Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits

It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 ± 2.7% (mean ± SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits

Enhanced Long-Term Microcircuit Plasticity in the Valproic Acid Animal Model of Autism

A single intra-peritoneal injection of valproic acid (VPA) on embryonic day (ED) 11.5 to pregnant rats has been shown to produce severe autistic-like symptoms in the offspring. Previous studies showed that the microcircuitry is hyperreactive due to hyperconnectivity of glutamatergic synapses and hyperplastic due to over-expression of NMDA receptors. These changes were restricted to the dimensions of a minicolumn (<50 μm). In the present study, we explored whether Long Term Microcircuit Plasticity (LTMP) was altered in this animal model. We performed multi-neuron patch-clamp recordings on clusters of layer 5 pyramidal cells in somatosensory cortex brain slices (PN 12–15), mapped the connectivity and characterized the synaptic properties for connected neurons. Pipettes were then withdrawn and the slice was perfused with 100 μM sodium glutamate in artificial cerebrospinal fluid in the recording chamber for 12 h. When we re-patched the same cluster of neurons, we found enhanced LTMP only at inter-somatic distances beyond minicolumnar dimensions. These data suggest that hyperconnectivity is already near its peak within the dimensions of the minicolumn in the treated animals and that LTMP, which is normally restricted to within a minicolumn, spills over to drive hyperconnectivity across the dimensions of a minicolumn. This study provides further evidence to support the notion that the neocortex is highly plastic in response to new experiences in this animal model of autism

Reconstruction and simulation of neocortical microcircuitry

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 mm3 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

Emergent Connectivity Principles in the Neocortex

The neocortex makes up over 80% of the mammalian brain and is responsible for higher cognitive functions, processing of sensory inputs and orchestration of complex motor outputs. It is a 6-layered structure composed of billions of morphologically and electrically diverse neurons. Functionally, the basic unit is the neocortical column (NCC), a vertical structure of 0.5 mm wide, repeated millions of times across the neocortex and connected in an intricate but consistent way. In this thesis I investigated the role of morphologies (neurogeometry) in shaping the elaborate connectivity scheme within a column. First, I suggest a morphological basis for the higher than expected reciprocal connectivity reported experimentally between connected pairs, using a newly defined measure: the Reciprocity Index (RI), which arises from a purely mathematical concept and can be derived from the morphometric statistics of neurons. Second, I show that most experimentally reported synaptic patterns between different classes of neurons can be directly computed from the statistics of their morphologies, while some pathways would require additional functional mechanisms to refine an even more specific synaptic pattern. My thesis is done within the Blue Brain Project, the first comprehensive attempt to reverse-engineer the mammalian brain, starting with the somatosensory NCC of a P14 rat. I present in this thesis my contribution in constructing the framework for building biologically accurate circuitry. I explain how we start from 3D neuron morphologies obtained in vitro, repair them for slicing artefacts, build circuits and accurately detect potential synapses. I also explore how modelling of the experimental procedures can help us characterize biases and predict in vivo data from in vitro data. I finally present recent exploratory work on how to use the supercomputing power to design novel in silico protocols to investigate the emergent dynamics of the neocortical column