Biology subjects

Kerr, C. C.

Publications and source records attributed to Kerr, C. C..

4 recordsLinked to original sources

bioRxiv2026

Optimising neural contrast and minimising tissue shrinkage in dragon lizard brains: buffered Lugol's iodine versus phosphotungstic acid for contrast-enhanced μCT

Contrast-enhanced micro-computed tomography (CT) has become a valuable tool for addressing neuroanatomical questions that previously required destructive histology. As a relatively new technique, it remains subject to ongoing technical refinement, and taxon- and tissue-specific protocol optimisation. We evaluated the performance of two common contrast-enhancing agents, buffered Lugol's iodine and phosphotungstic acid (PTA) in aqueous solution, for CT visualisation of the nervous system in the Jacky dragon lizard, Amphibolurus muricatus by comparing staining time, contrast specificity and brain tissue shrinkage evaluated at three concentrations (1.25%, 2.5% and 5%). Buffered Lugol's iodine outperformed aqueous PTA by requiring shorter staining times (as short as four days) and providing better white matter-grey matter differentiation, whereas PTA required longer incubation periods of approximately three weeks and produced poorer white-grey matter differentiation. Importantly, buffered Lugol's iodine produced higher brain-to-endocast occupancy estimates than aqueous PTA, suggesting substantially reduced tissue shrinkage. PTA-treated specimens exhibited an estimated 13.3% reduction in occupancy and approximately 20% greater brain tissue shrinkage than buffered Lugol's-treated specimens. Together, these findings demonstrate that buffered Lugol's iodine staining provides the high level of internal tissue contrast required to resolve fine-scale neuroanatomical organisation in three dimensions, preserves the spatial relationships among anatomical structures, and reduces artefacts, all of which result in a closer correspondence between the brain and endocranial cavity. From a practical perspective, the study provides guidance for contrast agent selection in future CT studies and reveals that buffered Lugol's iodine is better suited to fine-scale neuroanatomical, volumetric and geometric morphometric analyses, whereas PTA is advantageous for applications prioritising general soft-tissue or musculoskeletal visualisation.

neuroscience↗

bioRxiv2021

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

neuroscience↗

bioRxiv2018

NetPyNE: a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, e.g., a connectivity rule, instead of tens of loops to create millions of cell-to-cell connections. Users can then generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing using NeuroML and SONATA standardized formats. NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate different brain regions and phenomena.

neuroscience↗

bioRxiv2018

Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.\n\nSignificance statementEpileptic seizures are commonly thought to arise from a pathology of inhibition in the brain circuits. Theoretical models aiming to explain epileptic oscillations usually describe the neural activity solely in terms of inhibition and excitation. Single neuron adaptation properties are usually assumed to have only a limited contribution to seizure dynamics. To explore this issue, we developed a novel neural mass model with adaption in the excitatory population. By including adaptation and intrinsic excitability together with inhibition in this model, we were able to account for several experimentally observed properties of seizures, resting state dynamics, and pre-ictal oscillations, leading to improved understanding of epileptic seizures.

neuroscience↗