Biology subjects

Wu, Y. K.

Publications and source records attributed to Wu, Y. K..

10 recordsLinked to original sources

bioRxiv2026

Inter-regional interactions uncouple within-region inhibition stabilization and paradoxical responses

Paradoxical responses, in which excitatory perturbations of inhibitory neurons reduce their firing rates, are widely regarded as a hallmark of inhibition-stabilized networks (ISNs) and have been observed in multiple brain regions. However, because brain regions are interconnected by long-range projections, it remains unclear whether a paradoxical response observed in a given region reflects inhibition stabilization within that region or instead arises from distributed interactions among regions. Here, using analytically tractable multi-region population models and numerical simulations, we show that inter-regional connections can dissociate local inhibition stabilization from paradoxical responses. The condition for a paradoxical response in a given region is jointly determined by recurrent excitation within that region, feedback mediated through other regions, and the dynamics of those other regions. Consequently, inter-regional coupling can generate a paradoxical response in a region that is not locally inhibition-stabilized or abolish it in a region that is. Thus, in an interconnected network, a paradoxical response is neither necessary nor sufficient for local inhibition stabilization. These findings demonstrate that local perturbation responses cannot generally be interpreted solely in terms of local circuit dynamics and highlight the importance of accounting for inter-regional connections when inferring the local circuit properties of individual brain regions.

neuroscience↗

bioRxiv2026

Characteristics and dynamical signatures of recurrent cortical circuits during context-dependent processing

Context profoundly shapes neural responses and behavior. During context-dependent sensory processing, recurrent connections shape the integration of feedforward sensory input and feed-back input from downstream brain regions. How do different cell types, interacting through spatially structured recurrent lateral connections, give rise to context-dependent processing and circuit stability, and what dynamical signatures reveal their individual roles? To answer these questions, we employ data-driven approaches to construct spatially extended stabilized supralinear network models that capture the responses of diverse cell types in the mouse primary visual cortex during context-dependent processing. Analysis of well-fitting models reveals that the dominant inhibitory cell type affecting excitatory neurons is not fixed but dynamically varies with stimulus and space. While PV-mediated stabilization is indispensable across all models and stimulus conditions, SST-mediated stabilization is also required, and likely in a stimulus-dependent manner. Interestingly, even when a specific inhibitory cell type is required for circuit stabilization, a uniform perturbation of it does not necessarily produce a paradoxical change in its mean activity. Instead, assessing cell-type-specific circuit stabilization requires patterned perturbations, where paradoxical effects manifest along specific activity modes. Finally, we show that recurrent connections and input-output nonlinearities are essential for integrating feedforward and feedback inputs to reproduce the observed spatial response profiles. Recurrent excitatory connections, in particular, are required to account for responses to small stimuli, where external inputs are relatively weak. Taken together, our work reveals the crucial role of ubiquitous biological components in context-dependent processing and delineates the characteristics and dynamical signatures of these circuits.

neuroscience↗

bioRxiv2025

A multi-ring shifter network computes head direction in zebrafish

From insects to fish to mammals, many species have an internal compass: a set of recurrently connected neurons that combine motor feedback, vestibular signals, and external cues to compute the animals heading direction. Whether the underlying mechanism is universal across different species is unresolved. In Drosophila, for instance, the central complex contains three connected, yet anatomically separate, functional neuron rings. Two rings receive countervailing velocity signals that shift neuronal activity bumps around all three rings. An alternative, classical continuous-attractor model invokes only one ring but requires velocity-modulated synaptic connections. A single-ring compass has been discovered in the anterior hindbrain of zebrafish. Using theory and experiments, we find, however, that three rings, including two shifter rings, are intermingled on the same anatomical scaffold. Zebrafish and Drosophila, therefore, use the same basic compass mechanism, suggestive of convergent evolution.

neuroscience↗

bioRxiv2025

Investigating Rat-Brain Normal Tissue and Tumor FLASH Effects with a Novel Very High Energy Electron Beam

PurposeUltra-high dose rate (FLASH) irradiation is reported to reduce normal tissue toxicity while maintaining tumor control, however mechanism(s) remain obscure. To study FLASH mechanisms in brain tissue, we developed a novel experimental platform featuring a unique high-energy electron linear accelerator (High Intensity Gamma Source, HIGS) paired with an organotypic ex vivo brain metastasis model. MethodsWe varied inter-pulse spacing to modulate the mean dose rate (MDR) of our unique 35 MeV electron beam, while maintaining extremely high instantaneous dose rate (IDR), and used film dosimetry to characterize dosimetry and targeting accuracy. We combined this HIGS-FLASH beam with an organotypic rat brain slice/breast carcinoma co-culture model of brain metastasis to assess effects on normal and neoplastic tissues. Live cell and bioluminescence imaging demonstrated cancer cell growth effects, while normal tissue responses and immune activation were assessed with live cell imaging, cytokine profiles, and confocal microscopy. We performed comparison experiments with 20 MeV electrons from a Varian clinical linear accelerator (VCLA) operating at conventional dose rate. ResultsThe highest IDR of the HIGS-FLASH beam to-date was 20.7 {+/-} 0.6 MGy/s, with maximum MDR of 20.7 MGy/s (1 s pulse of 20.7 Gy). Beam targeting was accurate to < 1 mm and reproducible. HIGS-FLASH and VCLA dose rates equivalently decreased cancer cell growth. HIGS-FLASH irradiation significantly increased TNF and fractalkine levels and confocal microscopy revealed distinct changes in microglial morphology in normal brain slices, suggesting microglia activation following HIGS-FLASH irradiation. ConclusionsOur novel experimental platform produces extremely high dose rates and rapid normal/neoplastic tissue readouts for mechanistic research into the effects of FLASH radiation on the brain. HIGS-FLASH irradiation induces comparable cancer cell growth inhibition but differential effects on cytokines and microglial morphology, suggesting that acute innate immune responses may be involved in FLASH normal tissue effects in the brain.

cancer biology↗

bioRxiv2025

Cell-type-specific synaptic scaling mechanisms differentially contribute to associative learning

Excitatory synaptic scaling regulates network dynamics by proportionally adjusting excitatory synaptic strengths after sensory perturbations. During associative learning, blocking excitatory scaling in conditioned taste aversion paradigms prolongs generalized aversive responses and delays memory specificity. Recent evidence also implicates inhibitory synaptic scaling in the regulation of network dynamics. Specifically, parvalbumin (PV)-expressing inhibitory neurons, targeting perisomatic regions of excitatory (E) pyramidal neurons, and somatostatin (SST)-expressing neurons, targeting distal dendrites, exhibit distinct scaling responses. This leaves open the question of how complex plasticity mechanisms regulate recurrent excitatory-inhibitory circuit dynamics in associative learning. Using computational approaches, we demonstrate that Hebbian plasticity drives memory generalization to novel stimuli not presented during conditioning. Following conditioning, diverse synaptic scaling mechanisms progressively induce memory specificity, which can be regulated by top-down inputs. Our results reveal that, in the absence of excitatory scaling, PV-to-E scaling can effectively compensate and rescue memory specificity, highlighting the presence of degenerate mechanisms in the brain. Notably, in the process of establishing memory specificity, excitatory scaling and PV-to-E scaling function synergistically, while concurrently opposing SST-to-E scaling. The synergistic and antagonistic plasticity mechanisms are orchestrated to shape the temporal evolution of memory representations, from generalized to precise. Significance statementAssociative learning is a fundamental brain function that allows us to link experiences, adapt behavior, and form lasting memories. During this process, memory representations are shaped by synaptic scaling, a homeostatic plasticity mechanism that provides slow, negative feedback to regulate synaptic strengths and adjust network excitability. Operating at the synapses of diverse excitatory and inhibitory cell types, multiple forms of homeostatic plasticity influence the dynamics of associative learning. Here, we demonstrate that synergistic and antagonistic cell-type-specific synaptic scaling mechanisms operate at different types of inhibitory synapses to jointly govern the temporal evolution of memory representations. Through their interaction, they guide the transition from generalized to precise memories.

neuroscience↗

bioRxiv2024

Anatomical and functional organization of the interpeduncular nucleus in larval zebrafish

The habenulo-interpeduncular pathway is a highly conserved neural circuit across vertebrates, but the anatomical and functional architecture of the interpeduncular nucleus (IPN) remains poorly understood. Here, we use a combination of immunohistochemistry, volumetric electron microscopy (EM), and two-photon imaging to provide the first detailed characterization of the internal organization of the IPN in larval zebrafish. We show that the IPN receives extensive projections from the tegmentum, and reveal a strict segregation between the dorsal (dIPN) and ventral (vIPN) subcircuits, with minimal cross-communication. In the dIPN, we characterise in detail the inputs and outputs of r1{pi} neurons, which have been recently identified as representing the animals heading direction. In the vIPN, we identify six distinct glomerular structures, each exhibiting specific patterns of reciprocal connections and projection pathways. Finally, we demonstrate that the connectivity and spontaneous activity patterns of habenular axons are shaped by the local anatomical features of the IPN, suggesting a role for the local interneurons in modulating presynaptic dynamics. Together, these results enhance our understanding of the internal organization of the IPN, and provide a framework for future investigations into both its physiology and its involvement in behavior.

neuroscience↗

bioRxiv2023

Top-down modulation in canonical cortical circuits with inhibitory short-term plasticity

Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.

neuroscience↗

bioRxiv2022

Inhibition stabilization and paradoxical effects in recurrent neural networks with short-term plasticity

Inhibition stabilization is considered a ubiquitous property of cortical networks, whereby inhibition controls network activity in the presence of strong recurrent excitation. In networks with fixed connectivity, an identifying characteristic of inhibition stabilization is that increasing (decreasing) excitatory input to the inhibitory population leads to a decrease (increase) in inhibitory firing, known as the paradoxical effect. However, population responses to stimulation are highly nonlinear, and drastic changes in synaptic strengths induced by short-term plasticity (STP) can occur on the timescale of perception. How neuronal nonlinearities and STP affect inhibition stabilization and the paradoxical effect is unclear. Using analytical calculations, we demonstrate that in networks with STP the paradoxical effect implies inhibition stabilization, but inhibition stabilization does not imply the paradoxical effect. Interestingly, networks with neuronal nonlinearities and STP can transition non-monotonically between inhibition-stabilization and non-inhibition-stabilization, and between paradoxically- and non-paradoxically-responding regimes with increasing excitatory activity. Furthermore, we generalize our results to more complex scenarios including networks with multiple interneuron subtypes and any monotonically increasing neuronal nonlinearities. In summary, our work reveals the relationship between inhibition stabilization and the paradoxical effect in the presence of neuronal nonlinearity and STP, yielding several testable predictions.

neuroscience↗

bioRxiv2022

Neural dynamics and architecture of the heading direction circuit in a vertebrate brain

Animals can use different strategies to navigate. They may guide their movements by relying on external cues in their environment or, alternatively, by using an internal cognitive map of the space around them and their position within it. An essential part of this representation are heading cells, neurons whose activity depends on the heading direction of the animal. Although those cells have been found in vertebrates, the full network has never been observed and there is very little mechanistic understanding of how these cells acquire their response properties. In this study, we use volumetric functional imaging in larval zebrafish to observe, for the first time in a vertebrate, a full network that encodes allocentric heading direction. This network of approximately one hundred inhibitory neurons is arranged in an anatomical circle in the anterior hindbrain. Its activity is driven purely by the integration of internally generated signals, indicating that a simple vertebrate brain can encode maps of how an animal moves within its surroundings. Single cell reconstructions of electron micrographs allow us to uncover how the connectivity pattern of neurons within the network supports the implementation of a ring attractor network. The neurons we identify share features with neurons in the dorsal tegmentum nucleus of rodents and the fly central complex, showing that similar connectivity and mechanistic principles underlie the generation of cognitive maps of heading direction across the animal kingdom.

neuroscience↗

bioRxiv2021

Nonlinear transient amplification in recurrent neural networks with short-term plasticity

To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby symmetric excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. At the same time, other alternative mechanisms rely on asymmetric connectivity, in conflict with the Hebbian doctrine. Here we propose nonlinear transient amplification (NTA), a plausible circuit mechanism that reconciles symmetric connectivity with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

neuroscience↗