Biology subjects

Kempter, R.

Publications and source records attributed to Kempter, R..

15 recordsLinked to original sources

bioRxiv2026

Elementary Dynamics of Neural Microcircuits

Interactions between distinct populations of excitatory (E) and inhibitory (I) neurons can produce complex dynamical landscapes, featuring multistability, oscillations, and paradoxical perturbation responses. By employing an elementary model, the threshold-linear network (TLN), we indicate mathematical conditions for each dynamical regime across fundamental microcircuit architectures, thereby mapping previously unrelated systems neuroscience hypotheses to a common reference space and obtaining novel insights on inputs and connectivity. Namely, we compare balancing strategies in inhibition-stabilized E-I networks, we interpret experiments on gamma oscillations in a canonical neocortical E-I-I circuit, and we discuss bistability in hippocampal E-I-I networks. Then, we show that connectivity determines three fundamentally different kinds of interactions between assemblies in E-E-I circuits. Moreover in, E-E-I-I circuits we find that balanced clustering hinders lateral inhibition, while opponent clustering can produce different bistable configurations, even between completely unstructured assemblies. We conclude that TLNs allow to grasp deep and universal aspects of microcircuit dynamics.

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bioRxiv2025

Inhibitory inputs to avian ITD circuits

In birds, detection of sound source azimuth begins in the nucleus laminaris (NL), which computes interaural time differences (ITDs). Inhibition has been proposed to protect NL neurons from losing ITD sensitivity with increasing sound level, although the nature of this inhibition is incompletely understood. Anatomical studies show GABAergic synapses throughout NL and the first order nuclei, while in vitro studies in chicken NL showed that depolarizing, GABAergic inhibitory postsynaptic potentials (IPSPs) shorten membrane time constants, perhaps to allow the membrane potential to follow rapid synaptic currents accurately over a range of sound levels. Given the importance of inhibition in regulating auditory brainstem activity, we examined the nature of the inhibitory input to NL in vivo. The Superior Olivary Nucleus (SON) is the major source of descending GABAergic inhibition to the ipsilateral nucleus magnocellularis (NM), nucleus angularis (NA) and NL, and receives excitatory input from NA and NL. We used viral tracers to reveal projections from SON to NL, and characterized response types within SON. In NL, we isolated inhibitory synaptic contributions from extracellular field potentials through the iontophoretic application of blockers of GABA (gabazine) or glycine (strychnine). These blockers increase the onset and offset responses evoked by tonal stimuli, but did not shift best ITD. The co-application of gabazine and strychnine revealed supra-linear summation of GABA and glycine. Profiles of synaptic activation revealed more prominent inhibition following stimulus offset, suggesting inputs to NL originate from both the sustained and offset response types of the SON. These heterogeneous responses may represent separate SON neuronal populations.

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bioRxiv2025

Conditions for replay of neuronal assemblies

From cortical synfire chains to hippocampal replay, the idea that neural populations can be activated sequentially with precise spike timing is thought to be essential for several brain functions. It has been shown that neuronal sequences with weak feedforward connectivity can be replayed due to amplification via intra-assembly recurrent connections. However, the mechanisms behind this phenomenon are still unclear. Here, we simulate spiking networks with different excitatory and inhibitory connectivity and find that an exclusively excitatory network is sufficient for this amplification to occur. To explain the spiking network behavior, we introduce a population model of membrane-potential distributions, and we analytically describe how different connectivity structures determine replay speed, with weaker feedforward connectivity generating slower and wider pulses that can be sustained by recurrent connections. Pulse propagation is facilitated if the neuronal membrane time constant is large compared to the pulse width. Together, our simulations and analytical results predict the conditions for replay of neuronal assemblies. Author summaryIn this work, we study how neural activity can propagate through a network of neurons. We therefore consider the activities of defined groups of neurons, which are also called assemblies. We are particularly interested in understanding how a previously learned sequence of activity patterns of assemblies can be replayed. To this aim, we combine large-scale numerical simulations with simpler analytical descriptions of neural dynamics. Our simulations show that, if feedforward connections across assemblies are weak, pulses of activity can be amplified by intra-assembly recurrent connections, allowing for sequence retrieval across a wide range of network structures and parameters. We introduce a new theoretical framework to study sequential activity, deriving conditions for sequence replay to succeed, and unveiling how replay speed depends on different network parameters. Crucially, we find that subthreshold membrane potential distributions are essential in determining the properties of the activity pulse and whether replay can succeed. Our findings contribute to understanding the mechanisms of hippocampal replay in particular, which is important for memory consolidation, as well as the propagation of activity across feedforward neuronal circuits in general.

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bioRxiv2025

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks

The brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is sparse coding, which can reduce overlap between representations and minimize interference. It is well known that increased sparseness can enhance memory capacity, yet its impact on the speed of learning remains poorly understood. Here we analyze the relationship between population sparseness and learning speed -- specifically, how the learning speed that maximizes memory capacity depends on the sparseness of the neural code, and how this in turn affects the networks maximal capacity. To this end, we study a feedforward network with Hebbian and homeostatic plasticity and a two-state synapse model. The network learns to associate binary input-output pattern pairs, where sparseness corresponds to a small fraction of active neurons per pattern. The learning speed is modeled as the probability of synaptic changes during learning. The results presented in this manuscript are based on both network simulations and an analytical theory that predicts expected memory capacity and optimal learning speed. For both perfect and noisy retrieval cues, we find that the optimal learning speed indeed increases with increasing pattern sparseness -- an effect that is more pronounced for input sparseness than for output sparseness. Interestingly, the optimal learning speed stays the same across different network sizes if the number of active units in an input pattern is kept constant. While the capacity obtained at optimal learning speed increases monotonically with output sparseness, its dependence on input sparseness is non-monotonic. Overall, we provide the first detailed investigation of the interactions between population sparseness, learning speed, and storage capacity. Our findings propose that differences in population sparseness across brain regions may underlie observed differences in how quickly those regions adapt and learn. Author summaryThe brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is the way neural circuits encode and organize information about the external world. Population sparseness refers to the phenomenon in which, in many brain regions, only a small subset of neurons is active at any given time or in response to a particular stimulus. Sparse codes are believed to reduce overlap between representations and minimize interference and can thus enhance the storage capacity of a network. Here we investigate the effect of population sparseness on the speed of learning input-output associations in a network model with Hebbian learning. We find that the learning speed that yields the maximal capacity increases with increasing sparseness. The maximal capacity increases for sparser input and output representations but in the first case only up to a certain point before sparseness becomes destructive. These findings could contribute to explaining why some brain regions learn faster than others.

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bioRxiv2024

Single neuron contributions to the auditory brainstem EEG

The auditory brainstem response (ABR) is an acoustically evoked EEG potential that is an important diagnostic tool for hearing loss, especially in newborns. The ABR originates from the response sequence of auditory nerve and brainstem nuclei, and a click-evoked ABR typically shows three positive peaks ( waves) within the first six milliseconds. However, an assignment of the waves of the ABR to specific sources is difficult, and a quantification of contributions to the ABR waves is not available. Here, we exploit the large size and physical separation of the barn owl first-order cochlear nucleus magnocellularis (NM) to estimate single-cell contributions to the ABR. We simultaneously recorded NM neurons spikes and the EEG in owls of both sexes, and found that {gtrsim} 5, 000 spontaneous single-cell spikes are necessary to isolate a significant spike-triggered average response at the EEG electrode. An average single-neuron contribution to the ABR was predicted by convolving the spike-triggered average with the cells peri-stimulus time histogram. Amplitudes of predicted contributions of single NM cells typically reached 32.9 {+/-} 1.1 nV (mean {+/-} SE, range: 2.5 - 162.7 nV), or 0.07 {+/-} 0.02% (median {+/-} SE; range from 0.01% to 1%) of the ABR amplitude. The time of the predicted peak coincided best with the peak of the ABR wave II, independent of the click sound level. Our results suggest that individual neurons contributions to an EEG can vary widely, and that wave II of the ABR is shaped by NM units. Significance statementThe auditory brainstem response (ABR) is a scalp potential used for the diagnosis of hearing loss, both clinically and in research. We investigated the contribution of single action potentials from auditory brainstem neurons to the ABR and provide direct evidence that action potentials recorded in a first order auditory nucleus, and their EEG contribution, coincide with wave II of the ABR. The study also shows that the contribution of single cells varies strongly across the population.

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bioRxiv2024

Sub-type specific connectivity between CA3 pyramidal neurons may underlie their sequential activation during sharp waves

The CA3 region of the hippocampus is the major site of sharp wave initiation, and a brain region crucially involved in learning and memory. Highly recurrent connectivity within its excitatory network is thought to underlie processes involved in memory formation. Recent work has indicated that distinct subpopulations of pyramidal neurons within this region may contribute differently to network activity, including sharp waves, in CA3. Exactly how these contributions may arise is not yet known. Here, we disentangle the local connectivity between two distinct CA3 cell types: thorny and athorny pyramidal cells. We find an asymmetry in the connectivity between these two populations, with athorny cells receiving strong input from both athorny and thorny cells. Conversely, the thorny cell population receives very little input from the athorny population. Computational modeling suggests that this connectivity scheme may determine the sequential activation of these cell types during large network events such as sharp waves.

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bioRxiv2023

Experience-Dependent Plasticity in Nucleus Laminaris of the Barn Owl

Barn owls experience increasing interaural time differences (ITDs) during development, because their head width more than doubles in the month after hatching. We therefore hypothesized that their ITD detection circuit might be modified by experience. To test this, we raised owls with unilateral ear inserts that delayed and attenuated the acoustic signal, then measured the ITD representation in the brainstem nucleus laminaris (NL) when they were adult. The ITD circuit is composed of delay line inputs to coincidence detectors, and we predicted that plastic changes would lead to shorter delays in the axons from the manipulated ear, and complementary shifts in ITD representation on the two sides. In owls that received ear inserts starting around P14, the maps of ITD shifted in the predicted direction, but only on the ipsilateral side, and only in those tonotopic regions that had not experienced auditory stimulation prior to insertion. The contralateral map did not change. Experience-dependent plasticity of the ITD circuit occurs in NL, and our data suggest that ipsilateral and contralateral delays are independently regulated. Thus, altered auditory input during development leads to long-lasting changes in the representation of ITD. Significance StatementThe early life of barn owls is marked by increasing sensitivity to sound, and by increasing ITDs. Their prolonged post-hatch development allowed us to examine the role of altered auditory experience on the development of ITD detection circuits. We raised owls with a unilateral ear insert and found that their maps of ITD were altered by experience, but only in those tonotopic regions that had not experienced auditory stimulation prior to insertion. Thus experience-induced plasticity allows the sound localization circuits to be customized to individual characteristics, such as the size of the head, and potentially to compensate for natural conductive hearing losses.

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bioRxiv2023

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only the "bifurcation-based" inhibition-first model, which is based on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong drive and strong inhibitory coupling. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the bifurcation-based inhibition-first ripple model.

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bioRxiv2022

Quantitative modeling of the emergence of macroscopic grid-like representations

Grid cells are neurons in the entorhinal cortex that are thought to perform neural computations in support of spatial navigation. When subjects navigate through spatial environments, grid cells exhibit firing fields that are arranged in a triangular grid pattern. As direct recordings of grid cells from the human brain are only rarely possible, functional magnetic resonance imaging (fMRI) studies proposed and described an indirect measure of entorhinal grid-cell activity, which is quantified as a hexadirectional modulation of fMRI activity as a function of the subjects movement direction. However, it still remains unclear how the activity of a population of grid cells may exhibit hexadirectional modulation and thus provides the basis for the hexadirectional modulation of entorhinal cortex activity measured with fMRI. Here, we thus performed numerical simulations and analytical calculations to better understand how the aggregated activity of many grid cells may be hexadirectionally modulated. Our simulations implemented three different hypotheses proposing that the hexadirectional modulation occurs because grid cells show head-direction tuning aligned with the grid axes; are subjected to repetition suppression; or exhibit a bias towards a particular grid phase offset. Our simulations suggest that hexadirectional modulation is best explained by the conjunctive grid by head-direction cell hypothesis, which can produce the strongest and most robust hexasymmetry. In contrast, our simulations including previously observed biological properties of grid cells do not provide clear support for the structure-function mapping hypothesis. Our observations on hexadirectional modulation generated by grid-cell adaptation effects and the available data on adaptation properties of grid cells are insufficient to substantiate or refute the repetition suppression hypothesis. Furthermore, we found that the magnitude of the hexadirectional modulation depends considerably on the subjects navigation pattern. Our results thus indicate that future fMRI studies could be designed to test which of the three hypotheses most likely accounts for the fMRI measure of grid cells. These findings also underline the importance of quantifying the biological properties of single grid cells in humans to further elucidate how hexadirectional modulations of fMRI activity may emerge.

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bioRxiv2021

Learning accurate path integration in a ring attractor model of the head direction system

Ring attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.

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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.

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bioRxiv2020

A neural code of egocentric spatial information in human medial temporal lobe

Spatial navigation relies on neural systems that encode information about places, distances, and directions in relation to the external world or relative to the navigating organism. Since the proposal of cognitive maps, the neuroscience of navigation has focused on allocentric (world-referenced) neural representations including place, grid, and head-direction cells. Here, using single-neuron recordings during virtual navigation, we identify "anchor cells" in the human brain as a neural code for egocentric (self-centered) spatial maps: Anchor cells represent egocentric directions towards "anchor points" located in the environmental center or periphery. Anchor cells were abundant in parahippocampal cortex, supported full vectorial representations of egocentric space, and were integrated into a neural memory network. Neurons encoding allocentric direction complemented anchor-cell activity, potentially assisting anchor cells in transforming percepts into allocentric representations. Anchor cells may facilitate egocentric navigation strategies, may support route planning from egocentric viewpoints, and may underlie the first-person perspective in episodic memories.

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bioRxiv2018

Interneuronal gap junctions increase synchrony and robustness of hippocampal ripple oscillations

1Sharp wave-ripple (SWRs) are important for memory consolidation. Their signature in the hippocampal extracellular field potential (EFP) can be decomposed into a {approx} 100 ms long sharp wave superimposed by {approx} 200 Hz ripple oscillations. How ripple oscillations are generated is currently not well understood. A promising model for the genesis of ripple oscillations is based on recurrent interneuronal networks (INT-INT). According to this hypothesis, the INT-INT network in CA1 receives a burst of excitation from CA3 that generates the sharp wave, and recurrent inhibition leads to an ultrafast synchronization of the CA1 network causing the ripple oscillations; fast-spiking parvalbumin-positive basket cells (PV+BCs) may constitute the ripple-generating interneuronal network. PV+BCs are also coupled by gap junctions (GJs) but the function of GJs for ripple oscillations has not been quantified. Using simulations of CA1 hippocampal networks of PV+BCs, we show that GJs promote synchrony and increase the neuronal firing rate of the interneuronal ensemble, while the ripple frequency is only affected mildly. The promoting effect of GJs on ripple oscillations depends on fast GJ transmission ([lsim] 0.5 ms), which requires proximal gap junction coupling ([lsim] 100 m from soma).

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bioRxiv2017

Dipolar extracellular potentials generated by axonal projections

Extracellular field potentials (EFPs) are an important source of information in neuroscience, but their physiological basis is in many cases still a matter of debate. Axonal sources are typically discounted in modeling and data analysis because their contributions are assumed to be negligible. Here, we show experimentally and theoretically that contributions of axons to EFPs can be significant. Modeling action potentials propagating along axons, we showed that EFPs were prominent in the presence of a terminal zone where axons branch and terminate in close succession, as found in many brain regions. Our models predicted a dipolar far field and a polarity reversal at the center of the terminal zone. We confirmed these predictions using EFPs from the barn owl auditory brainstem where we recorded in nucleus laminaris using a multielectrode array. These results demonstrate that axonal terminal zones produce EFPs with considerable amplitude and spatial reach.

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bioRxiv2017

Encoding variable cortical states with short-term spikepatterns

Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronised bursts of spikes, which lock to the macroscopic 600 Hz EEG waves. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronised bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).\n\nSignificance statementNeurons in the hand area of the primary somatosensory cortex respond to repeated presentation of the same stimulus with variable sequences of spikes, which can be grouped into distinct temporal spike patterns. In a simplified model, we show that such spike patterns are product of synaptic inputs and intrinsic neural properties. This model can reproduce both single-neuron and population responses only when a private variability in each neuron is combined with a multiplicative gain shared over whole population, which fluctuates over trials and might represent the dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.

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