Biology subjects

Meng, J. H.

Publications and source records attributed to Meng, J. H..

9 recordsLinked to original sources

bioRxiv2025

Duet model of predictive coding unifies diverse neuroscienceexperimental protocols

The brain continuously generates predictions about the external world. When stimulus X is presented repeatedly, the brain predicts that the next one is also X. A deviant stimulus Y elicits a stronger sensory response than the baseline, reflecting the amplification of an unexpected stimulus. Here, we introduce the duet predictive coding model, a minimal and biologically plausible framework in which neurons encode both positive and negative prediction errors. This model reproduces neural responses observed in vision and audition across diverse predictive coding paradigms, particularly omission. Our proposed circuit mechanism predicts (1) neurons tuned to negative prediction errors in the oddball paradigm, supported by experimental evidence in mice; (2) the magnitude of unexpected responses quantitatively depends on the dissimilarity between standard and deviant stimuli and diminishes with increasing interstimulus interval. Our findings suggest that the brains deviance detection relies on dual-error computation, offering a unifying explanation across seemingly disparate experimental protocols.

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bioRxiv2025

Global error signal guides local optimization in mismatch calculation

1Corollary discharge denotes internal signals about the expected sensory consequences of ones own actions, leading to attenuation of sensory responses caused by self-produced stimulation. To investigate the underlying neural circuit mechanism, here we introduce a biologically plausible three-factor learning rule, where a global signal guides the updating of local inhibitory synapses to enable the computation of mismatch between a stimulus and its expectation or prediction. We show that our network model, endowed with positive and negative prediction error neurons, accounts for the salient physiological observations of motor-visual and motor-auditory mismatch responses in mice. Moreover, the model predicts that learning induces a bimodal distribution in activity correlation with stimulus and movement-induced prediction, supported by our analysis of neural data from a recent experiment. These results link global modulation to local learning for predictive error computation in the sensory areas, and shed insights into how disrupting inhibition impairs mismatch computation in specific ways.

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bioRxiv2025

Inhibitory and disinhibitory VIP IN-mediated circuits in neocortex

Cortical GABAergic interneurons (INs) expressing the neuropeptide vasoactive-intestinal peptide (VIP) predominantly function by inhibiting dendritic-targeting somatostatin (SST) expressing INs, thereby disinhibiting pyramidal cells (PCs) and facilitating cortical circuit plasticity. VIP INs are a molecularly heterogeneous group, but the physiological significance of this diversity is unclear at present. Here, we have characterized the functional diversity of VIP INs in the primary somatosensory cortex (vS1) using intersectional genetic approaches. We found that VIP INs are comprised of four primary populations that exhibit different laminar distributions, axonal and dendritic arbors, intrinsic electrophysiological properties, and efferent connectivity. Furthermore, we observe that these populations are differentially activated by long-range inputs, and display distinct responses to neuromodulation by endocannabinoids, acetylcholine and noradrenaline. Stimulation of VIP IN subpopulations in vivo results in differential effects on the cortical network, thus providing evidence for specialized modes of VIP IN-mediated regulation of PC activity during cortical information processing.

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bioRxiv2024

In Search of Transcriptomic Correlates of Neuronal Firing-Rate Adaptation across Subtypes, Regions and Species: A Patch-seq Analysis

Can the transcriptomic profile of a neuron predict its physiological properties? Using a Patch-seq dataset of the primary visual cortex, we addressed this question by focusing on spike rate adaptation (SRA), a well-known phenomenon that depends on small conductance calcium (Ca)-dependent potassium (SK) channels. We first show that in parvalbumin-expressing (PV) and somatostatin-expressing (SST) interneurons, expression levels of genes encoding the ion channels underlying action potential generation are correlated with the half-width (HW) of spikes. Surprisingly, the SK encoding genes are not correlated with the degree of SRA (dAdap). Instead, genes that encode proteins upstream from the SK current, as well as the low-voltage-activated Calcium channel, are correlated with dAdap. This general principle is supported by datasets from the mouse primary motor cortex and multiple macaque brain regions. Finally, we generate testable predictions by constructing a minimal model.

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bioRxiv2022

Id2 GABAergic interneurons comprise a neglected fourth major group of cortical inhibitory cells

Cortical GABAergic interneurons (INs) represent a diverse population of mainly locally projecting cells that provide specialized forms of inhibition to pyramidal neurons and other INs. Most recent work on INs has focused on subtypes distinguished by expression of Parvalbumin (PV), Somatostatin (SST), or Vasoactive Intestinal Peptide (VIP). However, a fourth group that includes neurogliaform cells (NGFCs) has remained enigmatic due to a lack of genetic tools. Here, we show that these INs can be accessed experimentally using intersectional genetics with the gene Id2. We find that outside of layer 1 (L1), the majority of Id2 INs are NGFCs that express high levels of neuropeptide Y (NPY) and exhibit a late-spiking firing pattern, with extensive local connectivity. While much sparser, non-NGFC Id2 INs had more variable properties, with most cells corresponding to a diverse group of INs that strongly expresses the neuropeptide CCK. In vivo, using silicon probe recordings, we observed several distinguishing aspects of NGFC activity, including a strong rebound in activity immediately following the cortical down state during NREM sleep. Our study provides insights into IN diversity and NGFC distribution and properties, and outlines an intersectional genetics approach for further study of this neglected group of INs.

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bioRxiv2022

Mechanisms of Dominant Electrophysiological Features of Four Subtypes of Layer 1 Interneurons

Neocortical Layer 1 (L1) consists of the distal dendrites of pyramidal cells and GABAergic interneurons (INs) and receives extensive long-range "top-down" projections, but L1 INs remain poorly understood. In this work, we systematically examined the distinct dominant electrophysiological features for four unique IN subtypes in L1 that were previously identified from mice of either gender: Canopy cells show an irregular firing pattern near rheobase; Neurogliaform cells (NGFCs) are late-spiking, and their firing rate accelerates during current injections; cells with strong expression of the 7 nicotinic receptor (7 cells), display onset (rebound) bursting; vasoactive intestinal peptide (VIP) expressing cells exhibit high input resistance, strong adaptation, and irregular firing. Computational modeling revealed that these diverse neurophysiological features could be explained by an extended exponential-integrate-and-fire neuron model with varying contributions of a slowly inactivating K+ channel (SIK), a T-type Ca2+ channel, and a spike-triggered Ca2+-dependent K+ channel. In particular, we show that irregular firing results from square-wave bursting through a fast-slow analysis. Furthermore, we demonstrate that irregular firing is frequently observed in VIP cells due to the interaction between strong adaptation and a SIK channel. At last, we reveal that the VIP and 7 cell models resonant with Alpha/Theta band input through a dynamic gain analysis. Significance StatementIn the neocortex, about 25% of neurons are interneurons. Interestingly, only somas of interneurons reside within Layer 1 (L1) of the neocortex, but not of excitatory pyramidal cells. L1 interneurons are diverse and believed to be important in the cortical-cortex interactions, especially top-down signaling in the cortical hierarchy. However, the electrophysiological features of L1 interneurons are poorly understood. Here, we systematically studied the electrophysiological features within each L1 interneuron subtype. Furthermore, we build computational models for each subtype and study the mechanisms behind these features. These electrophysiological features within each subtype should be incorporated to elucidate how different L1 interneuron subtypes contribute to communication between cortexes.

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bioRxiv2022

Structural spine plasticity: learning and forgetting of odor-specific subnetworks in the olfactory bulb

Learning to discriminate between different sensory stimuli is essential for survival. In rodents, the olfactory bulb, which contributes to odor discrimination via pattern separation, exhibits extensive structural synaptic plasticity involving the formation and removal of synaptic spines, even in adult animals. The network connectivity resulting from this plasticity is still poorly understood. To gain insight into this connectivity we present here a computational model for the structural plasticity of the reciprocal synapses between the dominant population of excitatory principal neurons and inhibitory interneurons. It incorporates the observed modulation of spine stability by odor exposure. The model captures the striking experimental observation that the exposure to odors does not always enhance their discriminability: while training with similar odors enhanced their discriminability, training with dissimilar odors actually reduced the discriminability of the training stimuli (Chu et al, 2016). Strikingly, this differential learning does not require the activity-dependence of the spine stability and occurs also in a model with purely random spine dynamics in which the spine density is changed homogeneously, e.g., due to a global signal. However, the experimentally observed odor-specific reduction in the response of principal cells as a result of extended odor exposure (Kato et al. 2012) and the concurrent disinhibition of a subset of principal cells arise only in the activity-dependent model. Moreover, this model predicts the experimentally testable recovery of odor response through weak but not through strong odor re-exposure and the forgetting of odors via exposure to interfering odors. Combined with the experimental observations, the computational model provides strong support for the prediction that odor exposure leads to the formation of odor-specific subnetworks in the olfactory bulb. Author SummaryA key feature of the brain is its ability to learn through the plasticity of its network. The olfactory bulb in the olfactory system is a remarkable brain area whose anatomical structure evolves substantially still in adult animals by establishing new synaptic connections and removing existing ones. We present a computational model for this process and employ it to interpret recent experimental results. By comparing the results of our model with those of a random control model we identify various experimental observations that lend strong support to the notion that the network of the olfactory bulb comprises learned, odor-specific subnetworks. Moreover, our model explains the recent observation that the learning of odors does not always improve their discriminability and provides testable predictions for the recovery of odor response after repeated odor exposure and for when the learning of new odors interferes with retaining the memory of familiar odors.

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bioRxiv2021

Interneuron activity-structural plasticity association is driven by context-dependent sensory experience

Neuronal dendritic spine dynamics provide a plasticity mechanism for altering brain circuit connectivity to integrate new information for learning and memory. Previous in vivo studies in the olfactory bulb (OB) showed that regional increases in activity caused localized spine stability, at a population level, yet how activity affects spine dynamics at an individual neuron level remains unknown. In this study, we tracked in vivo the correlation between an individual neurons activity and its dendritic spine dynamics of OB granule cell (GC) interneurons. Odor experience caused a consistent correlation between individual GC activity and spine stability. Dissecting the components of the OB circuit showed that increased principal cell (MC) activity was sufficient to drive this correlation, whereas cell-autonomously driven GC activity had no effect. A mathematical model was able to replicate the GC activity-spine stability correlation and showed MC output having improved odor discriminability while retaining odor memory. These results reveal that GC spine plasticity provides a sufficient network mechanism to decorrelate odors and maintain a memory trace.

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bioRxiv2020

Structural Spine Plasticity in Olfaction: Memory and Forgetting, Enhanced vs. Reduced Discriminability after Learning

How animals learn to discriminate between different sensory stimuli is an intriguing question. An important, common step towards discrimination is the enhancement of differences between the representations of relevant stimuli. This can be part of the learning process. In rodents, the olfac-tory bulb, which is known to contribute to this pattern separation, exhibits extensive structural synaptic plasticity even in adult animals: reciprocal connections between excitatory mitral cells and inhibitory granule cells are persistently formed and eliminated, correlated with mitral cell and granule cell activity. Here we present a Hebbian-type model for this plasticity. It captures the experimental observation that the same learning protocol that enhanced the discriminability of similar stimuli actually reduced that of dissimilar stimuli. The model predicts that the learned bulbar network structure is remembered across training with additional stimuli, unless the new stimuli interfere with the representations of previously learned ones.

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