Signals in stochastically generated neurons

Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations

Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.

Computational models of neurons and neural networks provide a valuable avenue to test our understanding of brain regions and to make predictions to guide future experimentation. Each neuron has a unique dendritic tree, features of which can vary depending on the location of the neuron within the particular brain region. In this study, we generated a complete population of dendritic trees for the most numerous type of neuron in the hippocampus, the dentate gyrus granule cell, using a realistic three-dimensional structural context to drive the generation process. Morphological properties can now be studied at the level of complete neuronal populations, and this work provides a foundation to build upon in the construction of large-scale, data-driven neuroanatomical and network models.

The timing of differentiation of adult hippocampal neurons is crucial for spatial memory

Adult neurogenesis in the dentate gyrus plays a critical role in hippocampus-dependent spatial learning. It remains unknown, however, how new neurons become functionally integrated into spatial circuits and contribute to hippocampus-mediated forms of learning and memory. To investigate these issues, we used a mouse model in which the differentiation of adult-generated dentate gyrus neurons can be anticipated by conditionally expressing the pro-differentiative gene PC3 (Tis21/BTG2) in nestin-positive progenitor cells. In contrast to previous studies that affected the number of newly generated neurons, this strategy selectively changes their timing of differentiation. New, adult-generated dentate gyrus progenitors, in which the PC3 transgene was expressed, showed accelerated differentiation and significantly reduced dendritic arborization and spine density. Functionally, this genetic manipulation specifically affected different hippocampus-dependent learning and memory tasks, including contextual fear conditioning, and selectively reduced synaptic plasticity in the dentate gyrus. Morphological and functional analyses of hippocampal neurons at different stages of differentiation, following transgene activation within defined time-windows, revealed that the new, adult-generated neurons up to 3-4 weeks of age are required not only to acquire new spatial information but also to use previously consolidated memories. Thus, the correct unwinding of these key memory functions, which can be an expression of the ability of adult-generated neurons to link subsequent events in memory circuits, is critically dependent on the correct timing of the initial stages of neuron maturation and connection to existing circuits.

Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: A hidden Markov model

Dendritic structure is traditionally characterized by distributions and interrelations of morphometric parameters, such as Sholl-like plots of the number of branches versus dendritic path distance. However, how much of a given morphology is effectively captured by any statistical description is generally unknown. In this work, we assemble a small number of standard geometrical parameters measured from experimental data in a simple stochastic algorithm to describe the dendrograms of hippocampal pyramidal cells. The model, consistent with the hidden Markov framework, is feedforward, local, and causal. It relies on two "hidden" local variables: the expected number of terminal tips in a given subtree, and the current path distance from the soma. The algorithm generates dendrograms that statistically reproduce all morphological essentials of dendrites observed in real neurons, including the distributions of branching and termination points, branch lengths, membrane area, topological asymmetry, and (assuming passive membrane parameters within physiological range) electrotonic characteristics. Thus, this algorithm and the small number of its morphometric parameters constitute a remarkably complete description of the dendrograms of hippocampal pyramidal cells. Specifically, it is found that CA3 and CA1 basal dendrites and CA3 apical dendrites can each be described as homogeneous morphological classes. In contrast, the accurate generation of CA1 apical dendrites necessitates the separate sampling of two types of branches, main and oblique, suggesting their derivations from different developmental mechanisms (terminal and interstitial growth, respectively). We further offer a plausible biophysical interpretation of the model hidden variables, relating them to microtubules and other intracellular resources.

Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study

We investigated the effect of morphological differences on neuronal firing behavior within the hippocampal CA3 pyramidal cell family by using three-dimensional reconstructions of dendritic morphology in computational simulations of electrophysiology. In this paper, we report for the first time that differences in dendritic structure within the same morphological class can have a dramatic influence on the firing rate and firing mode (spiking versus bursting and type of bursting). Our method consisted of converting morphological measurements from three-dimensional neuroanatomical data of CA3 pyramidal cells into a computational simulator format. In the simulation, active channels were distributed evenly across the cells so that the electrophysiological differences observed in the neurons would only be due to morphological differences. We found that differences in the size of the dendritic tree of CA3 pyramidal cells had a significant qualitative and quantitative effect on the electrophysiological response. Cells with larger dendritic trees: (1) had a lower burst rate, but a higher spike rate within a burst, (2) had higher thresholds for transitions from quiescent to bursting and from bursting to regular spiking and (3) tended to burst with a plateau. Dendritic tree size alone did not account for all the differences in electrophysiological responses. Differences in apical branching, such as the distribution of branch points and terminations per branch order, appear to effect the duration of a burst. These results highlight the importance of considering the contribution of morphology in electrophysiological and simulation studies.

NEURON: a tool for neuroscientists

NEURON is a simulation environment for models of individual neurons and networks of neurons that are closely linked to experimental data. NEURON provides tools for conveniently constructing, exercising, and managing models, so that special expertise in numerical methods or programming is not required for its productive use. This article describes two tools that address the problem of how to achieve computational efficiency and accuracy.