Biology subjects

Sprekeler, H.

Publications and source records attributed to Sprekeler, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Memory consolidation and representational drift

Memory consolidation is the process by which temporary, malleable memories are transformed into more stable, longer-lasting forms. On a coarse anatomical scale, consolidation redistributes memories in the brain, but it remains poorly understood how these changes manifest themselves on the finer, cellular scale of neuronal engrams and how they relate to the cognitive level. In this study, we developed a phenomenological model of engram dynamics under systems consolidation. The model describes consolidation as a brain-wide phenomenon, where memories deterministically follow a trajectory through a space of patterns distributed among brain regions. It captures a broad range of features of memory consolidation, including selective consolidation, semantization, and power-law forgetting. In the model, consolidation is accompanied by population-level changes in neuronal representations that resemble the widely observed phenomenon of representational drift. When only a subset of neurons is observed, the deterministic dynamics of the model can appear stochastic, and a readout of task features deteriorates over time even when a stable readout exists for the full system. Our model offers a dynamical systems perspective on memory consolidation as a distributed process, moving beyond the classic region-centered view, and provides a functional interpretation of drift as a means of redistributing engrams for improved memory retention.

neuroscience↗

bioRxiv2026

Innate development of cognitive functions and motor programs by chemoaffinity

Humans and animals are equipped with a rich innate repertoire of cognitive and behavioral skills [1-3]. Yet, the developmental programs that establish the underlying neural structures are unknown. During early development, neural connectivity is shaped by molecular axon guidance and cell adhesion programs that connect neurons based on the affinity between presynaptic receptors and postsynaptic ligands [4, 5]. Here, we show how such chemoaffinity-based connectivity rules can also establish innate cognitive functions and motor programs by structuring recurrent neuronal networks prior to experience. Different networks develop depending on the statistics of receptor and ligand expression. We illustrate this mechanism in computational models of chemoaffinity-based development that establish i) continuous attractor networks for path integration [6] with a toroidal grid cell topology [7], ii) networks with an exponentially large number of discrete attractors and sequences [8] as categorical, hierarchical, or temporal priors [9, 10], and iii) networks for arbitrary innate motor trajectories. Hence, chemoaffinity may shape not only the anatomical organization of the brain but also its innate cognitive and motor functions.

neuroscience↗

bioRxiv2025

Information theoretic measures of neural and behavioural coupling predict representational drift

In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neurons tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed redundant, and information requiring knowledge of the state of both neurons is termed synergistic. We found that a neurons tuning stability is positively correlated with the strength of its average pairwise redundancy with the population, and that these high-redundancy neurons also tend to show high average pairwise synergy. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability-redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain-machine interfaces. Author summaryActivity in the brain represents information about the outside world and how we interact with it. Recent evidence shows that these representations slowly change day to day, while memories and learned behaviours stay stable. Individual neurons change their relationship to external variables at different rates, and we explore how interactions with other neurons in the population relates to this neuron-to-neuron variability. We find that more stable neurons tend to share information about external variables with many other neurons. Our results suggest there are constraints on how representations can change over time, and that these constraints are exhibited in shared fluctuations in activity among neurons in the population.

neuroscience↗

bioRxiv2024

Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus

Seizures that continue for beyond five minutes are classified as status epilepticus (SE) and constitute a medical emergency. Benzodiazepines, the current first-line treatment, attempt to terminate SE by increasing the conductance of chloride-permeable type-A GABA receptors (GABAARs). Despite their widespread use, benzodiazepines are ineffective in over a third of cases. Previous research in animal models has demonstrated that changes in intraneuronal chloride homeostasis and GABAAR physiology may underlie the development of benzodiazepine resistance in SE. However, there remains a need to understand the effect of these changes at a network level to improve translation into the clinical domain. Therefore, informed by data from human EEG recordings of SE and experimental brain slice recordings, we used a large spiking neural network model that incorporates chloride dynamics to investigate and address the phenomenon of benzodiazepine resistance in SE. We found that the GABAAR reversal potential (EGABA) sets SE-like bursting and determines the response to GABAAR conductance modulation, with benzodiazepines being anti-seizure at low EGABA and ineffective or pro-seizure at high EGABA. The SE-like activity and EGABA depended on a non-linear relationship between the strength of Cl- extrusion and GABAAR conductance, but not on the initial EGABA of neurons. Independently controlling Cl- extrusion in the pyramidal and interneuronal cell populations revealed the critical role of pyramidal cell Cl- extrusion in determining the severity of SE activity and the response to simulated benzodiazepine application. Finally, we demonstrate the models utility for considering improved therapeutic approaches for terminating SE in the clinic.

neuroscience↗

bioRxiv2024

Mechanical problem solving in mice

Behavioral learning is a complex phenomenon that involves cognitive, perceptual, motor and emotional contributions. Yet, most animal studies focus on reductionist tasks that aim to isolate one of these aspects [1, 2, 3, 4]. Here, we use a lockbox -- a complex, multi-step mechanical puzzle -- to study learning dynamics in freely behaving mice. The mice engaged spontaneously with the task and learned to solve it within just a few trials. To dissect different contributions to this rapid form of learning, we combined deep learning-based behavioral tracking in a multi-camera setup with probabilistic inference and computational modeling. We find that the learning progress of the mice was initially dominated by the acquisition of motor skills, i.e., the increased ability to manipulate the individual mechanisms, while a cognitive strategy for the task sequence emerged only later. The lockbox paradigm may hence offer a promising framework for studying the interaction between low-level motor learning and high-level decision-making strategies in a single, ethologically relevant task.

animal behavior and cognition↗

bioRxiv2024

Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

Synaptic plasticity is a key player in the brains life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta-learn local co-active plasticity rules in large recurrent spiking net-works, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta-learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain net-work dynamics to plausible solutions a priori. Moreover, in line with previous work, we can [fi]nd multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.

neuroscience↗

bioRxiv2024

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally-generated top-down information. In the neocortex, top-down projections predominantly target layer 1, which contains NDNF-expressing interneurons, nestled between the dendrites of pyramidal cells (PCs). Here, we propose that NDNF interneurons shape cortical computations by presynap-tically inhibiting the outputs of somatostatin-expressing (SOM) interneurons via GABAergic volume transmission in layer 1. Whole-cell patch clamp recordings from genetically identified NDNF INs in layer 1 of the auditory cortex show that SOM-to-NDNF synapses are indeed modulated by ambient GABA. In a cortical microcircuit model, we then demonstrate that this mechanism can control inhibition in a layer-specific way and introduces a competition for dendritic inhibition between NDNF and SOM interneurons. This competition is mediated by a unique mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons, which can dynamically prioritise different inhibitory signals to the PC dendrite. NDNF interneurons can thereby control information flow in pyramidal cells by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition, as exemplified in a predictive coding application. This work corroborates that NDNF interneurons are ideally suited to control information flow within cortical layer 1.

neuroscience↗

bioRxiv2023

Transcriptomic correlates of state modulation in forebrain interneurons: A cross-species analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical and functional properties. In mouse visual cortex, they also differ in their modulation with an animals behavioural state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalises across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissimilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.

neuroscience↗

bioRxiv2023

Cortical interneurons: fit for function and fit to function? Evidence from development and evolution

Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labour, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalysed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.

neuroscience↗

bioRxiv2023

Encoding of continuous perceptual choices in human early visual cortex

Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex. However, many sensory features are graded rather than discrete, raising the question how choices are encoded when they span the full sensory continuum. Here we assessed this using motion stimuli that could vary anywhere between 0{degrees} and 360{degrees}. We employed a combination of neuroimaging and encoding models based on Gaussian Process Regression to assess how either stimuli or choices were encoded in brain responses. We found that single-voxel tuning patterns could be used to reconstruct the trial-by-trial physical direction of motion as well as the participants continuous choices. Importantly, these continuous choice signals were primarily observed in early visual areas. The tuning properties in this region generalized between choice encoding and stimulus encoding, even for reports that reflected pure guessing. We found only little information related to the decision outcome in regions beyond visual cortex, such as parietal cortex, possibly because our task did not involve differential motor preparation. This could suggest that decisions for continuous stimuli take can place already in sensory brain regions, potentially using similar mechanisms to the sensory recruitment in visual working memory.

neuroscience↗

bioRxiv2022

Control of neocortical memory by long-range inhibition in layer 1

Mounting evidence identifies layer 1 (L1) as a central site of memory in sensory neocortex. While this work revealed plasticity in several excitatory brain-wide afferent systems, the existence, connectivity and memory-related signaling of long-range inhibitory input to L1 remains elusive. We report that inhibitory afferents from zona incerta project specifically to auditory cortex L1, where they connect selectively to interneurons to disinhibit the cortical circuit and facilitate behavioral memory. Chronic calcium imaging of these synapses identifies a balanced form of plasticity that develops rapidly during threat learning and is characterized by the de novo appearance of negative stimulus responses which transmit most information. Our results therefore pinpoint malleability of long-range (dis)inhibitory afferents to L1 as a key factor for the exquisite computational flexibility of this unique layer.

neuroscience↗

bioRxiv2021

Invariant neural subspaces maintained by feedback modulation

Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.

neuroscience↗

bioRxiv2021

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts. Author summaryThe language of the brain consists of sequences of action potentials. These sequences often contain bursts, short "words" consisting of a few action potentials in rapid succession. Bursts appear to play a special role in the brain. They indicate whether a stimulus was perceived or missed, and they are very effective drivers of synaptic plasticity, the neural substrate of learning. Bursts occur rarely, but consistently, a condition that is thought to maximize the amount of information they can communicate. In our article, we argue that this condition is far from self-evident, but requires very special circumstances. We show that these circumstances can be reached by homeostatic inhibitory plasticity in certain inhibitory neuron types. This may sound complicated, but basically works just like a thermostat. When bursts occur too often, inhibition goes up and suppresses them. When they are too rare, inhibition goes down and thereby increases their number. In computer simulations, we show that this simple mechanism can create circumstances akin to those in the brain, and indeed allows bursts to convey information effectively. Whether this mechanism is indeed used by the brain now remains to be tested by our experimental colleagues.

neuroscience↗

bioRxiv2020

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways -- two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting -- as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years. Author summaryAfter new memories are acquired, they can be transferred over time into other brain areas -- a process called systems memory consolidation. For example, new declarative memories, which refer to the conscious memory of facts and events, depend on the hippocampus. Older declarative memories, however, also rely on neocortical networks. The cellular mechanisms underlying such a transfer are poorly understood. In this work, we show that a simple and in the brain ubiquitous connectivity pattern, combined with a standard learning rule, leads to gradual memory transfer. We illustrate our proposed mechanism in numerical simulations and mathematical analyses. At the neurophysiological level, our theory explains experimental findings on memory storage in the hippocampal formation when specific pathways between neural populations are disrupted. At the psychophysical level, we can account for the power-law forgetting curves typically found in humans. A consequence of the proposed model is that consolidated memories can yield faster responses because they are stored in increasingly shorter synaptic pathways between sensory and motor areas. By giving a mechanistic explanation of the consolidation process, we contribute to the understanding of the transfer of memories and the reorganization of memories over time.

neuroscience↗

bioRxiv2020

Interneuron diversity is required for compartment-specific feedback inhibition

Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. The resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models.

neuroscience↗

bioRxiv2020

Learning excitatory-inhibitory neuronal assemblies in recurrent networks

In sensory circuits with poor feature topography, stimulus-specific feedback inhibition necessitates carefully tuned synaptic circuitry. Recent experimental data from mouse primary visual cortex (V1) show that synapses between pyramidal neurons and parvalbumin-expressing (PV) inhibitory interneurons tend to be stronger for neurons that respond to similar stimulus features. The mechanism that underlies the formation of such excitatory-inhibitory (E/I) assemblies is unresolved. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Using a computational model, we show that both forms of plasticity must act synergistically to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can enable inhibitory circuits to actively shape cortical computations.

neuroscience↗

bioRxiv2020

Learning prediction error neurons in a canonical interneuron circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

neuroscience↗

bioRxiv2020

Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity

Hebbian plasticity, a mechanism believed to be the substrate of learning and memory, detects and further enhances correlated neural activity. Because this constitutes an unstable positive feedback loop, it requires additional homeostatic control. Computational work suggests that in recurrent networks, the homeostatic mechanisms observed in experiments are too slow to compensate instabilities arising from Hebbian plasticity and need to be complemented by rapid compensatory processes. We suggest presynaptic inhibition as a candidate that rapidly provides stability by compensating recurrent excitation induced by Hebbian changes. Presynaptic inhibition is mediated by presynaptic GABA receptors that effectively and reversibly attenuate transmitter release. Activation of these receptors can be triggered by excess network activity, hence providing a stabilising negative feedback loop that weakens recurrent interactions on sub-second timescales. We study the stabilising effect of presynaptic inhibition in a recurrent networks, in which presynaptic inhibition is implemented as a multiplicative reduction of recurrent synaptic weights in response to increasing inhibitory activity. We show that networks with presynaptic inhibition display a gradual increase of firing rates with growing excitatory weights, in contrast to traditional excitatory-inhibitory networks. This alleviates the positive feedback loop between Hebbian plasticity and network activity and thereby allows homeostasis to act on timescales similar to those observed in experiments. Our results generalise to spiking networks with a biophysically more detailed implementation of the presynaptic inhibition mechanism. In conclusion, presynaptic inhibition provides a powerful compensatory mechanism that rapidly reduces effective recurrent interactions and thereby stabilises Hebbian learning.

neuroscience↗