Ernst Strüngmann Institute for Neuroscience

Abstract Repetition is a powerful cue for auditory object formation. In natural environments, however, repetitions are rarely exact copies of sound sequences and often overlap with competing sounds. To characterise the temporal integration dynamics and the evidence required for perceptual organisation of acoustic scenes into objects, we developed a repetition-coherence framework inspired by motion-coherence paradigms in vision. Participants listened to dense tone-cloud sequences in which a subset of tones repeated across cycles, with the proportion of repeating tones indicating coherence. Across two psychophysical tasks (repetition detection and sensorimotor synchronization), repetition coherence had strikingly similar effects: it shaped both the likelihood of object formation and the consistency of subsequent tracking. At high coherence, the emergence of the object and the attainment of a stable tracking phase occurred at the same number of cycles. The behavioural trajectory of synchronization performance, used to infer the dynamics of temporal integration, parallels prior neurophysiological findings in non-human primates, establishing a link between perception and neurophysiology. Moreover, we observed threshold shifts due to unit duration in the detection task, which indicates a dissociation between the effect of temporal scale on the likelihood and timing of object emergence: longer units when repeated reduced detection probability, but emergence timing remained constant. These findings demonstrate convergent behavioural dynamics shaped by bottom-up regularities, while intrinsic temporal integration windows constrain the process differently according to task demands. Overall, repetition coherence provides a principled framework for probing the processes underpinning auditory object formation, revealing common behavioural signatures alongside distinct sensitivities to temporal scale.

Abstract Visual discrimination is traditionally studied with highly controlled, artificial stimuli such as gratings. Yet real-world object discrimination relies on many interacting image dimensions. Here we compared human discrimination performance of oriented gratings with that of natural object morphs, and then asked which stimulus dimensions are most important for morph discrimination. In Experiment 1, participants discriminated between two artificial gratings, two natural grating-like textures, and two natural, morphed stimuli. Morph stimuli were discriminated faster and over a larger stimulus range. In Experiment 2, we separated the contribution of distinct image features, specifically colour, texture, and shape, to the discriminability of the morph stimuli. To this end, we varied two feature dimensions at a time while keeping the third dimension constant. Discrimination was best when colour and texture varied together, resulting in steeper psychometric functions and faster responses than when shape changed. Together, these results suggest a discriminative advantage for natural-object morphs over gratings, driven primarily by local colour and texture information rather than global shape.

Humans predict the timing of imminent events to generate fast and precise actions, decisions, and other behaviors. Such temporal anticipation is critical over wide timescales, and especially salient over the range from hundreds of milliseconds to a few seconds. Despite advances in our understanding of basic timing behavior and its underlying neural mechanisms, it remains an open question whether anticipation is stable across these short time scales. Recent work shows that the brain models the probability density function (PDF) of events across time, suggesting a canonical mechanism for temporal anticipation. Here, we investigate whether this computation holds when the event distribution covers different time spans. We show that, irrespective of the time span, anticipation, measured as reaction time, scales with the event distribution. This demonstrates that the key computation-the estimation of event probability density-is invariant across temporal scales. We further show that the precision of anticipation is also scale invariant which contradicts Weber's law. The results are established in vision and audition, suggesting that the core computations in temporal anticipation are independent of sensory modality. Perceptual systems exploit probability estimation over time independently of temporal scale to anticipate imminent events.

Repetitive transcranial magnetic stimulation (rTMS) induces long-term changes in synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists. We developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS. We implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations. We show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). In line with previous experimental studies, this plasticity was strongly distance dependent and localised to the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression. The plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols.

The remarkable human capacity for bilingual and multilingual acquisition raises fundamental questions about how the brain develops efficient systems for processing multiple languages. In this study, we used neural network models trained on natural speech input to examine how these efficient representations emerge. Our models show that multiple phonological systems can be organized through parallel representations, preserving the unique aspects of each language while maintaining shared articulatory features. This parallel structure scaled effectively from two to three languages without needing additional neural architecture, highlighting the inherent efficiency in multilingual processing. Furthermore, the development of phonological representations varied based on the timing of language exposure, showing how earlier-learned languages shape the acquisition of subsequent ones. These findings imply that the human ability to speak multiple languages may arise from general principles of neural organization that optimize shared resources while maintaining essential distinctions between languages. This work has important implications for language learning, brain plasticity, and cognitive development.

Bats exhibit a unique repertoire of vocal behaviors, with many species employing echolocation to actively sense their environment while using communication calls for social interactions. This review explores the neural circuits underlying these behaviors, from brainstem and midbrain networks that generate and control vocalizations to higher order brain regions, such as the frontal cortex, which may contribute to the modulation of vocal behaviors. Although much is known about brainstem mechanisms for echolocation, less is understood about the neural control of communication calls and the integration of these systems. Recent findings highlight the interplay between auditory, motor, and spatial processing networks in shaping bat vocalizations, with evidence for both shared and distinct neural pathways for echolocation and communication. We propose a framework for vocal production circuits in bats, synthesize findings from diverse species and experimental techniques, and identify key questions to guide future research. This review underscores the importance of bats as models for studying vocal production and how they can provide insights into the evolution and adaptation of neural mechanisms across species.

Visual working memory (VWM) allows brief maintenance of information to serve behavioral goals. Recently, Henderson and colleagues (2022) highlighted this goal-oriented nature, demonstrating that a predictable response allows people to flexibly change a visuo-spatial memory representation into a motor-based one. Can such flexibility also be observed between two visual features, color and space? In this eye-tracking study, participants performed two blocks of trials during which they remembered a centrally presented (250ms) color for 3 seconds, subsequently reporting it on a color-wheel via method-of-adjustment. Critically, this color-wheel was either randomly rotated on every trial (random block) or was identical on every trial (fixed block). In a third block, participants remembered a spatial location. When people remember a color in the fixed color-wheel blocks, specific colors are tied to fixed locations. Do people take advantage of this inherent spatial information to aid behavioral performance? And is this reflected in gaze behavior? Confirming that people can utilize color-space associations, we find higher accuracy and quicker responses in fixed compared to random color-wheel blocks. This replicates comparable recent work (Bae & Chen, 2024). Spatial biases in gaze can reflect the location of a prioritized item (van Ede et al., 2019), which we replicate during the delay of the single-item spatial memory block. Importantly, there was also a bias when the color-wheel was fixed, but none when it was random (as color was not predictive of space). During recall, a further gaze bias towards the correct color was observed, which was larger in the random compared to fixed color-wheel blocks. This may reflect a lack of integration of spatial knowledge in random color-wheel blocks. Together, these results show that behavioral performance can be improved by incorporating spatial information to aid color memory, and that systematic changes in gaze can index this flexible utilization of VWM.

The primate visual brain is characterized by foveal magnification. Here, we show in macaque monkeys that foveal magnification affects the dynamics of saccade initiation. In a visually guided saccade task, saccadic reaction times (SRT) increased with target eccentricity. Notably, we effectively eliminated this increase by scaling the target size according to the foveal magnification factor in the superior colliculus. We then repeated the comparison between non-scaled and scaled targets while changing the task to a delayed, visually guided saccade task. In this task, the saccade was triggered by the foveal fixation offset rather than target onset, such that target onset long before the fixation offset was essentially irrelevant for SRT. In this task, we found that SRT increased with target eccentricity, with a similar rate for both non-scaled and scaled targets, consistent with an attentional scan from the fovea to the target, a recently hypothesized general mechanism of attention.

Neuronal coding and decoding would be compromised if neuronal responses were highly variable. Intriguingly, neuronal spike counts (SCs) show a reduction in across-trial variance ( ) in response to sensory stimulation, when SC variance is normalized by SC mean, that is, when using the Fano factor 1. Inspired by this seminal finding, has also been studied in electroencephalography (EEG) signals, revealing effects of various stimulus and cognitive factors as well as disease states. Here, we empirically show that outside of evoked potentials, the of the EEG or local field potential (LFP) is very highly correlated to the intra-trial variance ( ), which corresponds to the well-known power metric. We propose that the LFP power, rather than the raw LFP signal, should be considered with regard to putative changes of its variability. We quantify LFP power variability as the standard deviation of the logarithm of the power ratio between an active and a baseline condition, normalized by the mean of that log(power ratio), that is the coefficient of variation (CV) of the log(power ratio). This CV(log(power ratio)) is reduced for gamma and alpha power when they are enhanced by stimulation, and it is enhanced for alpha power when it is reduced by stimulation. This suggests a potential inverse relation between changes in band-limited power and the corresponding CV. We propose that the CV(log(power ratio)) is a useful metric that can be computed for numerous existing and future LFP, EEG or MEG datasets, which will provide insights into those signals' frequency-specific variability and how they might be used for neuronal coding and decoding.