Neuronal morphologies: the shapes of thoughts

The mammalian brain, one of the most fascinating systems in nature, is a complex biological structure that has kept scientists busy for over a century. Many of the brain's mysteries have been unraveled due to the enormous efforts of the scientific community, but yet many questions remain unsolved. The detailed drawings of Ramon y Cajal revealed the hidden structure of the brain, identifying the neurons as its fundamental structural and functional units. Although a significant amount of experimental reconstructions have been gathered over the past years, neuronal morphologies still remain one of the unsolved riddles of the brain. Why is neuronal diversity important for the functionality of the brain and how do neuronal morphologies ''shape'' our thoughts?

To address these questions one needs to characterize the various shapes of neuronal morphologies. Traditionally, this task has been performed by using a set of morphological features, such as total length, branch orders and asymmetry. However, these features focus on a specific morphological aspect thereby causing a significant information loss from the original structure. Inspired by algebraic topology, I have conceived a topological descriptor of neuronal trees that couples the topology of a tree with the geometric features of its structure, retaining more details of the original morphology than traditional morphometrics. This descriptor has proved to be very powerful in discriminating several neuronal types into concrete groups based on morphological grounds, and has lead to the discovery of two distinct classes of pyramidal cells in the human cortex. In addition, the Topological Morphology Descriptor is important for the generation of artificial cells whose morphologies remain faithful to the biological ones.

Neurons of the same morphological type have similar topological and geometric characteristics, therefore appearing to be highly structured. However, it is still unknown to what extent the complex neuronal morphology is shaped by the genetic information of an organism and to what extent it arises from stochastic processes. To study the impact of randomness and structure of neuronal morphologies on the connectivity of the network they form, I compared the properties of networks that arise from different artificially generated morphologies, ranging from random walks to constrained branching structures, against those of biological networks and computational reconstructions built from biological morphologies. Surprisingly, networks that are generated from almost random morphologies share a lot of common properties with biological networks, such as the spatial clustering of connections and the common neighbor effect, indicating that stochastic processes that take place during development, contribute significantly to the observed neuronal shapes.

This thesis resolves a number of the mysteries of neuronal morphologies and questions our beliefs about the role of randomness in the formation of the brain. Thus, it brings us closer to understanding the fundamental differences among morphologies, and how randomness and structure are combined together to generate one of the most complex biological systems.

Developmental and spatial expression pattern of syntaxin 13 in the mouse central nervous system

Harald Hirling, Juan-Carlos Sarria

Vesicular transport involves SNARE (soluble- N-ethylmaleimide-sensitive-factor-attachment-protein-receptor) proteins on transport vesicles and on target membranes. Syntaxin 13 is a SNARE enriched in brain, associated with recycling endosomes; its overexpression in PC12 cells promotes neurite outgrowth. This suggests an important role for receptor recycling during neuronal differentiation. Here we describe the spatiotemporal pattern of syntaxin 13 expression during mouse brain development. During early embryogenesis (E12-E15), it was found in the forebrain ventricular zone and in primary motor and sensory neurons in the brainstem, spinal cord and sensory ganglia. In the forebrain at E15, syntaxin 13 was not detected in neuroblasts in the intermediate zone of the embryonic hemispheric wall, while there was labeling in cortical neurons in deeper layers starting at E15-18, and progressively in later-generated neurons up to layer II around P6. Syntaxin 13 reached maximal expression in all brain divisions at about P7, followed by a decrease, with heterogeneous neuron populations displaying various staining intensities in adult brain. While usually restricted to the soma of neurons, we transiently detected syntaxin 13 in dendrites of pyramidal neurons during the first postnatal week. In conclusion, the developmentally regulated syntaxin 13 expression in various neuronal populations is consistent with its involvement in endocytic trafficking and neurite outgrowth.

2002

Sensory memory in a model of a cortical column

Julien Mayor

Sensory memory is the first step of a complex memorization process. Sensory information is buffered in a "sensory store" for a short period. Then part of it is transferred to working memory, where information can be actively processed and, eventually, through its rehearsal, can be memorized and stored in long-term memory. Sensory memory is characterized by a high capacity and a short temporal extension. In this work we show that such a transient buffering of information is well captured in models of cortical microcircuits. Sensory information stays for a short time in the perturbations it induced to the neural activity. Two fundamental characteristics of cortical columns appear to be useful to this purpose. The first one is an approximate balance of neuronal excitation and inhibition. The second one is the sparseness of cells recurrent connections. Together, these two discriminants ensure a broad set of complex dynamical behaviours of the neural network along with the presence of a phase of sustained non-oscillatory activity with very irregular spike trains in individual neurons. We show that at the vicinity of a phase transition from a quiescent state to a phase of chaotic spiking activity, global stimuli can be buffered in the "macroscopic" neuronal activity. However if the sensory input only excites a subpart of the network, efficient information buffering is made using the detailed spiking activity of every neuron in the network. The "microscopic" structure of the network is therefore needed in order to buffer sensory information. In most situations, addition of noise enhances the buffering performance and the computational power of the network. This effect is known in the physical literature as "stochastic resonance". If the network has to achieve complex transformation of its inputs, stochastic resonance is shown to be an emergent property of the population of neurons. Both the very irregular spiking activity present in vivo and the balanced excitation and inhibition measured in cortical columns have a new functional relevance in being the neuronal substrate of an efficient sensory memory .

EPFL2005

Untangling emergent cortical dynamics: neurons from networks, noise from chaos

Max Christian Nolte

The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can often be explained by circuit models with different simplifying assumptions for the underlying neuron models and their network structure, such as deterministic synapse models describing stochastic synapses, or a uniform network structure describing heterogeneous synaptic connectivity. Intrinsic neural variability, for example, can emerge from both stochastic synaptic properties (noise) and deterministic network dynamics (chaos). It is therefore often not clear if models with ad hoc simplifying assumptions for various biological details provide correct explanations for the emergence of cortical dynamics. In this thesis, we set out to advance our understanding of how detailed biological properties of cortical neurons and their network structure shape emergent dynamics by studying a model of a prototypical neocortical microcircuit that was reconstructed using all relevant available biological data. To make our predictions as biologically accurate as possible, we used a "zero tweak" strategy wherein parameters in the model were not adjusted to replicate specific experimentally observed dynamical properties, but instead were constrained by biological data. This allowed us to characterize the effect of two biological properties that are often abstracted away in ad hoc simplifications: stochastic synaptic transmission and a heterogeneous network structure with complex higher-order connectivity. Studying the model, we made several important predictions: (1) Stochastic synaptic transmission, in an interplay with recurrent network dynamics, causes rapid chaotic divergence of spontaneous activity. (2) Synaptic noise overshadows other local cellular noise sources. (3) Amid the noise and chaos, neurons can reliably respond to external inputs with millisecond spike-time precision. (4) This reliable response goes beyond mere feedforward suppression of recurrent dynamics and is driven by the circuit at a near-critical excitation-inhibition balance. (5) An abundance of high-dimensional cliques of all-to-all connected neurons which shape correlations between neurons in a hierarchical manner. (6) This effect is strongly reduced when synaptic connectivity is replaced by a rejected null model with reduced higher-order network structure. We conclude that a detailed representation of cellular noise sources and high-dimensional network structure is imperative to accurately model emergent cortical network dynamics. Models that make ad hoc simplifying assumptions need to carefully justify the exclusion of such details.