Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses

Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter

Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.

Mystery of the memory engram: History, current knowledge, and unanswered questions

The quest to understand the memory engram has intrigued humans for centuries. Recent technological advances, including genetic labelling, imaging, optogenetic and chemogenetic techniques, have propelled the field of memory research forward. These tools have enabled researchers to create and erase memory components. While these innovative techniques have yielded invaluable insights, they often focus on specific elements of the memory trace. Genetic labelling may rely on a particular immediate early gene as a marker of activity, optogenetics may activate or inhibit one specific type of neuron, and imaging may capture activity snapshots in a given brain region at specific times. Yet, memories are multifaceted, involving diverse arrays of neuronal subpopulations, circuits, and regions that work in concert to create, store, and retrieve information. Consideration of contributions of both excitatory and inhibitory neurons, micro and macro circuits across brain regions, the dynamic nature of active ensembles, and representational drift is crucial for a comprehensive understanding of the complex nature of memory.

Action of GABAB receptor on local network oscillation in somatosensory cortex of oral part: focusing on NMDA receptor

The balance of activity between glutamatergic and GABAergic networks is particularly important for oscillatory neural activities in the brain. Here, we investigated the roles of GABAB receptors in network oscillation in the oral somatosensory cortex (OSC), focusing on NMDA receptors. Neural oscillation at the frequency of 8-10 Hz was elicited in rat brain slices after caffeine application. Oscillations comprised a non-NMDA receptor-dependent initial phase and a later NMDA receptor-dependent oscillatory phase, with the oscillator located in the upper layer of the OSC. Baclofen was applied to investigate the actions of GABAB receptors. The later NMDA receptor-dependent oscillatory phase completely disappeared, but the initial phase did not. These results suggest that GABAB receptors mainly act on NMDA receptor, in which metabotropic actions of GABAB receptors may contribute to the attenuation of NMDA receptor activities. A regulatory system for network oscillation involving GABAB receptors may be present in the OSC.

Velocity of conduction between columns and layers in barrel cortex reported by parvalbumin interneurons

Inhibitory interneurons expressing parvalbumin (PV) play critical roles throughout the brain. Their rapid spiking enables them to control circuit dynamics on a millisecond time scale, and the timing of their activation by different excitatory pathways is critical to these functions. We used a genetically encoded hybrid voltage sensor to image PV interneuron voltage changes with sub-millisecond precision in primary somatosensory barrel cortex (BC) of adult mice. Electrical stimulation evoked depolarizations with a latency that increased with distance from the stimulating electrode, allowing us to determine conduction velocity. Spread of responses between cortical layers yielded an interlaminar conduction velocity and spread within layers yielded intralaminar conduction velocities in different layers. Velocities ranged from 74 to 473 μm/ms depending on trajectory; interlaminar conduction was 71% faster than intralaminar conduction. Thus, computations within columns are more rapid than between columns. The BC integrates thalamic and intracortical input for functions such as texture discrimination and sensory tuning. Timing differences between intra- and interlaminar PV interneuron activation could impact these functions. Imaging of voltage in PV interneurons reveals differences in signaling dynamics within cortical circuitry. This approach offers a unique opportunity to investigate conduction in populations of axons based on their targeting specificity.

Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making

Experience and training are known to boost our skills and mold the brain's organization and function. Yet, structural plasticity and functional neurotransmission are typically studied at different scales (large-scale networks, local circuits), limiting our understanding of the adaptive interactions that support learning of complex cognitive skills in the adult brain. Here, we employ multimodal brain imaging to investigate the link between microstructural (myelination) and neurochemical (GABAergic) plasticity for decision-making. We test (in males, due to potential confounding menstrual cycle effects on GABA measurements in females) for changes in MRI-measured myelin, GABA, and functional connectivity before versus after training on a perceptual decision task that involves identifying targets in clutter. We demonstrate that training alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between MRI measures of myelin, GABA, and functional connectivity indicates that pulvinar myelin plasticity interacts-through thalamocortical connectivity-with GABAergic inhibition in visual cortex to support learning. Our findings propose a dynamic interplay of adaptive microstructural and neurochemical plasticity in subcortico-cortical circuits that supports learning for optimized decision-making in the adult human brain.

The plasticitome of cortical interneurons

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.

Alternative strategy for driving voltage-oscillator in neocortex of rats

Information integration in the brain requires functional connectivity between local neural networks. Here, we investigated the interregional coupling mechanism from the viewpoint of oscillations using optical recording methods. Low-frequency electrical stimulation of rat neocortical slices in a caffeine-containing medium induced oscillatory activity between the primary visual cortex (Oc1) and medial secondary visual cortex (Oc2M), in which the oscillation generator was located in the Oc2M and was triggered by a feedforward signal. During to-and-fro oscillatory activity, neural excitation was marked in layer II/III. When the upper layer was disrupted between Oc1 and Oc2M, feedforward signals could propagate through the deep layer and switch on the oscillator in the Oc2M. When the lower layer was disrupted between Oc1 and Oc2M, feedforward signals could propagate through the upper layer and switch on the oscillator in the Oc2M. In the backward direction, neither the upper layer cut nor the lower layer cut disrupted the propagation of the oscillations. In all cases, the horizontal and vertical pathways were used as needed. Fluctuations in the oscillatory waveforms of the local field potential at the upper and lower layers in the Oc2M were reversed, suggesting that the oscillation originated between the two layers. Thus, the neocortex may work as a safety device for interregional communications in an alternative way to drive voltage oscillators in the neocortex.

Visual-area-specific tonic modulation of GABA release by endocannabinoids sets the activity and coordination of neocortical principal neurons

Perisomatic inhibition of pyramidal neurons (PNs) coordinates cortical network activity during sensory processing, and this role is mainly attributed to parvalbumin-expressing basket cells (BCs). However, cannabinoid receptor type 1 (CB1)-expressing interneurons are also BCs, but the connectivity and function of these elusive but prominent neocortical inhibitory neurons are unclear. We find that their connectivity pattern is visual area specific. Persistently active CB1 signaling suppresses GABA release from CB1 BCs in the medial secondary visual cortex (V2M), but not in the primary visual cortex (V1). Accordingly, in vivo, tonic CB1 signaling is responsible for higher but less coordinated PN activity in the V2M than in the V1. These differential firing dynamics in the V1 and V2M can be captured by a computational network model that incorporates visual-area-specific properties. Our results indicate a differential CB1-mediated mechanism controlling PN activity, suggesting an alternative connectivity scheme of a specific GABAergic circuit in different cortical areas.

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CB1⁺ basket cells exhibit visual-area-specific morphology and connectivity patterns

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Tonic CB1 signaling underlies high pyramidal neurons (PN) activity in V2M but not V1

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Tonic CB1 signaling differentially modulates PN-correlated activity in V1 and V2M

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Numerical simulations capture specific CB1-dependent firing dynamics of V1 and V2M

In Utero Electroporation for Manipulation of Specific Neuronal Populations

The complexity of brain functions is supported by the heterogeneity of brain tissue and millisecond-scale information processing. Understanding how complex neural circuits control animal behavior requires the precise manipulation of specific neuronal subtypes at high spatiotemporal resolution. In utero electroporation, when combined with optogenetics, is a powerful method for precisely controlling the activity of specific neurons. Optogenetics allows for the control of cellular membrane potentials through light-sensitive ion channels artificially expressed in the plasma membrane of neurons. Here, we first review the basic mechanisms and characteristics of in utero electroporation. Then, we discuss recent applications of in utero electroporation combined with optogenetics to investigate the functions and characteristics of specific regions, layers, and cell types. These techniques will pave the way for further advances in understanding the complex neuronal and circuit mechanisms that underlie behavioral outputs.

Synaptic Plasticity and Oscillations in Alzheimer's Disease: A Complex Picture of a Multifaceted Disease

Brain plasticity is widely accepted as the core neurophysiological basis of memory and is generally defined by activity-dependent changes in synaptic efficacy, such as long-term potentiation (LTP) and long-term depression (LTD). By using diverse induction protocols like high-frequency stimulation (HFS) or spike-timing dependent plasticity (STDP), such crucial cognition-relevant plastic processes are shown to be impaired in Alzheimer’s disease (AD). In AD, the severity of the cognitive impairment also correlates with the level of disruption of neuronal network dynamics. Currently under debate, the named amyloid hypothesis points to amyloid-beta peptide 1–42 (Aβ42) as the trigger of the functional deviations underlying cognitive impairment in AD. However, there are missing functional mechanistic data that comprehensively dissect the early subtle changes that lead to synaptic dysfunction and subsequent neuronal network collapse in AD. The convergence of the study of both, mechanisms underlying brain plasticity, and neuronal network dynamics, may represent the most efficient approach to address the early triggering and aberrant mechanisms underlying the progressive clinical cognitive impairment in AD. Here we comment on the emerging integrative roles of brain plasticity and network oscillations in AD research and on the future perspectives of research in this field.

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields

Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Synapses from each inhibitory population change according to distinct plasticity rules. We tested different combinations of three rules: Hebbian, anti-Hebbian, and homeostatic scaling. Depending on the inhibitory plasticity rule, synapses become unspecific (flat), anticorrelated to, or correlated with excitatory synapses. Crucially, the neuron's receptive field (i.e., its response to presynaptic stimuli) depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or nonpreferred inputs, in line with recent experimental findings that show dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field does not follow directly from the weight profiles of its presynaptic afferents. Our results show how plasticity rules in various cell types can interact to shape cortical circuit motifs and their dynamics.SIGNIFICANCE STATEMENT Neurons in sensory areas of the cortex are known to respond to specific features of a given input (e.g., specific sound frequencies), but recent experimental studies show that such responses (i.e., their receptive fields) depend on context. Inspired by the cortical connectivity, we built models of excitatory and inhibitory inputs onto a single neuron, to study how receptive fields may change on short and long time scales. We show how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and, moreover, how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change. Our work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields.

Synaptic inhibition in the neocortex: Orchestration and computation through canonical circuits and variations on the theme

The neocortex plays a crucial role in all basic and abstract cognitive functions. Conscious mental processes are achieved through a correct flow of information within and across neocortical networks, whose particular activity state results from a tight balance between excitation and inhibition. The proper equilibrium between these indissoluble forces is operated with multiscale organization: along the dendro-somatic axis of single neurons and at the network level. Fast synaptic inhibition is assured by a multitude of inhibitory interneurons. During cortical activities, these cells operate a finely tuned division of labor that is epitomized by their detailed connectivity scheme. Recent results combining the use of mouse genetics, cutting-edge optical and neurophysiological approaches have highlighted the role of fast synaptic inhibition in driving cognition-related activity through a canonical cortical circuit, involving several major interneuron subtypes and principal neurons. Here we detail the organization of this cortical blueprint and we highlight the crucial role played by different neuron types in fundamental cortical computations. In addition, we argue that this canonical circuit is prone to many variations on the theme, depending on the resolution of the classification of neuronal types, and the cortical area investigated. Finally, we discuss how specific alterations of distinct inhibitory circuits can underlie several devastating brain diseases.

Network control through coordinated inhibition

Coordinated excitatory and inhibitory activity is required for proper brain functioning. Recent computational and experimental studies have demonstrated that activity patterns in recurrent cortical networks are dominated by inhibition. Whereas previous studies have suggested that inhibitory plasticity is important for homeostatic control, this new framework puts inhibition in the driver's seat. Complex neuronal networks in the brain comprise many configurations in parallel, controlled by external and internal 'switches'. Context-dependent modulation and plasticity of inhibitory connections play a key role in memory and learning. It is therefore important to realize that synaptic plasticity is often multisynaptic and that a proper balance between excitation and inhibition is not fixed, but depends on context and activity level.

The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles

GABAergic interneurons are highly diverse, and their synaptic outputs express various forms of plasticity. Compelling evidence indicates that activity-dependent changes of inhibitory synaptic transmission play a significant role in regulating neural circuits critically involved in learning and memory and circuit refinement. Here, we provide an updated overview of inhibitory synaptic plasticity with a focus on the hippocampus and neocortex. To illustrate the diversity of inhibitory interneurons, we discuss the case of two highly divergent interneuron types, parvalbumin-expressing basket cells and neurogliaform cells, which support unique roles on circuit dynamics. We also present recent progress on the molecular mechanisms underlying long-term, activity-dependent plasticity of fast inhibitory transmission. Lastly, we discuss the role of inhibitory synaptic plasticity in neuronal circuits' function. The emerging picture is that inhibitory synaptic transmission in the CNS is extremely diverse, undergoes various mechanistically distinct forms of plasticity and contributes to a much more refined computational role than initially thought. Both the remarkable diversity of inhibitory interneurons and the various forms of plasticity expressed by GABAergic synapses provide an amazingly rich inhibitory repertoire that is central to a variety of complex neural circuit functions, including memory.

Computational Modeling of Inhibitory Transsynaptic Signaling in Hippocampal and Cortical Neurons Expressing Intrabodies Against Gephyrin

GABAergic transmission regulates neuronal excitability, dendritic integration of synaptic signals and oscillatory activity, thought to be involved in high cognitive functions. By anchoring synaptic receptors just opposite to release sites, the scaffold protein gephyrin plays a key role in these tasks. In addition, by regulating GABA_A receptor trafficking, gephyrin contributes to maintain, at the network level, an appropriate balance between Excitation (E) and Inhibition (I), crucial for information processing. An E/I imbalance leads to neuropsychiatric disorders such as epilepsy, schizophrenia and autism. In this article, we exploit a previously published computational method to fit spontaneous synaptic events, using a simplified model of the subcellular pathways involving gephyrin at inhibitory synapses. The model was used to analyze experimental data recorded under different conditions, with the main goal to gain insights on the possible consequences of gephyrin block on IPSCs. The same approach can be useful, in general, to analyze experiments designed to block a single protein. The results suggested possible ways to correlate the changes observed in the amplitude and time course of individual events recorded after different experimental protocols with the changes that may occur in the main subcellular pathways involved in gephyrin-dependent transsynaptic signaling.

Neuroregeneration and plasticity: a review of the physiological mechanisms for achieving functional recovery postinjury

Neuronal networks, especially those in the central nervous system (CNS), evolved to support extensive functional capabilities while ensuring stability. Several physiological "brakes" that maintain the stability of the neuronal networks in a healthy state quickly become a hinderance postinjury. These "brakes" include inhibition from the extracellular environment, intrinsic factors of neurons and the control of neuronal plasticity. There are distinct differences between the neuronal networks in the peripheral nervous system (PNS) and the CNS. Underpinning these differences is the trade-off between reduced functional capabilities with increased adaptability through the formation of new connections and new neurons. The PNS has "facilitators" that stimulate neuroregeneration and plasticity, while the CNS has "brakes" that limit them. By studying how these "facilitators" and "brakes" work and identifying the key processes and molecules involved, we can attempt to apply these theories to the neuronal networks of the CNS to increase its adaptability. The difference in adaptability between the CNS and PNS leads to a difference in neuroregenerative properties and plasticity. Plasticity ensures quick functional recovery of abilities in the short and medium term. Neuroregeneration involves synthesizing new neurons and connections, providing extra resources in the long term to replace those damaged by the injury, and achieving a lasting functional recovery. Therefore, by understanding the factors that affect neuroregeneration and plasticity, we can combine their advantages and develop rehabilitation techniques. Rehabilitation training methods, coordinated with pharmacological interventions and/or electrical stimulation, contributes to a precise, holistic treatment plan that achieves functional recovery from nervous system injuries. Furthermore, these techniques are not limited to limb movement, as other functions lost as a result of brain injury, such as speech, can also be recovered with an appropriate training program.

Robust perisomatic GABAergic self-innervation inhibits basket cells in the human and mouse supragranular neocortex

Inhibitory autapses are self-innervating synaptic connections in GABAergic interneurons in the brain. Autapses in neocortical layers have not been systematically investigated, and their function in different mammalian species and specific interneuron types is poorly known. We investigated GABAergic parvalbumin-expressing basket cells (pvBCs) in layer 2/3 (L2/3) in human neocortical tissue resected in deep-brain surgery, and in mice as control. Most pvBCs showed robust GABA_AR-mediated self-innervation in both species, but autapses were rare in nonfast-spiking GABAergic interneurons. Light- and electron microscopy analyses revealed pvBC axons innervating their own soma and proximal dendrites. GABAergic self-inhibition conductance was similar in human and mouse pvBCs and comparable to that of synapses from pvBCs to other L2/3 neurons. Autaptic conductance prolonged somatic inhibition in pvBCs after a spike and inhibited repetitive firing. Perisomatic autaptic inhibition is common in both human and mouse pvBCs of supragranular neocortex, where they efficiently control discharge of the pvBCs.