Biology subjects

Triesch, J.

Publications and source records attributed to Triesch, J..

18 recordsLinked to original sources

bioRxiv2026

An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress

Neuronal computation is constrained by the energetic cost of maintaining synaptic transmission, ionic gradients and action-potential generation. Prior work has shown that food restriction in mice shifts visual cortical neurons toward a different operating regime characterized by reduced excitatory synaptic drive, increased membrane resistance and a more depolarized resting membrane potential. This preserves neuronal firing rates, but comes at the cost of reduced coding precision. Deriving an improved estimate of neuronal energy consumption, we have recently shown that these adaptations reflect an optimal trade-off between neuronal information transmission and energy consumption. However, it remains unclear through which control strategies individual neurons adapt their biophysical parameters to maintain a favorable energy-information trade-off. Here we consider several candidate models implementing different control strategies. A "synapses-first" model, in which metabolic stress drives a downscaling of synaptic efficacies to save energy, while an intrinsic plasticity mechanism increases excitability to preserve the average firing rate, successfully reproduces key experimental observations. Alternative models in which energy shortages drive changes in intrinsic excitability or firing-rate variability can also reproduce the endpoint of neuronal adaptation, but predict different time courses and exhibit less energy-efficient and less stable dynamics. New experiments are needed to distinguish among these competing models.

neuroscience↗

bioRxiv2026

Bats use Dip Echolocation to overcome rhythmic noise

Active sensing systems are known to adapt the structure of sensory signals. Whether they can improve perception by controlling when sensory information is acquired remains unclear. We show that echolocating fruit bats exposed to rhythmic noise preferentially emit calls during recurring low-noise periods, a behaviour we term "dip echolocation". Dip echolocation occurred in laboratory and wild bats and represents an active-sensing analogue of dip listening in humans. A normative model showed that temporal positioning of calls emerges from a trade-off between sensory information and energetic cost, alongside concurrent adaptations of call structure. Pharmacological inactivation of the frontal auditory field disrupted precise temporal control, implicating a role for frontal cortical circuits in adaptive vocal timing. These findings identify adaptive vocal timing as an active-sensing strategy for overcoming acoustic interference.

animal behavior and cognition↗

bioRxiv2026

Neuronal degeneracy reflects context-specific trade-offs between energy and information

Among neurons of the same type, the different electrophysiological parameters vary drastically. It is presently unclear if there is any hidden structure in this diversity and how it relates to neuronal function. One potential source of structure is the evolutionary pressure that has sculpted brains to work in an energy-efficient manner. Here we show that neurons parameters do not vary randomly, but that neurons populate a manifold within their parameter space permitting energy-efficient signaling. This manifold represents a Pareto front of degenerate solutions to the problem of energy-efficiency and explains the prevalence of low to intermediate firing rates (2 - 5 Hz) in neocortex. Furthermore, we show that neurons in different sensory brain areas populate different manifolds and that food restriction induces systematic shifts of neuronal parameters along this manifold. Our results suggest that the large diversity of neuronal properties is actually tightly regulated to ensure energy-efficient signaling in different contexts. In briefWe show that there is hidden structure in the diversity of electrophysiological properties of neurons that is governed by trade-offs between energy consumption and information transmission. HighlightsO_LIWe derive an updated neuronal energy budget for a large class of neuron models. C_LIO_LIComputational modeling predicts a manifold of degenerate solutions to energy-efficient information coding forming a Pareto front in the neuronal parameter space. C_LIO_LINeurons from visual and somatosensory cortex fall on the predicted manifold. C_LIO_LIOptimal energy-efficiency is achieved for intermediate-low firing rates (2 - 5 Hz) as observed in neocortex. C_LIO_LIWe observe an energy-efficient form of non-multiplicative synaptic scaling during food deprivation. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/708507v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@ca6d36org.highwire.dtl.DTLVardef@1c81ca3org.highwire.dtl.DTLVardef@d03833org.highwire.dtl.DTLVardef@19bc7c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Hippocampal network activity changes during early epileptogenesis predict subsequent epilepsy

The circuit mechanisms underlying focal epileptogenesis are, despite of decades of epilepsy research, still incompletely understood. In this study, we aimed to characterize the changes in hippocampal network activity induced by a potentially epileptogenic insult. In rats, long-lasting electrical perforant pathway stimulation leads in a high percentage of animals to the development of temporal lobe epilepsy. However, a subset of animals remains resilient against the stimulation. We monitored alterations of neuronal activity by chronically recording the local field potential (LFP) from the hippocampal dentate gyrus before, during and after the potentially epileptogenic insult. Intriguingly, epilepsy animals identified by subsequent spontaneous epileptic seizures were characterized by a transient increase in the aperiodic exponent suggesting a shift towards a reduced local excitation-to-inhibition (E/I) ratio during the first days after the perforant path stimulation. Furthermore, these animals developed a strong impairment of theta oscillation prevalence and regularity during early epileptogenesis. In contrast, resilient non-epilepsy animals without spontaneous seizures neither showed this modulation in E/I ratio nor a corruption of hippocampal theta activity. In fact, the increase in the aperiodic exponent on the first day after completion of the electrical stimulation paradigm could predict epileptogenesis with very high fidelity (AUC 0.92) and correlated significantly with later seizure rate. This finding opens the opportunity to dissect mechanisms of epileptogenesis and to test the effectiveness of anti-epileptogenesis treatment in very early disease stages by allowing identification of individuals at high risk. Furthermore, it might offer a potential explanation for the frequently observed failure of anti-epileptogenesis drugs boosting GABAergic inhibition.

neuroscience↗

bioRxiv2025

Homeostatic regulation across fast and slow timescales through aggregate synaptic dynamics

Neuronal firing rates must remain stable despite continuous synaptic modifications driven by Hebbian plasticity. Two compensatory mechanisms have been implicated in this stability. Heterosynaptic plasticity, which rapidly redistributes synaptic strength among competing inputs, and synaptic scaling, which adjusts synaptic efficacies globally on slow timescales. How these interact to maintain stable activity remains unclear. Here, we propose a unified framework in which local synaptic competition for a shared resource pool, combined with slow cell-wide homeostatic regulation of the total pool size, prevents runaway synaptic dynamics and ensures firing rate homeostasis. We show analytically that this architecture guarantees stability regardless of the timescale difference between Hebbian and homeostatic plasticity, and that the ratio of sensing and regulatory timescales determines whether convergence is monotonic or oscillatory. Fitting the model to activity blockade data reveals an inverse coupling between these timescales. When competition is spatially restricted to dendritic compartments, the framework reproduces experimentally observed synaptic weight distributions and local homeostatic upscaling following denervation, suggesting that heterosynaptic competition and synaptic scaling share a common mechanistic origin.

neuroscience↗

bioRxiv2023

A developmental increase of inhibition promotes the emergence of hippocampal ripples

Sharp wave-ripples (SPW-Rs) are a hippocampal network phenomenon critical for memory consolidation and planning. SPW-Rs have been extensively studied in the adult brain, yet their developmental trajectory is poorly understood. While SPWs have been recorded in rodents shortly after birth, the time point and mechanisms of ripple emergence are still unclear. Here, we combine in vivo electrophysiology with optogenetics and chemogenetics in 4 to 12 days-old mice to address this knowledge gap. We show that ripples are robustly detected and induced by light stimulation of ChR2-transfected CA1 pyramidal neurons only from postnatal day (P) 10 onwards. Leveraging a spiking neural network model, we mechanistically link the maturation of inhibition and ripple emergence. We corroborate these findings by reducing ripple rate upon chemogenetic silencing of CA1 interneurons. Finally, we show that early SPW-Rs elicit a more robust prefrontal cortex response then SPWs lacking ripples. Thus, development of inhibition promotes ripples emergence.

neuroscience↗

bioRxiv2023

Longitudinal Changes in Value-based Learning in Middle Childhood: Distinct Contributions of Hippocampus and Striatum

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Childrens learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

neuroscience↗

bioRxiv2023

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

neuroscience↗

bioRxiv2021

A Computational Model of the Effect ofShort-Term Monocular Deprivation onBinocular Rivalry in the Context of Amblyopia

Treatments for amblyopia focus on vision therapy and patching of one eye. Predicting the success of these methods remains difficult, however. Recent research has used binocular rivalry to monitor visual cortical plasticity during occlusion therapy, leading to a successful prediction of the recovery rate of the amblyopic eye. The underlying mechanisms and their relation to neural homeostatic plasticity are not known. Here we propose a spiking neural network to explain the effect of short-term monocular deprivation on binocular rivalry. The model reproduces perceptual switches as observed experimentally. When one eye is occluded, inhibitory plasticity changes the balance between the eyes and leads to longer dominance periods for the eye that has been deprived. The model suggests that homeostatic inhibitory plasticity is a critical component of the observed effects and might play an important role in the recovery from amblyopia.

neuroscience↗

bioRxiv2021

A phenomenological model of neuroimmune interactions in epileptogenesis

Epilepsy can have many different causes and its development (epileptogenesis) involves a bewildering complexity of interacting processes. Here, we present a first-of-its-kind computational model to better understand the role of neuroimmune interactions in the development of acquired epilepsy. Our model describes the interactions between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model is validated using data from animal models that mimic human epileptogenesis caused by infection, status epilepticus, and blood-brain barrier disruption. The mathematical model successfully explains characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries, or its dependence on the intensity of an injury. Furthermore, stochasticity in the model captures the variability of epileptogenesis outcomes in individuals exposed to identical injury. Notably, in line with the concept of degeneracy, our simulations reveal multiple routes towards epileptogenesis with neuronal loss as a sufficient but non-necessary component. We show that our framework allows for in silico predictions of therapeutic strategies, providing information on injury-specific therapeutic targets and optimal time windows for intervention.

neuroscience↗

bioRxiv2021

Biological complexity facilitates tuning of the neuronal parameter space

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability. Significance statementOver the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands of searching for viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with fewer ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for this improved distribution of valid model parameters, we propose that this may generalise to evolutionary selection in other complex biological systems. In briefStudying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but because of their complexity. HighlightsO_LI15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour. C_LIO_LIBut knocking out ion channels can be compensated only in the full model. C_LIO_LIRandom sampling leads to ~ 6% solutions in full but only ~ 1% in reduced model. C_LIO_LILaw of large numbers generalises our observations to other complex biological systems. C_LI

neuroscience↗

bioRxiv2020

The Development of Active Binocular Vision under Normal and Alternate Rearing Conditions

The development of binocular vision is an active learning process comprising the development of disparity tuned neurons in visual cortex and the establishment of precise vergence control of the eyes. We present a computational model for the learning and self-calibration of active binocular vision based on the Active Efficient Coding framework, an extension of classic efficient coding ideas to active perception. Under normal rearing conditions, the model develops disparity tuned neurons and precise vergence control, allowing it to correctly interpret random dot stereogramms. Under altered rearing conditions modeled after neurophysiological experiments, the model qualitatively reproduces key experimental findings on changes in binocularity and disparity tuning. Furthermore, the model makes testable predictions regarding how altered rearing conditions impede the learning of precise vergence control. Finally, the model predicts a surprising new effect that impaired vergence control affects the statistics of orientation tuning in visual cortical neurons.

neuroscience↗

bioRxiv2019

Active Efficient Coding Explains the Development of Binocular Vision and its Failure in Amblyopia

The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a new formulation of the Active Efficient Coding theory, which proposes that eye movements, as well as stimulus encoding, are jointly adapted to maximize the overall coding efficiency. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to co-ordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops, in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refractive errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision. Significance StatementBrains must operate in an energy-efficient manner. The efficient coding hypothesis states that sensory systems achieve this by adapting neural representations to the statistics of sensory input signals. Importantly, however, these statistics are shaped by the organisms behavior and how it samples information from the environment. Therefore, optimal performance requires jointly optimizing neural representations and behavior, a theory called Active Efficient Coding. Here we test the plausibility of this theory by proposing a computational model of the development of binocular vision. The model explains the development of accurate binocular vision under healthy conditions. In the case of refractive errors, however, the model develops an amblyopia-like state and suggests conditions for successful treatment.

neuroscience↗

bioRxiv2018

Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence

Synapses between cortical neurons are subject to constant modifications through synaptic plasticity mechanisms, which are believed to underlie learning and memory formation. The strengths of excitatory and inhibitory synapses in the cortex follow a right-skewed long-tailed distribution. Similarly, the firing rates of excitatory and inhibitory neurons also follow a right-skewed long-tailed distribution. How these distributions come about and how they maintain their shape over time is currently not well understood. Here we propose a spiking neural network model that explains the origin of these distributions as a consequence of the interaction of spike-timing dependent plasticity (STDP) of excitatory and inhibitory synapses and a multiplicative form of synaptic normalisation. Specifically, we show that the combination of additive STDP and multiplicative normalisation leads to lognormal-like distributions of excitatory and inhibitory synaptic efficacies as observed experimentally. The shape of these distributions remains stable even if spontaneous fluctuations of synaptic efficacies are added. In the same network, lognormal-like distributions of the firing rates of excitatory and inhibitory neurons result from small variability in the spiking thresholds of individual neurons. Interestingly, we find that variation in firing rates is strongly coupled to variation in synaptic efficacies: neurons with the highest firing rates develop very strong connections onto other neurons. Finally, we define an impact measure for individual neurons and demonstrate the existence of a small group of neurons with an exceptionally strong impact on the network, that arise as a result of synaptic plasticity. In summary, synaptic plasticity and small variability in neuronal parameters underlie a neural oligarchy in recurrent neural networks.\n\nAuthor summaryOur brains neural networks are composed of billions of neurons that exchange signals via trillions of synapses. Are these neurons created equal, or do they contribute in similar ways to the network dynamics? Or do some neurons wield much more power than others? Recent experiments have shown that some neurons are much more active than the average neuron and that some synaptic connections are much stronger than the average synaptic connection. However, it is still unclear how these properties come about in the brain. Here we present a neural network model that explains these findings as a result of the interaction of synaptic plasticity mechanisms that modify synapses efficacies. The model reproduces recent findings on the statistics of neuronal firing rates and synaptic efficacies and predicts a small class of neurons with exceptionally high impact on the network dynamics. Such neurons may play a key role in brain disorders such as epilepsy.

neuroscience↗

bioRxiv2018

EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities

BackgroundCorticospinal excitability depends on the current brain state. The recent development of real-time EEG-triggered transcranial magnetic stimulation (EEG-TMS) allows studying this relationship in a causal fashion. Specifically, it has been shown that corticospinal excitability is higher during the scalp surface negative EEG peak compared to the positive peak of {micro}-oscillations in sensorimotor cortex, as indexed by larger motor evoked potentials (MEPs) for fixed stimulation intensity.\n\nObjectiveWe further characterize the effect of {micro}-rhythm phase on the MEP input-output (IO) curve by measuring the degree of excitability modulation across a range of stimulation intensities. We furthermore seek to optimize stimulation parameters to enable discrimination of functionally relevant EEG-defined brain states.\n\nMethodsA real-time EEG-TMS system was used to trigger MEPs during instantaneous brain-states corresponding to {micro}-rhythm surface positive and negative peaks with five different stimulation intensities covering an individually calibrated MEP IO curve in 15 healthy participants.\n\nResultsMEP amplitude is modulated by {micro}-phase across a wide range of stimulation intensities, with larger MEPs at the surface negative peak. The largest relative MEP-modulation was observed for weak intensities, the largest absolute MEP-modulation for intermediate intensities. These results indicate a leftward shift of the MEP IO curve during the {micro}-rhythm negative peak.\n\nConclusionThe choice of stimulation intensity influences the observed degree of corticospinal excitability modulation by {micro}-phase. Lower stimulation intensities enable more efficient differentiation of EEG {micro}-phase-defined brain states.

neuroscience↗

bioRxiv2017

Ongoing brain rhythms shape I-wave properties in a computational model

BackgroundResponses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity for the development of more effective stimulation protocols through closed-loop TMS-EEG. However, it is not yet clear how features of ongoing activity affect the responses of cortical circuits to TMS.\n\nObjective/HypothesisHere we investigate the dependence of TMS-responses on power and phase of ongoing oscillatory activity in a computational model of TMS-induced I-waves.\n\nMethodsThe model comprises populations of cortical layer 2/3 (L2/3) neurons and a population of cortical layer 5 (L5) neurons and generates I-waves in response to TMS. Oscillatory input to the L2/3 neurons induces rhythmic fluctuations in activity of L5 neurons. TMS pulses are simulated at different phases and amplitudes of the ongoing rhythm.\n\nResultsThe model shows a robust dependence of I-wave properties on phase and power of ongoing rhythms, with the strongest response occurring for TMS at maximal L5 depolarization. The amount of phase-modulation depends on stimulation intensity, with stronger modulation for lower intensity.\n\nConclusionThe model predicts that responses to TMS are highly variable for low stimulation intensities if ongoing brain rhythms are not taken into account. Closed-loop TMS-EEG holds promise for obtaining more reliable TMS effects.

neuroscience↗

bioRxiv2017

Competition for a limited supply of synaptic building blocks predicts multiplicative synaptic normalization and heterosynaptic plasticity

Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.

neuroscience↗

bioRxiv2017

Learning of Active Binocular Vision in a Biomechanical Model of the Oculomotor System

We present a model for the autonomous learning of active binocular vision using a recently developed biome-chanical model of the human oculomotor system. The model is formulated in the Active Efficient Coding (AEC) framework, a recent generalization of classic efficient coding theories to active perception. The model simultaneously learns how to efficiently encode binocular images and how to generate accurate vergence eye movements that facilitate efficient encoding of the visual input. In order to resolve the redundancy problem arising from the actuation of the eyes through antagonistic muscle pairs, we consider the metabolic costs associated with eye movements. We show that the model successfully learns to trade off vergence accuracy against the associated metabolic costs, producing high fidelity vergence eye movements obeying Sherringtons law of reciprocal innervation.

neuroscience↗