From synapse to network: models of information storage and retrieval in neural circuits

Gastrodin prevents stress-induced synaptic plasticity impairment and behavioral dysfunction via cAMP/PKA/CREB signaling pathway

BACKGROUND

Chronic stress is widely recognized as a critical factor that impairs synaptic plasticity dependent brain function and behavior, contributing to the onset of depression and anxiety disorders, which subsequently undermine learning and memory processes. Gastrodin (GAS), a prominent bioactive constituent of Gastrodiae Rhizoma exhibiting notable neuroprotective properties, holds significant potential for the prevention and treatment of stress-induced neurological dysfunction. However, the protective effects of GAS on stress-induced synaptic plasticity impairment and the underlying mechanisms have yet to be fully elucidated.

OBJECTIVES

To investigate the potential of GAS in protecting synaptic plasticity from chronic stress and its underlying cellular and molecular mechanisms.

METHOD

A chronic stress model was constructed in C57BL/6J mice, and the effects of GAS on synaptic plasticity were examined using Golgi staining and immunohistochemistry. Systematic behavioral analysis was employed to assess the impact of GAS on depressive- and anxiety-like behaviors and cognitive function of mice. Metabolomics, transcriptomics, Western blotting, immunolocalization, enzyme-linked immunosorbent assay, and the administration of signal blockers were utilized to investigate the cellular and molecular pathways via which GAS safeguards synaptic plasticity.

RESULTS

The results showed that chronic stress exposure reduces the dendritic arbor complexity, density of dendritic spines, proportion of mushroom spines of hippocampal neurons, as well as disrupts synaptic function, impairs cognitive function and induces depressive-like behaviors. Importantly, impairment of hippocampal synaptic plasticity, anxiety- and depressive-like behaviors, and cognitive decline induced by chronic stress were significantly ameliorated following GAS treatment. Moreover, we identified the cAMP/PKA/CREB signaling in hippocampal neurons as a potential mechanism through which GAS prevents synaptic plasticity impairment from chronic stress exposure. Blockade of cAMP/PKA/CREB signaling abolished the protective effects of GAS on synaptic plasticity of hippocampal neurons and behaviors in stress-exposed mice.

CONCLUSION

This study is the first to identify GAS as a potential natural compound for alleviating stress-induced synaptic plasticity impairment and behavioral dysfunction by activating the cAMP/PKA/CREB signaling pathway in hippocampal neurons, offering a promising strategy for stress-induced neurological disorders.

Synaptic plasticity rules driving representational shifting in the hippocampus

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PF) as an indicator of ongoing plasticity during memory formation and familiarization. By implementing different plasticity rules in computational models of spiking place cells and comparing them to experimentally measured PFs from mice navigating familiar and new environments, we found that behavioral timescale synaptic plasticity (BTSP), rather than Hebbian spike-timing-dependent plasticity (STDP), best explains PF shifting dynamics. BTSP-triggering events are rare, but more frequent during new experiences. During exploration, their probability is dynamic-it decays after PF onset, but continually drives a population-level representational drift. Additionally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity continuously shapes neuronal representations during learning.

Representational learning by optimization of neural manifolds in an olfactory memory network

Cognitive brain functions rely on experience-dependent internal representations of relevant information. Such representations are organized by attractor dynamics or other mechanisms that constrain population activity onto "neural manifolds". Quantitative analyses of representational manifolds are complicated by their potentially complex geometry, particularly in the absence of attractor states. Here we trained juvenile and adult zebrafish in an odor discrimination task and measured neuronal population activity to analyze representations of behaviorally relevant odors in telencephalic area pDp, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. However, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals better than other descriptors of population activity, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.

Subchronic cyanuric acid treatment impairs spatial flexible behavior in female adolescent rats through depressing GluN2B-dependent neuronal and synaptic function

Subchronic exposure to cyanuric acid (CA) and its structural analogue melamine induces long-term effects on brain and behavior in male rodents. To examine if this exposure induced negative effects on cognitive function in females, we examined the behavioral performance and further attempted to investigate synaptic and neuronal function. CA was intraperitoneal treated with 20 or 40 mg/kg/day to adolescent female rats for 4 consecutive weeks. Multiple behavioral tests were employed to assess spatial cognition, learning strategy, locomotion and motivation. Hippocampal synaptic function at Schaffer collaterals-CA1 synapses and excitatory postsynaptic currents (EPSCs) in CA1 pyramidal neurons was evaluated. Meanwhile, the glutamate transport inhibitor DL-threo-β-benzyloxyaspartate (DL-TBOA) was infused into hippocampal CA1 region to certify the underlying mechanism. We found that subchronic CA exposure impairs reversal learning ability with dose-dependent effects but did not affect spatial learning and memory, or learning strategy. The expression and phosphorylation of N-methyl-D-aspartate receptor (NMDAR) GluN2B subunits were simultaneously reduced in the hippocampus and the GluN2B-mediated synaptic function, including long-term depression (LTD) and paired-pulse facilitation (PPF), was suppressed. CA could also diminish postsynaptic density protein-95 (PSD-95) expression but did change the levels of α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionate receptor (AMPAR) GluA1 or NMDAR GluN2A subunit, or hippocampal spine density. Meanwhile, CA depressed frequency and amplitude of GluN2B-mediated EPSCs, indicating the presynaptic and postsynaptic actions of CA on neuronal activity. Furthermore, the DL-TBOA infusions could effectively mitigate the diminished GluN2B-LTD and GluN2B-EPSCs and the impairments in behavioral flexibility. Our findings provide the first evidence that CA can exert neurotoxic effects on females and certify that one of the potential mechanisms for neuronal and synaptic dysfunction is the GluN2B-mediated signaling pathway.

Role of Neural Circuits in Cognitive Impairment

Cognitive impairment refers to abnormalities in learning, memory and cognitive judgment, mainly manifested as symptoms such as decreased memory, impaired orientation and reduced computational ability. As the fundamental unit of information processing in the brain, neural circuits have recently attracted great attention due to their functions in regulating pain, emotion and behavior. Furthermore, a growing number of studies have suggested that neural circuits play an important role in cognitive impairment. Neural circuits can affect perception, attention and decision-making, they can also regulate language skill, thinking and memory. Pathological conditions crucially affecting the integrity and preservation of neural circuits and their connectivity will heavily impact cognitive abilities. Nowadays, technological developments have led to many novel methods for studying neural circuits, such as brain imaging, optogenetic techniques, and chemical genetics approaches. Therefore, neural circuits show great promise as a potential target in mitigating cognitive impairment. In this review we discuss the pathogenesis of cognitive impairment and the regulation and detection of neural circuits, thus highlighting the role of neural circuits in cognitive impairment. Hence, therapeutic agents against cognitive impairment may be developed that target neural circuits important in cognition.

Representational learning by optimization of neural manifolds in an olfactory memory network

Higher brain functions depend on experience-dependent representations of relevant information that may be organized by attractor dynamics or by geometrical modifications of continuous "neural manifolds". To explore these scenarios we analyzed odor-evoked activity in telencephalic area pDp of juvenile and adult zebrafish, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. Rather, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.

BTSP, not STDP, Drives Shifts in Hippocampal Representations During Familiarization

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in-vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PFs) as an indicator of ongoing plasticity during memory formation. By implementing different plasticity rules in computational models of spiking place cells and comparing to experimentally measured PFs from mice navigating familiar and novel environments, we found that Behavioral-Timescale-Synaptic-Plasticity (BTSP), rather than Hebbian Spike-Timing-Dependent-Plasticity, is the principal mechanism governing PF shifting dynamics. BTSP-triggering events are rare, but more frequent during novel experiences. During exploration, their probability is dynamic: it decays after PF onset, but continually drives a population-level representational drift. Finally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity shapes neuronal representations during learning.

Memory: Synaptic or Cellular, That Is the Question

According to the commonly accepted opinion, memory engrams are formed and stored at the level of neural networks due to a change in the strength of synaptic connections between neurons. This hypothesis of synaptic plasticity (HSP), formulated by Donald Hebb in the 1940s, continues to dominate the directions of experimental studies and the interpretations of experimental results in the field. The universal acceptance of the HSP has transformed it from a hypothesis into an incontrovertible theory. In this article, I show that the entire body of experimental and clinical data obtained in studies of long-term memory in mammals and humans is inconsistent with the HSP. Instead, these data suggest that long-term memory is formed and stored at the intracellular level where it is reliably protected from ongoing synaptic activity, including pathological epileptic activity. It seems that the generally accepted HSP became a serious obstacle to understanding the mechanisms of memory and that progress in this field requires rethinking this doctrine and shifting experimental efforts toward exploring the intracellular mechanisms.

Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective

The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Formation and computational implications of assemblies in neural circuits

In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.

Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two "parent" processes. We propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not "run away." Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

Dynamical systems on large networks with predator-prey interactions are stable and exhibit oscillations

We analyze the stability of linear dynamical systems defined on sparse, random graphs with predator-prey, competitive, and mutualistic interactions. These systems are aimed at modeling the stability of fixed points in large systems defined on complex networks, such as ecosystems consisting of a large number of species that interact through a food web. We develop an exact theory for the spectral distribution and the leading eigenvalue of the corresponding sparse Jacobian matrices. This theory reveals that the nature of local interactions has a strong influence on a system's stability. We show that, in general, linear dynamical systems defined on random graphs with a prescribed degree distribution of unbounded support are unstable if they are large enough, implying a tradeoff between stability and diversity. Remarkably, in contrast to the generic case, antagonistic systems that contain only interactions of the predator-prey type can be stable in the infinite size limit. This feature for antagonistic systems is accompanied by a peculiar oscillatory behavior of the dynamical response of the system after a perturbation, when the mean degree of the graph is small enough. Moreover, for antagonistic systems we also find that there exist a dynamical phase transition and critical mean degree above which the response becomes nonoscillatory.