whisker Deflection in Rat Barrel Cortex

This computational study integrates anatomical and physiological data to assess the functional role of the lateral excitatory connections between layer 2/3 (L2/3) pyramidal cells (PCs) in shaping their response during early stages of intracortical processing of a whisker deflection (WD). Based on in vivo and in vitro recordings, and 3D reconstructions of connected pairs of L2/3 PCs, our model predicts that: 1) AMPAR and NMDAR conductances/synapse are 0.52 ± 0.24 and 0.40 ± 0.34 nS, respectively; 2) following WD, connection between L2/3 PCs induces a composite EPSPs of 7.6 ± 1.7 mV, well below the threshold for action potential (AP) initiation; 3) together with the excitatory feedforward L4-to-L2/3 connection, WD evoked a composite EPSP of 16.3 ± 3.5 mV and a probability of 0.01 to generate an AP. When considering the variability in L4 spiny neurons responsiveness, it increased to 17.8 ± 11.2 mV; this 3-fold increase in the SD yielded AP probability of 0.35; 4) the interaction between L4-to-L2/3 and L2/3-to-L2/3 inputs is highly nonlinear; 5) L2/3 dendritic morphology significantly affects L2/3 PCs responsiveness. We conclude that early stages of intracortical signaling of WD are dominated by a combination of feedforward L4–L2/3 and L2/3–L2/3 lateral connections.

Biophysics of Synaptic Inhibition in Dendrites

This chapter aims at investigating the functional implications of the biologically realistic and widespread case in which a single inhibitory axon forms multiple (10–20) synaptic contacts on the dendrites of its target neuron. We analyzed the impact of multi-site dendritic inhibition on the neurons’ output and, thus, gained several counterintuitive insights into the biophysical and functional implications of such connectivity pattern. In the course of the chapter, we propose a functional role for very distal dendritic inhibition; demonstrate the regional effect of multiple, rather than single, inhibitory synapses in terms of the spread of their collective shunting effect in the dendritic tree; and suggest an explanation as to why, in both cortex and hippocampus, the total number of inhibitory dendritic synapses per pyramidal cell is smaller (about 20 %) than that of excitatory synapses. This chapter, thus, provides a fresh perspective on the biophysical design principles that govern the operation of inhibition in dendrites.

Computational Neuroscience: Capturing the Essence

The nervous system faces a most challenging task – to receive information from the outside world, process it, to change adaptively, and to generate an output – the appropriate behavior of the organism in a complex world. The research agenda of computational neuroscience (CN) is to use theoretical tools in order to understand how the different elements composing the nervous system: membrane ion channels, synapses, neurons, networks, and the systems they form, implement this demanding challenge successfully. CN deals with theoretical questions at both the cellular and subcellular levels, as well as at the networks, system, and behavioral levels. It focuses both on extracting basic biophysical principles (e.g., the rules governing the input-output relationship in single neurons) as well as on high-level rules governing the computational functions of a whole system, e.g., “How is a spot of light moving in the visual field encoded in the retina?” Or “how do networks of interconnected neurons represent and retain memories?” Ultimately, CN aims to understand, via mathematical theory, how do high-level phenomena such as cognition, emotions, creativity, and imagination, as well as brain disorders such as autism and schizophrenia, emerge from elementary brain-mechanisms. Here we highlight a few theoretical approaches used in CN and provide the respective fundamental insights that were gained. We start with biophysical models of single neurons and end with examples for models at the network level.

Inhibitory coverage of dendritic excitation

“The Coverage Problem” is a class of optimization problems used in different fields that aims to explore the best strategy in order to maximally “cover” a region under some constrains. For example, how could a limited number of cellular antennas be distributed such that their signals would cover a maximal region (e.g., a whole city) while limiting the radio-frequency radiation to safe levels? Here we ask whether the notion of coverage could be applied to inhibitory synapses, giving them optimal control over the excitatory/excitable activity in the dendritic tree. In support of this idea is the fact that most (85%) [1-3] inhibitory synapses target dendrites rather than operate at the soma/axon region as a global veto mechanism of the neuron’s output . Additionally, it seems to be the rule rather than the exception that single inhibitory axons target specific dendritic sub-regions where each axon makes multiple synapses [4-6], implying that the role of such inhibition is to cover a particular dendritic region. Using analytic solutions of the cable theory as well as detailed compartmental models, we searched for the spatial distribution of inhibition that maximally covers the modeled dendrite under different constraints (e.g., fixed number of contacts per axon and fixed total inhibitory conductance). We explored the conditions in which the inhibitory coverage would be most effective in controlling the neuronal output and compared our results to the actual distributions of inhibitory synapses in different dendrites. We showed that despite the small number of inhibitory synapses (relative to the excitatory synapses) in dendrites of most central neurons, when the synapses are strategically placed, they can effectively dampen the excitatory/excitability activity in dendrites both globally and in a domain-specific manner.

The Blue Brain Project: calibrating the neocortical column

The Blue Brain Project is an attempt to reverse-engineer and model the neocortical column, to explore how it functions and to serve as a tool for neuroscientists and medical researchers. In order to achieve the goal of automatically fitting models to the latest data from clearly defined sources, a series of calibration steps have been developed. Each calibration step includes a physiological database, analysis technique and comparison to model data. All data is scored for completeness and quality. The aspects of the neocortical model for which calibration steps have been implemented are: the volume and composition of the column, ion channels, single cell electrical behavior, morphology repair and cloning, synaptic properties, short- and long-term plasticity, synaptic integration, polysynaptic loops, touch detection, structural and functional connectivity and emergent phenomena. The result of the calibration process is a score indicating the overall precision and quality of the fit. This system provides a means to identify those areas which require additional biological data as well as those areas where the model is biologically accurate or in need of refinement. The calibration process will continue to develop, as further biological details become known, and guide the refinement of the neocortical column model.