Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

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In recent work, Vogels and collaborators demonstrated the ability of spike-time dependent inhibitory plasticity to stabilise recurrent spiking neural networks by balancing out the excitatory input received by neurons in the network with the required amount of inhibition [1]. Further, as an application of this unsupervised balance, they showed that such a network can operate as a non-attractor associative memory, by being able to store patterns that become indistinguishable from the background activity of the network, but that can be recalled successfully by external stimuli. In other research, also related to homoeostasis in neuronal networks, Butz and colleagues investigated the rewiring of deafferentated neurons driven by homoeostatic structural plasticity [2,3]. Their work, based on biological observations of restructuring in the visual cortex [4,5], suggests that neurons endeavour to maintain an appropriate level of electrical activity by adjusting the number of synaptic contacts they make. In order to remedy the loss in electrical activity experienced by neurons as a result of deafferentation, they implemented a growth rule that enabled the neurons to increase the number of lateral connections. In our research, we measure the memory capacity of balanced spiking neural networks possessing capabilities of both fast synaptic and slow structural plasticity by calculating a signal to noise ratio [6]. The memory capacity of a network of neurons is dependent on various parameters such as the magnitude of the recall stimulus, the size of the pattern relative to the size of the network, the learning rule that dictates inhibitory plasticity, and so on. In this work, as an initial step, we investigate the memory capacity of a non lesioned network containing spike-time dependent plastic inhibitory synapses similar to the network used by Vogels and collaborators. Random, possibly overlapping patterns are iteratively stored in the network as Hebbian assemblies. In order to measure their signal to noise ratio, the stored patterns are recalled by the activation of a randomly selected subset of their neurons. In general, during the recall of the stored patterns, we observe a gradual decrease in the signal to noise ratio as more and more patterns are stored in the network. This signifies an inverse relation between the mean signal to noise ratio and the total number of such patterns that were stored in the network. We are currently investigating the performance of our balanced networks during and after focal lesions and homoeostatic rewiring.

Grid cells in the dorsocaudal medial entorhinal cortex (dMEC) of the rat provide a metric representation of the animal's local environment. The collective firing patterns in a network of grid cells forms a triangular mesh that accurately tracks the location of the animal. The activity of a grid cell network, similar to head direction cells, displays path integration characteristics. Classical robotics use path integrators in the form of inertial navigation systems to track spatial information of an agent as well. In this paper, we describe an implementation of a network of grid cells as a dead reckoning system for the PR2 robot.

Head direction cells are thought to be an integral part of the neural navigation system. These cells track the agent's current head direction irrespective of the host's location. In doing so, they process a combination of inputs: angular velocity and visual inputs are major effectors; to correctly encode the agent's current heading. There are close to fifteen models of head direction cell systems found in literature today. Very few of these models have been implemented for bio-mimetic navigation in robots. In this paper, we describe an implementation of the head direction cell system on the robot operating system (ROS) robotic platform as a first step towards a bio-mimetic navigation system for Willow Garage's personal robot 2 (PR2) robot.

Meeting abstract presetnaed at the 27th Annual Computational Neuroscience Meeting (CNS*2018): Part Two.