Interplay between short- and long-term plasticity in cell-assembly formation

Biphasic Cholinergic Modulation of Reverberatory Activity in Neuronal Networks

Acetylcholine (ACh) is an important neuromodulator in various cognitive functions. However, it is unclear how ACh influences neural circuit dynamics by altering cellular properties. Here, we investigated how ACh influences reverberatory activity in cultured neuronal networks. We found that ACh suppressed the occurrence of evoked reverberation at low to moderate doses, but to a much lesser extent at high doses. Moreover, high doses of ACh caused a longer duration of evoked reverberation, and a higher occurrence of spontaneous activity. With whole-cell recording from single neurons, we found that ACh inhibited excitatory postsynaptic currents (EPSCs) while elevating neuronal firing in a dose-dependent manner. Furthermore, all ACh-induced cellular and network changes were blocked by muscarinic, but not nicotinic receptor antagonists. With computational modeling, we found that simulated changes in EPSCs and the excitability of single cells mimicking the effects of ACh indeed modulated the evoked network reverberation similar to experimental observations. Thus, ACh modulates network dynamics in a biphasic fashion, probably by inhibiting excitatory synaptic transmission and facilitating neuronal excitability through muscarinic signaling pathways.

Computational models of Idling brain activity for memory processing

Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous activity is a unique feature of the brain and is currently lacking in many artificially constructed intelligent machines. Spontaneous activity may represent the brain's idling states, which are internally driven by neuronal networks and possibly participate in offline processing during awake, sleep, and resting states. Evidence is accumulating that the brain's spontaneous activity is not mere noise but part of the mechanisms to process information about previous experiences. A bunch of literature has shown how previous sensory and behavioral experiences influence the subsequent patterns of brain activity with various methods in various animals. It seems, however, that the patterns of neural activity and their computational roles differ significantly from area to area and from function to function. In this article, I review the various forms of the brain's spontaneous activity, especially those observed during memory processing, and some attempts to model the generation mechanisms and computational roles of such activities.

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.

Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

Endogenous neurosteroids pregnanolone and pregnanolone sulfate potentiate presynaptic glutamate release through distinct mechanisms

BACKGROUND AND PURPOSE

Neurosteroids influence neuronal function and have multiple promising clinical applications. Direct modulation of postsynaptic neurotransmitter receptors by neurosteroids is well characterized, but presynaptic effects remain poorly understood. Here, we report presynaptic glutamate release potentiation by neurosteroids pregnanolone and pregnanolone sulfate and compare their mechanisms of action to phorbol 12,13-dibutyrate (PDBu), a mimic of the second messenger DAG.

EXPERIMENTAL APPROACH

We use whole-cell patch-clamp electrophysiology and pharmacology in rat hippocampal microisland cultures and total internal reflection fluorescence (TIRF) microscopy in HEK293 cells expressing GFP-tagged vesicle priming protein Munc13-1, to explore the mechanisms of neurosteroid presynaptic modulation.

KEY RESULTS

Pregnanolone sulfate and pregnanolone potentiate glutamate release downstream of presynaptic Ca2+ influx, resembling the action of a phorbol ester PDBu. PDBu partially occludes the effect of pregnanolone, but not of pregnanolone sulfate. Calphostin C, an inhibitor that disrupts DAG binding to its targets, reduces the effect PDBu and pregnanolone, but not of pregnanolone sulfate, suggesting that pregnanolone might interact with a well-known DAG/phorbol ester target Munc13-1. However, TIRF microscopy experiments found no evidence of pregnanolone-induced membrane translocation of GFP-tagged Munc13-1, suggesting that pregnanolone may regulate Munc13-1 indirectly or interact with other DAG targets.

CONCLUSION AND IMPLICATIONS

We describe a novel presynaptic effect of neurosteroids pregnanolone and pregnanolone sulfate to potentiate glutamate release downstream of presynaptic Ca2+ influx. The mechanism of action of pregnanolone, but not of pregnanolone sulfate, partly overlaps with that of PDBu. Presynaptic effects of neurosteroids may contribute to their therapeutic potential in the treatment of disorders of the glutamate system.

From Topological Analyses to Functional Modeling: The Case of Hippocampus

Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.

Disruption of Cholinergic Circuits as an Area for Targeted Drug Treatment of Alzheimer's Disease: In Vivo Assessment of Short-Term Plasticity in Rat Brain

The search for new therapeutics for the treatment of Alzheimer’s disease (AD) is still in progress. Aberrant pathways of synaptic transmission in basal forebrain cholinergic neural circuits are thought to be associated with the progression of AD. However, the effect of amyloid-beta (Aβ) on short-term plasticity (STP) of cholinergic circuits in the nucleus basalis magnocellularis (NBM) is largely unknown. STP assessment in rat brain cholinergic circuitry may indicate a new target for AD cholinergic therapeutics. Thus, we aimed to study in vivo electrophysiological patterns of synaptic activity in NBM-hippocampus and NBM-basolateral amygdala circuits associated with AD-like neurodegeneration. The extracellular single-unit recordings of responses from the hippocampal and basolateral amygdala neurons to high-frequency stimulation (HFS) of the NBM were performed after intracerebroventricular injection of Aβ 25–35. We found that after Aβ 25–35 exposure the number of hippocampal neurons exhibiting inhibitory responses to HFS of NBM is decreased. The reverse tendency was seen in the basolateral amygdala inhibitory neural populations, whereas the number of amygdala neurons with excitatory responses decreased. The low intensity of inhibitory and excitatory responses during HFS and post-stimulus period is probably due to the anomalous basal synaptic transmission and excitability of hippocampal and amygdala neurons. These functional changes were accompanied by structural alteration of hippocampal, amygdala, and NBM neurons. We have thus demonstrated that Aβ 25–35 induces STP disruption in NBM-hippocampus and NBM-basolateral amygdala circuits as manifested by unbalanced excitatory/inhibitory responses and their frequency. The results of this study may contribute to a better understanding of synaptic integrity. We believe that advancing our understanding of in vivo mechanisms of synaptic plasticity disruption in specific neural circuits could lead to effective drug searches for AD treatment.

Emulating synaptic response in n- and p-channel MoS2 transistors by utilizing charge trapping dynamics

Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS₂) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ₁ = 27.4 ms, τ₂ = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing.

Autonomous emergence of connectivity assemblies via spike triplet interactions

Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP), which depends on the interactions of three precisely-timed spikes, and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We derive synaptic changes arising from correlations up to third-order and describe them as the sum of structural motifs, which determine how any spike in the network influences a given synaptic connection through possible connectivity paths. This motif expansion framework reveals novel structural motifs under the triplet STDP rule, which support the formation of bidirectional connections and ultimately the spontaneous emergence of global network structure in the form of self-connected groups of neurons, or assemblies. We propose that under triplet STDP assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule common in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of the triplet STDP rule or the synaptic transmission function differentially promote structural motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.

Emergent non-random connectivity structures in different brain regions are tightly related to specific patterns of neural activity and support diverse brain functions. For instance, self-connected groups of neurons, known as assemblies, have been proposed to represent functional units in brain circuits and can emerge even without patterned external instruction. Here we investigate the emergence of non-random connectivity in recurrent networks using a particular plasticity rule, triplet STDP, which relies on the interaction of spike triplets and can capture higher-order statistical dependencies in neural activity. We derive the evolution of the synaptic strengths in the network and explore the conditions for the self-organization of connectivity into assemblies. We demonstrate key differences of the triplet STDP rule compared to the classical pair-based rule in terms of how assemblies are formed, including the realistic asymmetric shape and influence of novel connectivity motifs on network plasticity driven by higher-order correlations. Assembly formation depends on the specific shape of the STDP window and synaptic transmission function, pointing towards an important role of neuromodulatory signals on formation of intrinsically generated assemblies.

Replays of spatial memories suppress topological fluctuations in cognitive map

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space-a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of "memory replays" that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space.

Reduced presynaptic vesicle stores mediate cellular and network plasticity defects in an early-stage mouse model of Alzheimer's disease

Background

Identifying effective strategies to prevent memory loss in AD has eluded researchers to date, and likely reflects insufficient understanding of early pathogenic mechanisms directly affecting memory encoding. As synaptic loss best correlates with memory loss in AD, refocusing efforts to identify factors driving synaptic impairments may provide the critical insight needed to advance the field. In this study, we reveal a previously undescribed cascade of events underlying pre and postsynaptic hippocampal signaling deficits linked to cognitive decline in AD. These profound alterations in synaptic plasticity, intracellular Ca²⁺ signaling, and network propagation are observed in 3–4 month old 3xTg-AD mice, an age which does not yet show overt histopathology or major behavioral deficits.

Methods

In this study, we examined hippocampal synaptic structure and function from the ultrastructural level to the network level using a range of techniques including electron microscopy (EM), patch clamp and field potential electrophysiology, synaptic immunolabeling, spine morphology analyses, 2-photon Ca²⁺ imaging, and voltage-sensitive dye-based imaging of hippocampal network function in 3–4 month old 3xTg-AD and age/background strain control mice.

Results

In 3xTg-AD mice, short-term plasticity at the CA1-CA3 Schaffer collateral synapse is profoundly impaired; this has broader implications for setting long-term plasticity thresholds. Alterations in spontaneous vesicle release and paired-pulse facilitation implicated presynaptic signaling abnormalities, and EM analysis revealed a reduction in the ready-releasable and reserve pools of presynaptic vesicles in CA3 terminals; this is an entirely new finding in the field. Concurrently, increased synaptically-evoked Ca²⁺ in CA1 spines triggered by LTP-inducing tetani is further enhanced during PTP and E-LTP epochs, and is accompanied by impaired synaptic structure and spine morphology. Notably, vesicle stores, synaptic structure and short-term plasticity are restored by normalizing intracellular Ca²⁺ signaling in the AD mice.

Conclusions

These findings suggest the Ca²⁺ dyshomeostasis within synaptic compartments has an early and fundamental role in driving synaptic pathophysiology in early stages of AD, and may thus reflect a foundational disease feature driving later cognitive impairment. The overall significance is the identification of previously unidentified defects in pre and postsynaptic compartments affecting synaptic vesicle stores, synaptic plasticity, and network propagation, which directly impact memory encoding.

Electronic supplementary material

The online version of this article (10.1186/s13024-019-0307-7) contains supplementary material, which is available to authorized users.

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful tools for characterizing short-term plasticity from multi-electrode spike recordings in vivo.

A Lognormal Recurrent Network Model for Burst Generation during Hippocampal Sharp Waves

The strength of cortical synapses distributes lognormally, with a long tail of strong synapses. Various properties of neuronal activity, such as the average firing rates of neurons, the rate and magnitude of spike bursts, the magnitude of population synchrony, and the correlations between presynaptic and postsynaptic spikes, also obey lognormal-like distributions reported in the rodent hippocampal CA1 and CA3 areas. Theoretical models have demonstrated how such a firing rate distribution emerges from neural network dynamics. However, how the other properties also display lognormal patterns remain unknown. Because these features are likely to originate from neural dynamics in CA3, we model a recurrent neural network with the weights of recurrent excitatory connections distributed lognormally to explore the underlying mechanisms and their functional implications. Using multi-timescale adaptive threshold neurons, we construct a low-frequency spontaneous firing state of bursty neurons. This state well replicates the observed statistical properties of population synchrony in hippocampal pyramidal cells. Our results show that the lognormal distribution of synaptic weights consistently accounts for the observed long-tailed features of hippocampal activity. Furthermore, our model demonstrates that bursts spread over the lognormal network much more effectively than single spikes, implying an advantage of spike bursts in information transfer. This efficiency in burst propagation is not found in neural network models with Gaussian-weighted recurrent excitatory synapses. Our model proposes a potential network mechanism to generate sharp waves in CA3 and associated ripples in CA1 because bursts occur in CA3 pyramidal neurons most frequently during sharp waves.

Mixed signal learning by spike correlation propagation in feedback inhibitory circuits

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.