Biology subjects

Haimerl, C.

Publications and source records attributed to Haimerl, C..

5 recordsLinked to original sources

bioRxiv2026

Prolonged TGF-β locks NK cells in a dysfunctional state through persistent epigenetic remodeling of IRF, T-bet and EOMES binding sites

TGF-{beta} signaling is a major regulator of immune cell differentiation and function, yet whether prolonged signaling can durably imprint dysfunctional immune states independently of continued pathway engagement remains unclear. Here, we identify signal duration as a critical determinant of human natural killer (NK) cell fate: short-term TGF-{beta} exposure induces largely reversible transcriptional, chromatin and functional changes, whereas prolonged exposure establishes persistent effector dysfunction independent of continued signaling. Mechanistically, prolonged TGF-{beta} drives durable loss of chromatin accessibility at effector-associated regulatory elements, particularly those enriched for IRF, EOMES, and T-bet binding motifs, resulting in stable restriction of NK cell effector programs despite reversibility of active histone marks. In contrast, tissue residency-associated programs remain largely reversible, revealing that distinct NK cell fate programs differ fundamentally in their susceptibility to epigenetic fixation downstream of the same cytokine signal. SMAD4 CUT&RUN and knockout experiments further demonstrate that canonical TGF-{beta} signaling acts primarily through rewiring of upstream transcriptional regulatory networks rather than direct targeting of effector loci. Finally, NK cells from patients with hepatocellular carcinoma recapitulate key functional and epigenetic features of the persistent TGF-{beta}-associated state defined in vitro. Together, these findings identify signal duration as a critical determinant of TGF-{beta}-driven cell fate and demonstrate that prolonged TGF-{beta} exposure can induce epigenetically stabilized dysfunctional states that persist after signal withdrawal, with important implications for therapeutic strategies aimed at reversing chronic TGF-{beta}-mediated dysfunction.

immunology↗

bioRxiv2025

Representational drift without synaptic plasticity

Neural computations support stable behavior despite relying on many dynamically changing biological processes. One such process is representational drift (RD), in which neurons responses change over the timescale of minutes to weeks, while perception and behavior remain unchanged. Generally, RD is believed to be caused by changes in synaptic weights, which alter individual neurons tuning properties. Since these changes alter the population readout, they require adaptation of downstream areas to maintain stable function, a costly and non-local problem. Here we propose that much of the observed drift phenomena can be explained by a simpler mechanism: changes in the excitability of cells without changes in synaptic weights. We show that such excitability changes can change the apparent tuning of neurons without requiring adaptation of population readouts in downstream areas. We use spike coding networks (SCN) to show that the extent of these tuning shifts matches experimentally observed changes. Moreover, specific decoders trained on one excitability setting perform poorly on others, while a general decoder can perform close to optimal across excitability changes if trained across many days. Our work proposes a simple mechanism without synaptic plasticity that explains experimentally observed RD, while downstream decoding and, by extension, behavior remain stable.

neuroscience↗

bioRxiv2021

Targeted comodulation supports flexible and accurate decoding in V1

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and task-specific readout through a selective combination of these responses. The former has been the topic of extensive study, but the means by which the brain achieves the latter remain poorly understood, especially when adapting to changing task demands. Here we introduce a novel theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We find that responses of neurons in area V1 of monkeys performing a visual orientation discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in neurons that are most informative for the current behavioral task. We propose that this modulation serves as a label that supports adaptive downstream readout. Our theoretical and experimental results show that V1 modulation can be used to decode from neural activity after only a small number of training trials, consistent with observed behavior. In a hierarchical visual neural network model, the modulator-induced labels are learned quickly and accompany task information across several processing stages to finally guide decisions. Consistent with this model, we find that the V1 modulatory signal is also present in the activity of simultaneously recorded MT units, and that its label of task information is preserved. Our findings provide evidence for a novel mechanism for task-adaptive information routing in the brain, through targeted co-modulation.

neuroscience↗

bioRxiv2019

Flexible and accurate decoding of neural populations through stochastic comodulation

Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later processing stages for relevant information to be transmitted. A statistically optimal decoder (e.g., maximum likelihood) can utilize task-relevant cells for any given task configuration, but relies on complete knowledge of the relationship between the task and the stimulus-response and noise properties of the encoding population. The brain could learn an optimal decoder for a task through supervised learning (i.e., regression), but this typically requires many training trials, and thus lacks the flexibility of humans or animals, that can rapidly adjust to changes in task parameters or structure. Here, we propose a novel decoding solution based on functionally targeted stochastic modulation. Population recordings during different discrimination tasks have revealed that a substantial portion of trial-to-trial variability in cell responses can be explained by stochastic modulatory signals that are shared, and that seem to preferentially target task-informative neurons (Rabinowitz et al., 2015). The variability introduced by these modulators corrupts the encoded stimulus signal, but we propose that it also serves as a label for the informative neurons, allowing the decoder to solve the identification problem. We show in simulations of a modulated Poisson spiking model that a linear decoder with readout weights proportional to the estimated neuron-specific strength of modulation achieves performance close to an optimal decoder.

neuroscience↗

bioRxiv2018

Internal representation of hippocampal neuronal population span a time-distance continuum

The hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatio-temporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from one day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatio-temporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies.\n\nSignificance StatementThe hippocampus organizes experience in sequences of events that form episodic memory. How are time and space internally computed in the hippocampus in the absence of sequential external inputs? Here we show that time and space are integrated together within the hippocampal network with different degrees of tuning across days. This was found by recording the activity of hundreds of pyramidal cells for several days. We also propose a mechanism supporting such spatio-temporal integration based on a ring attractor network model: the degree of tuning between space and time can be adjusted by modulating the power of a non-sequential external excitatory drive. In this way, the hippocampus is able to generate a spatio-temporal representation tuned to the task at hand.

neuroscience↗