Biology subjects

Silver, R. A.

Publications and source records attributed to Silver, R. A..

7 recordsLinked to original sources

bioRxiv2025

Population activity of mossy fibre axon input to the cerebellar cortex during behaviours

The cerebellum gathers information from the neocortex via the cortico-ponto-cerebellar pathway, and forms sensorimotor associations for predicting movements and their sensory consequences. However, little is known about the functional properties of this major input to the cerebellar cortex. Recordings from individual cerebellar mossy fibre axons (MFAs) have shown that they convey sensory and motor information, but nothing is known about their population code. Here, we report that the population activity of pontine MFAs is heterogeneous, high-dimensional and that different subpopulations of MFAs are active during quiet and active behavioural states. Population activity occupied a substantial fraction of the state space and some MFAs are particularly informative about behaviour. Surprisingly, positively and negatively modulated MFAs are intermingled, suggesting granule cells integrate opposite-signed inputs to generate mixed bidirectional sensorimotor representations. Our results establish that neocortex and cerebellum can communicate with a low redundancy, high capacity, bidirectional population code, which is well-suited for forming sensorimotor associations. Highlights[bullet] Population activity of main mossy fibre axonal input to cerebellar cortex. [bullet]Ponto-cerebellar code is high dimensional during behaviours. [bullet]Behavioural information conveyed by bidirectional population code. [bullet]Modelling predicts heterogeneous response properties of granule cells.

neuroscience↗

bioRxiv2023

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

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 make 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.

neuroscience↗

bioRxiv2022

Validation of a stereological method for estimating particle size and density from 2D projections with high accuracy

AO_SCPLOWBSTRACTC_SCPLOWStereological methods for estimating the 3D particle size and density from 2D projections are essential to many research fields. These methods are, however, prone to errors arising from undetected particle profiles due to sectioning and limited resolution, known as lost caps. A potential solution by Keiding et al. (1972) accounts for lost caps by quantifying the smallest detectable profiles in terms of their limiting section angle ({phi}). However, this simple solution has not been widely adopted nor validated. Rather, model-independent design-based stereological methods, which do not explicitly account for lost caps, have come to the fore. Here, we provide the first experimental validation of the Keiding model by quantifying {phi} of synaptic vesicles using electron-tomography 3D reconstructions. This analysis reveals a Gaussian distribution for {phi} rather than a single value. Nevertheless, curve fits of the Keiding model to the 2D diameter distribution accurately estimate the mean {phi} and 3D diameter distribution. While systematic testing using Monte Carlo simulations revealed an upper limit to determining {phi}, our analysis shows that mean {phi} can be used to estimate the 3D particle density from the 2D density under a wide range of conditions, and this method is potentially more accurate than minimum-size-based lost-cap corrections and disector methods. We applied the Keiding model to estimate the size and density of somata, nuclei and vesicles in rodent cerebella, where high packing density can be problematic for design-based methods.

neuroscience↗

bioRxiv2019

Norepinephrine controls the gain of the inhibitory circuit in the cerebellar input layer

Golgi cells (GoCs) are the main inhibitory interneurons in the input layer of the cerebellar cortex and are electrically coupled together, forming syncytia. GoCs control the excitability of granule cells (GCs) through feedforward, feedback and spillover-mediated inhibition. The GoC circuit therefore plays a central role in determining how sensory and motor information is transformed as it flows through the cerebellar input layer. Recent work has shown that GCs are activated when animals perform active behaviours, but the underlying mechanisms remain poorly understood. Norepinephrine (NE), also known as noradrenaline, is a powerful modulator of network function during active behavioral states and the axons of NE-releasing neurons in the locus coeruleus innervate the cerebellar cortex. Here we show that NE hyperpolarizes the GoC membrane potential, decreases spontaneous firing and reduces the gain of the spike frequency versus input-current relationship. The GoC membrane hyperpolarization can be mimicked with an 2-noradrenergic agonist, inhibited with a specific 2 antagonist and is abolished when G protein-coupled inwardly-rectifying potassium (GIRK) channels are blocked. Moreover, NE reduces the effective electrical coupling between GoCs through a persistent sodium current (INaP)-dependent mechanism. Our results suggest that NE controls the gain of the GoC inhibitory circuit by modulating membrane conductances that act to reduce membrane excitability and decrease electrical coupling. These mechanisms appear configured to reduce the level of GoC inhibition onto GCs during active behavioural states.

neuroscience↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗

bioRxiv2017

Assessing the role of inhibition in stabilizing neocortical networks requires large-scale perturbation of the inhibitory population

Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011) and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This \"balance\" between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations they perform. Networks of neurons combining an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs; Tsodyks et al. 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). We re-examined this question by investigating how cortical network models consisting of many neurons behave following perturbations, and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed in order to robustly detect an ISN regime in cortex. We propose that wide-field optogenetic suppression of inhibition under a promoter targeting all inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.\n\nSignificance statementMany useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an \"inhibition-stabilized network\" or \"ISN\" regime). However, recent experimental results suggest this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism, and found that in order to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs, targetting a majority of inhibitory neurons, may be able to resolve this question.

neuroscience↗

bioRxiv2017

Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

Pattern separation is a fundamental function of the brain. Divergent feedforward networks separate overlapping activity patterns by mapping them onto larger numbers of neurons, aiding learning in downstream circuits. However, the relationship between the synaptic connectivity within these circuits and their ability to separate patterns is poorly understood. To investigate this we built simplified and biologically detailed models of the cerebellar input layer and systematically varied the spatial correlation of their inputs and their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the mossy fiber input or granule cell output patterns. Our results establish that the extent of synaptic connectivity governs the pattern separation performance of feedforward networks by counteracting the beneficial effects of expanding coding space and threshold-mediated decorrelation. The sparse synaptic connectivity in the cerebellar input layer provides an optimal solution to this trade-off, enabling efficient pattern separation and faster learning.

neuroscience↗