Biology subjects

Stemmler, M.

Publications and source records attributed to Stemmler, M..

7 recordsLinked to original sources

bioRxiv2026

The lattice scales of grid-cell activity

Grid cells in the rodent medial entorhinal cortex (MEC) form a multi-scale representation of the animals environment and are thought to be a signature of continuous-attractor networks.Experiments suggest that grid scales are discrete and arranged in a geometric progression with constant scale ratios, as predicted by efficient-coding theories that posit that the grid-cell system is optimized for spatial resolution. This normative explanation has been challenged by a developmental theory whereby grid-cell modularity arises from pattern formation in a system with smooth parameter gradients. As a consequence, scale ratios should not be constant, but vary by module. However, as we show here, the experimental data chosen to support this mechanistic theory fail to do so. Publicly available large-scale grid-cell datasets could provide a clear benchmark and further our understanding of the neuronal basis of spatial navigation. We, therefore, reanalyzed such recordings and found that they refute the developmental theory. Instead, the measured scale ratios agree with the geometric-progression hypothesis and set strict bounds for any future grid-cell theory.

neuroscience↗

bioRxiv2024

Grid cells anticipate the animal's future movement

Grid cells in the rodent medial entorhinal cortex preferentially fire spikes when the animal is within certain regions of space. When experimental data are averaged over time, spatial firing fields become apparent. If these firing fields represented only the current position of the animal, a grid cells firing should not depend on whether the animal is running into or out of a firing field. Yet many grid cells are sensitive to the animals direction of motion relative to the firing-field center. Such apparent egocentric "inbound-outbound tuning" could be a sign of prospective encoding of future position, but it is unclear whether grid cells code ahead in space or in time. To investigate this question, we decided to undo the inbound-outbound modulation by shifting all spikes within a given firing field by a fixed distance in space or in time. For grid-cell data recorded in mice, optimizing in space requires a forward shift of around 2.5 cm, whereas optimizing in time yielded a forward shift of about 170 ms. In either case the firing-field sizes decrease. Minimizing just the field size yields somewhat smaller shifts (roughly 1.8 cm and around 115 ms ahead). Jointly optimizing along the temporal and spatial dimension reveals a continuum of flat inbound-outbound tuning curves and a shallow minimum for field sizes, located at about 2.3 cm and 35 ms. These findings call into question a purely spatial or purely temporal interpretation of grid-cell firing fields and inbound-outbound tuning.

neuroscience↗

bioRxiv2023

Grid Codes versus Multi-Scale, Multi-Field Place Codes for Space

Recent work on bats flying over long distances has revealed that single hippocampal cells represent space on many scales, suggesting that these cells simultaneously participate in multiple neuronal networks to yield a multi-scale, multi-field place cell code. While the first theoretical analyses revealed that this code outperforms classical single-scale, single-field place codes, it remains an open question what the performance boundaries of this code are, what functional properties the network responsible for this code has, and how it compares to a highly regular grid code, in which cells form distinct modules, each with its own attractor dynamics on the network. In this paper we address these questions with rigorous analyses of comprehensive simulations. Specifically, we perform an evolutionary optimization of several multi-scale, multi-field place cell networks and compare the results against a single-scale, single-field as well as against a simple grid code. We focus on two main characteristics: the general performance of the code itself and the dynamics of the network generating it. Our simulation experiments show that, under normal conditions, the grid code easily outperforms any multi-scale, multi-field place code with respect to decoding accuracy. However, the latter is more robust to noise and lesions, such as drop-out. The robustness comes at a cost, as the grid code requires a significantly smaller number of neurons and fields per neuron. Further analyses of the network dynamics also revealed that the proposed topology of multi-scale, multi-field place cells does not, in fact, result in a continuous attractor network. More precisely, the simulated networks do not maintain activity bumps without position specific input. The multi-scale, multi-field code, therefore, seems to be a compromise between a place code and a grid code that invokes a trade-off between accurate positional encoding and robustness.

neuroscience↗

bioRxiv2021

Metabolic breakdown of non-small cell lung cancers by mitochondrial HSPD1 targeting

The identification of novel targets is of paramount importance to develop more effective drugs and improve the treatment of non-small cell lung cancer (NSCLC), the leading cause of cancer-related deaths worldwide. Since cells alter their metabolic rewiring during tumorigenesis and along cancer progression, targeting key metabolic players and metabolism-associated proteins represents a valuable approach with a high therapeutic potential. Metabolic fitness relies on the functionality of heat shock proteins (HSPs), molecular chaperones that facilitate the correct folding of metabolism enzymes and their assembly in macromolecular structures. Here, we show HSPD1 (HSP60) as a survival gene ubiquitously expressed in NSCLC and associated with poor patients prognosis. HSPD1 knockdown or its chemical disruption by the small molecule KHS101 induces a drastic breakdown of oxidative phosphorylation, and suppresses cell proliferation both in vitro and in vivo. By combining drug profiling with transcriptomics and through a whole-genome CRISPR/Cas9 screen, we demonstrate that HSPD1-targeted anti-cancer effects are dependent on OXPHOS and validated molecular determinants of KHS101 sensitivity, in particular, the creatine-transporter SLC6A8 and the subunit of the cytochrome c oxidase complex COX5B. These results highlight mitochondrial metabolism as an attractive target and HSPD1 as a potential theranostic marker for developing therapies to combat NCSLC. SignificanceHSPD1 elimination or disruption interferes with NSCLC metabolic activity causing a strong OXPHOS-dependent energetic breakdown, which the cancer cells fail to overcome, highlighting HSPD1 as a potential theranostic marker for improving lung cancer therapy.

cancer biology↗

bioRxiv2019

N-cadherin stabilises neural identity by dampening anti-neural signals

A switch from E-to N-cadherin regulates the transition from pluripotency to neural identity but the mechanism by which cadherins regulate differentiation was previously unknown. Here we show that the acquisition of N-cadherin stabilises neural identity by dampening anti-neural signals. We use quantitative image-analysis to identify an effect of N-cadherin to promote neural differentiation independently of effects on cell cohesiveness. We reveal that cadherin switching diminishes the level of nuclear {beta}-catenin, and that N-cadherin also dampens FGF activity and consequently stabilises neural fate. Finally, we compare the timing of cadherin switching and differentiation in vivo and in vitro, and find that this process becomes dysregulated during in vitro differentiation. We propose that N-cadherin helps to propagate a stable neural identity throughout the emerging neuroepithelium, and that dysregulation of this process contributes to asynchronous differentiation in culture.

developmental biology↗

bioRxiv2018

Anticipatory neural activity improves the decoding accuracy for dynamic head-direction signals

Insects and vertebrates harbor specific neurons that encode the animals head direction (HD) and provide an internal compass for spatial navigation. Each HD cell fires most strongly in one preferred direction. As the animal turns its head, however, HD cells in rat anterodorsal thalamic nucleus (ADN) and other brain areas fire already before their preferred direction is reached, as if the neurons anticipated the future head direction. This phenomenon has been explained at a mechanistic level but a functional interpretation is still missing. To close this gap, we use a computational approach based on the animals movement statistics and a simple model for the behavior of the ADN head-direction network. Network activity is read out using population vectors in a biologically plausible manner, so that only past spikes are taken into account. We find that anticipatory firing improves the representation of the present HD by reducing the motion-induced temporal bias inherent in causal decoding. The amount of anticipation observed in ADN enhances the precision of the HD compass read-out by up to 40%. In addition, our framework predicts that neural integration times not only reflect biophysical constraints, but also the statistics of natural stimuli; anticipatory tuning should be found whenever neurons encode sensory signals that change gradually in time.\n\nSignificance statementAcross different brain regions, populations of noisy neurons encode dynamically changing stimuli. Decoding a time-varying stimulus from the population response involves a trade-off: For short read-out times, stimulus estimates are unreliable as the number of stochastic spikes will be small; for long read-out times, estimates are biased because they lag behind the true stimulus. We show that optimal decoding relies not only on finding the right read-out time window, but requires neurons to anticipate future stimulus values. We apply this framework to the rodent head-direction system and show that the experimentally observed anticipation of future head directions can be explained at a quantitative level from the neuronal tuning properties, the network size, and the animals head-movement statistics.

neuroscience↗

bioRxiv2017

GABA regulates resonance and spike rate encoding via a universal mechanism that underlies the modulation of action potential generation

Different mechanisms for action potential (AP) generation exist that shape neuronal coding and network dynamics. The neuro-transmitter GABA regulates neuronal activity but its role in modulating AP dynamics itself is unclear. Here we show that GABA indeed changes the AP mechanism: it causes regularly firing hippocampal CA3 neurons to bistably switch between spiking and quiescence, converts graded discharge-to-current relationships to have abrupt onsets, and induces resonance. Modeling reveals that A-currents enable these GABA-induced transitions. Mathematically, we prove that this transition sequence originates from a single universal principle that generically underlies the modulation of AP dynamics in any conductance-based neuron model. Conductance clamp experiments in hippocampal and brainstem neurons show the same transitions, confirming the universal theory. In simulated networks, synaptically controlled AP dynamics, permits dynamic gating of signals and targeted synchronization of neuronal sub-ensembles. These results advance the systematic understanding of AP modulation and its role in neuronal and network function.

neuroscience↗