Fast sodium channel gating supports localized and efficient axonal action potential initiation

Action potentials (APs) are initiated in the proximal axon of most neurons. In myelinated axons, a 50-times higher sodium channel density in the initial segment compared to the soma may account for this phenomenon. However, little is known about sodium channel density and gating in proximal unmyelinated axons. To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. We demonstrate that sodium channel density in the proximal axon is approximately 5 times higher than in the soma. Furthermore, sodium channel activation and inactivation are approximately 2 times faster. Modeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. Thus, sodium channel gating and density in unmyelinated mossy fiber axons appear to be specialized for robust AP initiation and propagation with minimal current flow

Stimfit: Quantifying electrophysiological data with Python

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals

Active dendritic integration as a mechanism for robust and precise grid cell firing

Understanding how active dendrites are exploited for behaviorally relevant computations is a fundamental challenge in neuroscience. Grid cells in medial entorhinal cortex are an attractive model system for addressing this question, as the computation they perform is clear: they convert synaptic inputs into spatially modulated, periodic firing. Whether active dendrites contribute to the generation of the dual temporal and rate codes characteristic of grid cell output is unknown. We show that dendrites of medial entorhinal cortex neurons are highly excitable and exhibit a supralinear input–output function in vitro, while in vivo recordings reveal membrane potential signatures consistent with recruitment of active dendritic conductances. By incorporating these nonlinear dynamics into grid cell models, we show that they can sharpen the precision of the temporal code and enhance the robustness of the rate code, thereby supporting a stable, accurate representation of space under varying environmental conditions. Our results suggest that active dendrites may therefore constitute a key cellular mechanism for ensuring reliable spatial navigation

Janus kinase 2 inhibition by pacritinib as potential therapeutic target for liver fibrosis

anus kinase 2 (JAK2) signaling is increased in human and experimental liver fibrosis with portal hypertension. JAK2 inhibitors, such as pacritinib, are already in advanced clinical development for other indications and might also be effective in liver fibrosis. Here, we investigated the antifibrotic role of the JAK2 inhibitor pacritinib on activated hepatic stellate cells (HSCs) in vitro and in two animal models of liver fibrosis in vivo.Jonel Trebicka is supported by the German Research Foundation project ID 403224013–SFB 1382 (A09); by the German Federal Ministry of Education and Research (BMBF) for the DEEP‐HCC project; by the Hessian Ministry of Higher Education, Research, and the Arts (HMWK) for the ENABLE cluster project; and by Eurostars (Grant ID 12350). The MICROB‐PREDICT (project ID 825694), DECISION (project ID 847949), GALAXY (project ID 668031), LIVERHOPE (project ID 731875), and IHMCSA (project ID 964590) projects have received funding from the European Union's Horizon 2020 research and innovation program. The manuscript reflects only the authors' views, and the European Commission is not responsible for any use that may be made of the information it contains. The funders had no influence on study design, data collection and analysis, decision to publish, or preparation of the manuscript

Complete Issue 42(1)

Complete digitized issue (volume 42, issue 1, November 1959) of The Gavel of Delta Sigma Rho

How Does a Memory Find Its Neurons?

This article comments on the Hypothesis paper by Thiago F. A. França and José M. Monserrat, https://doi.org/10.1002/bies.201800068International audienceHow does the brain store and recall memories? We have long known that a brain region called the hippocampus is critical for processing episodic memories. More recently, targeted manipulation of the activity of specific hippocampal neurons in rodents has suggested that memories are encoded by the firing of defined groups of hippocampal neurons, so-called cellular engrams. In the current issue of BioEssays, FranS ca and Monserrat discuss a key question that has puzzled the field for a while: Among the millions of neurons in the mammalian hippocam-pus, how is a subset recruited into a memory engram? [1] Basic neurophysiology teaches us that two factors largely determine whether a neuron is active: On the one hand, each neuron is excited or inhibited by thousands of synaptic inputs that convey information both about the outside world, and about ongoing processing of information in the brain. On the other hand, how these synaptic inputs will affect the activity of a neuron depends on its excitability, which in turn is determined by factors such as its morphological and biophysical properties. A straightforward prediction, then, is that an engram is uniquely associated with a specific memory: A given set of sensory stimuli, let's say the odors, colors, and sounds experienced during memory formation, should recruit a given set of neurons-an engram-that is largely predetermined by synaptic connectivity and excitability. Similarly, activating an engram should recall its associated memory, but not others. Recent experiments in rodents have challenged these straightforward predictions: manipulating the excitability of neurons suggests that there is some redundancy in the hippocampal code, as inhibiting putative future engram cells during memory formation does not inhibit learning of a new memory. [2] Instead, it appears as if the memory could simply be stored in a different population of neurons. From these experiments, one might conclude that hippocampal neurons are stochastically recruited into engrams, and that their initial synaptic wiring plays only a minor role in their selection. [3] How can the straightforward predictions from basic neuro-physiology be reconciled with the seemingly stochastic composition of engrams? FranS ca and Monserrat invoke insights into C. elegans circuits to argue that individual neurons can represent multiple dimensions of both external and internal variables, most of which escape our observation. While we typically test the purely spatial aspect of memories in rodents, we have relatively little experimental control over variables other than physical space, such as an animal's current goal, attention, or more fundamental sensations such as hunger or thirst. Furthermore, the authors argue that once there is some synaptic distance between the primary sensory inputs and the neurons of interest, these dimensions will be increasingly abstract. Finally, they propose that the apparent redundancy of the code can be explained by the insight that while stimulating two non-overlapping populations of engram neurons may have the same behavioral effect, that does not necessarily mean that the same memory was recalled, as recalling even very different memories may evoke the same behavioral output. Thus, they conclude that the wiring and excitability of hippocampal neurons provides a template for memory allocation, and that the seemingly stochastic nature of engrams can be explained by the notion that hippocampal neurons represent multiple, abstract dimensions. This is a timely review on a highly interesting topic, and the authors make some novel and creative points about memory allocation. Further work will be required to show whether the striking temporal dynamics of hippocampal representations, [4] which appears to be at odds with engrams determined by hard wiring, are indeed a consequence of varying attentional levels, or whether additional mechanisms such as synaptic turnover may contribute to engram formation. An important conclusion from their work is that we need better behavioral tasks for animals used in hippocampal research, which are typically rodents. A simple behavior, as tested by a dichotomic go/no-go task, may be the end point of widely different cognitive processes. This redundancy could be reduced if more complex behaviors including multiple choices were tested. More sophisticated behavioral tasks, together with improved control and monitoring of external stimuli and behavioral variables, may therefore disambiguate the effects of stimulating different engrams, and help understand how hippocampal neurons are recruited into memory circuits

The hippocampus as a perceptual map: neuronal and behavioral discrimination during memory encoding

Posté sur BioRxiv le 8 décembre 2019The hippocampus is thought to encode similar events as distinct memory representations that are used for behavioral decisions. Where and how this “pattern separation” function is accomplished in the hippocampal circuit, and how it relates to behavior, is still unclear. Here we perform in vivo 2-photon Ca 2+ imaging from hippocampal subregions of head-fixed mice performing a virtual-reality spatial discrimination task. We find that population activity in the input region of the hippocampus, the dentate gyrus, robustly discriminates small changes in environments, whereas spatial discrimination in CA1 reflects the behavioral performance of the animals and depends on the degree of differences between environments. Our results demonstrate that the dentate gyrus amplifies small differences in its inputs, while downstream hippocampal circuits will act as the final arbiter on this decorrelated information, thereby producing a “perceptual map” that will guide behaviour