Novel Models of Visual Topographic Map Alignment in the Superior Colliculus

Functional and spatial rewiring principles jointly regulate context-sensitive computation

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

Postsynaptic NMDA Receptor Expression Is Required for Visual Corticocollicular Projection Refinement in the Mouse Superior Colliculus

Efficient sensory processing of spatial information is facilitated through the organization of neuronal connections into topographic maps of space. In integrative sensory centers, converging topographic maps must be aligned to merge spatially congruent information. The superior colliculus (SC) receives topographically ordered visual inputs from retinal ganglion cells (RGCs) in the eye and layer 5 neurons in the primary visual cortex (L5-V1). Previous studies suggest that RGCs instruct the alignment of later-arriving L5-V1 inputs in an activity-dependent manner. However, the molecular mechanisms underlying this remain unclear. Here, we explored the role of NMDA receptors in visual map alignment in the SC using a conditional genetic knockout approach. We leveraged a novel knock-in mouse line that expresses tamoxifen-inducible Cre recombinase under the control of the Tal1 gene (Tal1CreERT2 ), which we show allows for specific recombination in the superficial layers of the SC. We used Tal1CreERT2 mice of either sex to conditionally delete the obligate GluN1 subunit of the NMDA receptor (SC-cKO) during the period of visual map alignment. We observed a significant disruption of L5-V1 axon terminal organization in the SC of SC-cKO mice. Importantly, retinocollicular topography was unaffected in this context, suggesting that alignment is also disrupted. Time-course experiments suggest that NMDA receptors may play a critical role in the refinement of L5-V1 inputs in the SC. Together, these data implicate NMDA receptors as critical mediators of activity-dependent visual map alignment in the SC.SIGNIFICANCE STATEMENT Alignment of topographic inputs is critical for integration of spatially congruent sensory information; however, little is known about the mechanisms underlying this complex process. Here, we took a conditional genetic approach to explore the role of NMDA receptors in the alignment of retinal and cortical visual inputs in the superior colliculus. We characterize a novel mouse line providing spatial and temporal control of recombination in the superior colliculus and reveal a critical role for NMDA expression in visual map alignment. These data support a role for neuronal activity in visual map alignment and provide mechanistic insight into this complex developmental process.

High-throughput sequencing of single neuron projections reveals spatial organization in the olfactory cortex

In most sensory modalities, neuronal connectivity reflects behaviorally relevant stimulus features, such as spatial location, orientation, and sound frequency. By contrast, the prevailing view in the olfactory cortex, based on the reconstruction of dozens of neurons, is that connectivity is random. Here, we used high-throughput sequencing-based neuroanatomical techniques to analyze the projections of 5,309 mouse olfactory bulb and 30,433 piriform cortex output neurons at single-cell resolution. Surprisingly, statistical analysis of this much larger dataset revealed that the olfactory cortex connectivity is spatially structured. Single olfactory bulb neurons targeting a particular location along the anterior-posterior axis of piriform cortex also project to matched, functionally distinct, extra-piriform targets. Moreover, single neurons from the targeted piriform locus also project to the same matched extra-piriform targets, forming triadic circuit motifs. Thus, as in other sensory modalities, olfactory information is routed at early stages of processing to functionally diverse targets in a coordinated manner.

Wiring subcortical image-forming centers: Topography, laminar targeting, and map alignment

Efficient sensory processing is a complex and important function for species survival. As such, sensory circuits are highly organized to facilitate rapid detection of salient stimuli and initiate motor responses. For decades, the retina's projections to image-forming centers have served as useful models to elucidate the mechanisms by which such exquisite circuitry is wired. In this chapter, we review the roles of molecular cues, neuronal activity, and axon-axon competition in the development of topographically ordered retinal ganglion cell (RGC) projections to the superior colliculus (SC) and dorsal lateral geniculate nucleus (dLGN). Further, we discuss our current state of understanding regarding the laminar-specific targeting of subclasses of RGCs in the SC and its homolog, the optic tectum (OT). Finally, we cover recent studies examining the alignment of projections from primary visual cortex with RGCs that monitor the same region of space in the SC.

Theoretical Models of Neural Development

Constructing a functioning nervous system requires the precise orchestration of a vast array of mechanical, molecular, and neural-activity-dependent cues. Theoretical models can play a vital role in helping to frame quantitative issues, reveal mathematical commonalities between apparently diverse systems, identify what is and what is not possible in principle, and test the abilities of specific mechanisms to explain the data. This review focuses on the progress that has been made over the last decade in our theoretical understanding of neural development.

Software for Brain Network Simulations: A Comparative Study

Numerical simulations of brain networks are a critical part of our efforts in understanding brain functions under pathological and normal conditions. For several decades, the community has developed many software packages and simulators to accelerate research in computational neuroscience. In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks. Next, our investigations on the characteristics of computational architecture and efficiency indicate that all simulators compile the most computationally intensive procedures into binary code, with the aim of maximizing their computational performance. However, not all simulators provide the simplest method for module development and/or guarantee efficient binary code. Third, a study of their amenability for high-performance computing reveals that NEST can almost transparently map an existing model on a cluster or multicore computer, while NEURON requires code modification if the model developed for a single computer has to be mapped on a computational cluster. Interestingly, parallelization is the weakest characteristic of BRIAN, which provides no support for cluster computations and limited support for multicore computers. Fourth, we identify the level of user support and frequency of usage for all simulators. Finally, we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package. The results indicate that BRIAN provides the most concise language for both cases considered. Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models. Overall, the case studies reinforce our general observation that simulators have a bias in the computational performance toward specific types of the brain network models.