Event-driven simulation of cerebellar granule cells

Internalization of extracellular Tau oligomers in Alzheimer's disease

A key factor in the progression of Alzheimer's disease (AD) is internalization of extracellular Tau oligomers (ecTauOs) by neuroglial cells. Aberrant hyperphosphorylation of Tau results in their dissociation from microtubules and formation of toxic intracellular Tau oligomers (icTauOs). These are subsequently released to the extracellular space following neuronal dysfunction and death. Although receptor mediated internalization of these ecTauOs by other neurons, microglia and astrocytes can facilitate elimination, incomplete degradation thereof promotes inflammation, exacerbates pathologic spread and accelerates neurodegeneration. Targeting Tau oligomer degradation pathways, blocking internalization receptors, and mitigating neuroinflammation are proposed as therapeutic strategies to control Tau propagation and toxicity. This review highlights the urgent need for innovative approaches to prevent the spread of Tau pathology, emphasizing its implications for AD and related neurodegenerative diseases.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

A Safety Study of the Effects of 2-Dimensional Shear Wave Elastography on Synaptic Morphologic Characteristics and Function in the Hippocampus of Neonatal Mice

OBJECTIVES

The aim of this study was to determine the effects of 2-dimensional (2D) shear wave elastography (SWE) on synaptic morphologic characteristics and function in the neonatal mouse hippocampus and whether it affects the capacity for learning and memory later in life.

METHODS

We divided neonatal mice into a control group and a 2D SWE group scanned for 10, 20, or 30 minutes. Hippocampal morphologic characteristics were assessed by hematoxylin-eosin and Nissl staining. Ultrastructures of hippocampal neurons were visualized by electron microscopy. Protein and messenger RNA expression levels of synaptophysin, N-methyl-d-aspartate receptor 1 (NMDAR1), NMDAR2A, and NMDAR2B were quantified by a western blot and polymerase chain reaction, respectively. Learning and memory of adult mice were evaluated by the Morris water maze and the novel object recognition task.

RESULTS

Compared with the control group, the hippocampal morphologic characteristics of the experimental groups did not differ under light microscopy, and the synaptic structures assessed by electron microscopy appeared normal. Western blot and polymerase chain reaction results showed that expression of synaptophysin, NMDAR1, NMDAR2A, and NMDAR2B were downregulated after exposure to 2D SWE, but there were no statistical differences between the experimental groups. This downregulation disappeared within 24 hours. The results of the Morris water maze and novel object recognition suggested that the 2D SWE scanning on neonatal mice had no effect on learning and memory in adulthood.

CONCLUSIONS

This study demonstrated that when the mice were exposed to neonatal cranial ultrasound by 2D SWE lasting for longer than 10 minutes, the expression of genes involved in synaptic function was affected, but this effect lasted no longer than 24 hours and did not affect learning and memory in adulthood.

Modeling the Encoding of Saccade Kinematic Metrics in the Purkinje Cell Layer of the Cerebellar Vermis

Recent electrophysiological observations related to saccadic eye movements in rhesus monkeys, suggest a prediction of the sensory consequences of movement in the Purkinje cell layer of the cerebellar oculomotor vermis (OMV). A definite encoding of real-time motion of the eye has been observed in simple-spike responses of the combined burst-pause Purkinje cell populations, organized based upon their complex-spike directional tuning. However, the underlying control mechanisms that could lead to such action encoding are still unclear. We propose a saccade control model, with emphasis on the structure of the OMV and its interaction with the extra-cerebellar components. In the simulated bilateral organization of the OMV, each caudal fastigial nucleus is arranged to receive incoming projections from combined burst-pause Purkinje cell populations. The OMV, through the caudal fastigial nuclei, interacts with the brainstem to provide adaptive saccade gain corrections that minimize the visual error in reaching a given target location. The simulation results corroborate the experimental Purkinje cell population activity patterns and their relation with saccade kinematic metrics. The Purkinje layer activity that emerges from the proposed organization, precisely predicted the speed of the eye at different target eccentricities. Simulated granular layer activity suggests no separate dynamics with respect to shaping the bilateral Purkine layer activity. We further examine the validity of the simulated OMV in maintaining the accuracy of saccadic eye movements in the presence of signal dependent variabilities, that can occur in extra-cerebellar pathways.

A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation.

Analytical integrate-and-fire neuron models with conductance-based dynamics and realistic postsynaptic potential time course for event-driven simulation strategies

In a previous paper (Rudolph & Destexhe, 2006), we proposed various models, the gIF neuron models, of analytical integrate-and-fire (IF) neurons with conductance-based (COBA) dynamics for use in event-driven simulations. These models are based on an analytical approximation of the differential equation describing the IF neuron with exponential synaptic conductances and were successfully tested with respect to their response to random and oscillating inputs. Because they are analytical and mathematically simple, the gIF models are best suited for fast event-driven simulation strategies. However, the drawback of such models is they rely on a nonrealistic postsynaptic potential (PSP) time course, consisting of a discontinuous jump followed by a decay governed by the membrane time constant. Here, we address this limitation by conceiving an analytical approximation of the COBA IF neuron model with the full PSP time course. The subthreshold and suprathreshold response of this gIF4 model reproduces remarkably well the postsynaptic responses of the numerically solved passive membrane equation subject to conductance noise, while gaining at least two orders of magnitude in computational performance. Although the analytical structure of the gIF4 model is more complex than that of its predecessors due to the necessity of calculating future spike times, a simple and fast algorithmic implementation for use in large-scale neural network simulations is proposed.