Biology subjects

Pang, R.

Publications and source records attributed to Pang, R..

8 recordsLinked to original sources

bioRxiv2025

Learning neural dynamics through instructive signals

Rapid learning is essential for flexible behavior, but its basis in the brain remains unknown. Here we introduce the PRISM plasticity rule, a unifying mechanistic model of three well-established, fast-acting synaptic plasticity rules--in hippocampus, cerebellum and mushroom body--which relies exclusively on pre-synaptic activity and an "instructive signal" from another brain area. Using a multi-region network model we show that guiding PRISM plasticity with instructive signals enables the network to quickly learn extremely flexible nonlinear dynamics underlying behaviorally relevant computations, as well as to emulate unknown external system dynamics from real-time error signals, which we demonstrate with comprehensive simulations supported by exact mathematical theory. Thus, PRISM plasticity guided by instructive signals is well-suited to rapidly learn general-purpose neural computations--in contrast to canonical Hebbian rules. Finally, we show how including this plasticity rule in artificial learning algorithms can solve long-range temporal credit assignment, a long-standing challenge in machine learning. HighlightsO_LIPRISM (PResynaptic and Instructive Signal-Mediated) plasticity--a unifying mechanistic model of three fast-acting plasticity rules in hippocampus, cerebellum, and mushroom body. C_LIO_LIMathematical theory inspired by Support Vector Machine exactly predicts the dynamics learned in a network model through PRISM plasticity. C_LIO_LIExamples of learning nonlinear dynamics in a single shot via sparse instructive signals or from real-time error signals. C_LIO_LIMachine-learning application of instructive signals and PRISM plasticity to solve the long-range temporal credit assignment problem. C_LI

neuroscience↗

bioRxiv2024

A non-Hebbian code for episodic memory

Hebbian plasticity has long dominated neurobiological models of memory formation. Yet plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. To address this, we present an episodic memory model governed by a simple non-Hebbian rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces when created via spiking and support diverse one-shot sequential and associative recall tasks, and policy learning. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning, and well-suited to encode episodes and policies as paths through a world model.

neuroscience↗

bioRxiv2024

Inferring neural dynamics of memory during naturalistic social communication

Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present Natural Continuation (NC)--a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.

neuroscience↗

bioRxiv2023

Neurexins control the strength and precise timing of glycinergic inhibition in the auditory brainstem

Neurexins play diverse functions as presynaptic organizers in various glutamatergic and GABAergic synapses. However, it remains unknown whether and how neurexins are involved in shaping functional properties of the glycinergic synapses, which mediate prominent inhibition in the brainstem and spinal cord. To address these issues, we examined the role of neurexins in a model glycinergic synapse between the principal neuron in the medial nucleus of the trapezoid body (MNTB) and the principal neuron in the lateral superior olive (LSO) in the auditory brainstem. Combining RNAscope with stereotactic injection of AAV-Cre in the MNTB of neurexin1/2/3 conditional triple knockout mice, we showed that MNTB neurons highly express all isoforms of neurexins although their expression levels vary remarkably. Selective ablation of all neurexins in MNTB neurons not only reduced the amplitude but also altered the kinetics of the glycinergic synaptic transmission at LSO neurons. The synaptic dysfunctions primarily resulted from an impaired Ca2+ sensitivity of release and a loosened coupling between voltage-gated Ca2+ channels and synaptic vesicles. Together, our current findings demonstrate that neurexins are essential in controlling the strength and temporal precision of the glycinergic synapse, which therefore corroborates the role of neurexins as key presynaptic organizers in all major types of fast chemical synapses.

neuroscience↗

bioRxiv2022

Precision motor timing via scalar input fluctuations

Complex motor skills like playing piano require precise timing over long periods, without errors accumulating between subprocesses like the left and right hand movements. While biological models can produce motor-like sequences, how the brain quenches timing errors is not well understood. Motivated by songbirds, where the left and right brain nuclei governing song sequences do not connect but may receive low-dimensional thalamic input, we present a model where timing errors in an autonomous sequence generator are continually corrected by one-dimensional input fluctuations. We show in a spiking neural network model how such input can rapidly correct temporal offsets in a propagating spike pulse, recapitulating the precise timing seen in songbird brains. In a reduced, more general model, we show that such timing correction emerges when the spatial profile of the input over the sequence sufficiently reflects its temporal fluctuations, yielding time-locking attractors that slow advanced sequences and hasten lagging ones, up to the input timescale. Unlike models without fluctuating input, our model predicts anti-correlated durations of adjacent segments of the output sequence, which we verify in recorded zebra finch songs. This work provides a bioplausible picture of how temporal precision could arise in extended motor sequences and generally how low-dimensional input could continuously coordinate time-varying output signals. SignificanceComplex motor skills like playing piano require precision timing over long periods, often among multiple components like left and right muscle groups. Although brain-like network models can produce motor-like outputs, timing regulation is not well understood. We introduce a model, inspired by songbird brains, where imprecise timing in a cortical-like system is corrected by a single thalamic input regulating the sequential propagation, or tempo, of cortical activity. This model illuminates a relation between the inputs spatial structure and temporal variation that lets lagging activity hasten and advanced activity slow, which makes a prediction about output timing that we verify in real birdsong. This work reveals a simple, neuroplausible mechanism that may play a role in precision cortical or motor timing.

neuroscience↗

bioRxiv2022

Path vectors: a neural code for sequential memory

While recalling lists of unrelated items is highly challenging we can recall much longer sequences structured as an episode or story. It is unknown why these patterns have such a striking influence on memory. We introduce a model where the experience of an episode or story is represented as a path through a pre-existing network of cognitive states. We demonstrate that by summing the neural representations of the visited states, this path can be transformed into a simple neural code: a path vector. We show how, by leveraging sparse connectivity and high dimensionality, path vectors provide robust codes for a large set of sequences and can be decoded mechanistically for memory retrieval. Fitting our model to data reveals how human free and serial recall may emerge from adapting coding mechanisms tuned for sequences aligned with existing network paths. We thus posit that sequences such as episodes or stories map more directly to existing cognitive network paths than arbitrary lists, with the latter eliciting paths that tend to interfere and impair recall. Our model suggests that mnemonic strategies like imposing narrative structure on a list act to improve recall by reducing such path interference. This work illuminates a simple bioplausible means for flexibly recruiting existing cognitive structures to encode new memories.

neuroscience↗

bioRxiv2020

Metabolites alleviate staphylococcal bloodstream infection in a NO-dependent manner via arginase inhibition

Staphylococcus aureus is a notorious bacterial pathogen that often causes soft tissue and bloodstream infections and invariably garners resistance mechanisms against new antibiotics. Host innate immunity modulated by metabolites has been proved as a powerful strategy against bacterial infections. However, few studies focus on the application of this strategy against S. aureus infection. Here, we identified four metabolite biomarkers, L-proline, L-isoleucine, L-leucine, and L-valine (PILV), by a metabolomics study. In animal models of S. aureus bloodstream infection, exogenous administration of each metabolite or PILV shows an anti-infective effect, while PILV treatment has higher protection than individual metabolite treatment. Each metabolite targets nitric oxide (NO) to kill S. aureus via arginase inhibition, and PILV exhibits additive inhibition of arginase activity that causes further killing. This suppression also contributes to the metabolite-mediated phagocytic killing of S. aureus in human blood. Our finding demonstrates the metabolite-mediated innate immunity as a therapeutic intervention for S. aureus infection.

microbiology↗

bioRxiv2018

Fast and Flexible Sequence Induction In Spiking Neural Networks Via Rapid Excitability Changes

Cognitive flexibility, the adaptation of mental processing to changes in task demands, is thought to depend on biological neural networks ability to rapidly modulate the dynamics governing how they process information. While extensive work has elucidated how network dynamics can be reshaped by slowly occurring structural changes, e.g. the gradual modification of recurrent synaptic patterns, much less is known about how dynamics might be reconfigured over faster timescales of seconds. One compelling example of rapid and selective modulation of network dynamics potentially involved in cognitive flexibility is observed in rodent hippocampus, where short bouts of exploratory behavior cause new activity sequences to preferentially \"replay\" during subsequent awake rest periods without continued sensory input. Fast mechanisms for selectively biasing sequential activity through networks, however, remain unknown. Using a spiking neural network model, we asked whether a simplified version of sequence replay could arise from three biophysically plausible components: recurrent, spatially organized connectivity; homogeneous, stochastic \"gating\" inputs; and rapid, activity-dependent scaling of gating input strengths, based on a phenomenon known as long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of flexible sequences reflecting recent behavior, despite unchanged recurrent weights. Specifically, activation-triggered LTP-IE \"tags\" neurons in the recurrent network by increasing their spiking probability when gating input is applied, and the sequential ordering of spikes is reconstructed by the existing recurrent connectivity. In a proof-of-concept demonstration, we also show how LTP-IE-based sequences can implement temporary stimulus-response mappings in a straightforward manner. These results elucidate a simple yet previously unexplored combination of biological mechanisms that converge in hippocampus and suffice for fast and flexible reconfiguration of sequential network dynamics, suggesting their potential role in cognitive flexibility over rapid timescales.

neuroscience↗