Network control through coordinated inhibition

Neurobiological Causal Models of Language Processing

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

Glutamate Spillover Dynamically Strengthens Gabaergic Synaptic Inhibition of the Hypothalamic Paraventricular Nucleus

The hypothalamic paraventricular nucleus (PVN) is strongly inhibited by γ-aminobutyric acid (GABA) from the surrounding peri-nuclear zone (PNZ). Because glutamate mediates fast excitatory transmission and is substrate for GABA synthesis, we tested its capacity to dynamically strengthen GABA inhibition. In PVN slices from male mice, bath glutamate applied during ionotropic glutamate receptor blockade increased PNZ-evoked inhibitory postsynaptic currents (eIPSCs) without affecting GABA-A receptor agonist currents or single-channel conductance, implicating a presynaptic mechanism(s). Consistent with this interpretation, bath glutamate failed to strengthen IPSCs during pharmacological saturation of GABA-A receptors. Presynaptic analyses revealed that glutamate did not affect paired-pulse ratio, peak eIPSC variability, GABA vesicle recycling speed, or readily releasable pool (RRP) size. Notably, glutamate-GABA strengthening (GGS) was unaffected by metabotropic glutamate receptor blockade and graded external Ca2+ when normalized to baseline amplitude. GGS was prevented by pan- but not glial-specific inhibition of glutamate uptake and by inhibition of glutamic acid decarboxylase (GAD), indicating reliance on glutamate uptake by neuronal excitatory amino acid transporter 3 (EAAT3) and enzymatic conversion of glutamate to GABA. EAAT3 immunoreactivity was strongly localized to presumptive PVN GABA terminals. High bath K+ also induced GGS, which was prevented by glutamate vesicle depletion, indicating that synaptic glutamate release strengthens PVN GABA inhibition. GGS suppressed PVN cell firing, indicating its functional significance. In sum, PVN GGS buffers neuronal excitation by apparent "over-filling" of vesicles with GABA synthesized from synaptically released glutamate. We posit that GGS protects against sustained PVN excitation and excitotoxicity while potentially aiding stress adaptation and habituation.

Spike timing-dependent plasticity and memory

Spike timing-dependent plasticity (STDP) is a bidirectional form of synaptic plasticity discovered about 30 years ago and based on the relative timing of pre- and post-synaptic spiking activity with a millisecond precision. STDP is thought to be involved in the formation of memory but the millisecond-precision spike-timing required for STDP is difficult to reconcile with the much slower timescales of behavioral learning. This review therefore aims to expose and discuss recent findings about i) the multiple STDP learning rules at both excitatory and inhibitory synapses in vitro, ii) the contribution of STDP-like synaptic plasticity in the formation of memory in vivo and iii) the implementation of STDP rules in artificial neural networks and memristive devices.

Forebrain E-I balance controlled in cognition through coordinated inhibition and inhibitory transcriptome mechanism

Introduction

Forebrain neural networks are vital for cognitive functioning, and their excitatory-inhibitory (E-I) balance is governed by neural homeostasis. However, the homeostatic control strategies and transcriptomic mechanisms that maintain forebrain E-I balance and optimal cognition remain unclear.

Methods

We used patch-clamp and RNA sequencing to investigate the patterns of neural network homeostasis with suppressing forebrain excitatory neural activity and spatial training.

Results

We found that inhibitory transmission and receptor transcription were reduced in tamoxifen-inducible Kir2.1 conditional knock-in mice. In contrast, spatial training increased inhibitory synaptic connections and the transcription of inhibitory receptors.

Discussion

Our study provides significant evidence that inhibitory systems play a critical role in the homeostatic control of the E-I balance in the forebrain during cognitive training and E-I rebalance, and we have provided insights into multiple gene candidates for cognition-related homeostasis in the forebrain.

Where is the error? Hierarchical predictive coding through dendritic error computation

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

Activation-Inhibition Coordination in Neuron, Brain, and Behavior Sequencing/Organization: Implications for Laterality and Lateralization

Activation-inhibition coordination is considered a dynamic process that functions as a common mechanism in the synchronization and functioning of neurons, brain, behavior, and their sequencing/organization, including over these different scales. The concept has broad applicability, for example, in applications to maladaptivity/atypicality. Young developed the hypothesis to help explain the efficacy of right-hand reaching to grasp in 1-month-olds, a study that implicated that the left hemisphere is specialized for activation-inhibition coordination. This underlying left-hemisphere function, noted to characterize the left hemisphere right from birth, can explain equally its language and fine motor skills, for example. The right hemisphere appears specialized for less complex inhibitory skills, such as outright damping/inhibition. The hypotheses related to inhibition and hemispheric specialization that appear in the literature typically refer to right hemisphere skills in these regards. The research to present also refers to excitation/inhibition balance/ratio in synaptic function, but not to coordination in the sense described here. Furthermore, it refers to the inhibitory function widely in neuronal networks. The paper presents a comprehensive literature review, framing the research in terms of the proposed concept. Further, the paper presents a broad model of activation-inhibition coordination that can help better understand neuron, brain, and behavior, generally, and left hemisphere specialization, specifically.

The sloppy relationship between neural circuit structure and function

Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: Function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are a closely related to the structure and implementation of functionally relevant neural manifolds. Abstract figure legend What are the relationships between noisy and highly variable microscopic neural circuit variables on the one hand and the generation of behaviour on the other? Here it is proposed that an intermediate level of description exists where this relationship can be understood in terms of low-dimensional dynamics. Recordings of neural activity during unconstrained behaviour and the development of new machine learning methods will help to uncover these links. This article is protected by copyright. All rights reserved.

Dendritic Excitability and Synaptic Plasticity In Vitro and In Vivo

Much of our understanding of dendritic and synaptic physiology comes from in vitro experimentation, where the afforded mechanical stability and convenience of applying drugs allowed patch-clamping based recording techniques to investigate ion channel distributions, their gating kinetics, and to uncover dendritic integrative and synaptic plasticity rules. However, with current efforts to study these questions in vivo, there is a great need to translate existing knowledge between in vitro and in vivo experimental conditions. In this review, we identify discrepancies between in vitro and in vivo ionic composition of extracellular media and discuss how changes in ionic composition alter dendritic excitability and plasticity induction. Here, we argue that under physiological in vivo ionic conditions, dendrites are expected to be more excitable and the threshold for synaptic plasticity induction to be lowered. Consequently, the plasticity rules described in vitro vary significantly from those implemented in vivo.

Axonal CB1 Receptors Mediate Inhibitory Bouton Formation via cAMP Increase and PKA

Experience-dependent formation and removal of inhibitory synapses are essential throughout life. For instance, GABAergic synapses are removed to facilitate learning, and strong excitatory activity is accompanied by the formation of inhibitory synapses to maintain coordination between excitation and inhibition. We recently discovered that active dendrites trigger the growth of inhibitory synapses via CB1 receptor-mediated endocannabinoid signaling, but the underlying mechanism remained unclear. Using two-photon microscopy to monitor the formation of individual inhibitory boutons in hippocampal organotypic slices from mice (both sexes), we found that CB1 receptor activation mediated the formation of inhibitory boutons and promoted their subsequent stabilization. Inhibitory bouton formation did not require neuronal activity and was independent of G_i/o-protein signaling, but was directly induced by elevating cAMP levels using forskolin and by activating G_s-proteins using DREADDs. Blocking PKA activity prevented CB1 receptor-mediated inhibitory bouton formation. Our findings reveal that axonal CB1 receptors signal via unconventional downstream pathways and that inhibitory bouton formation is triggered by an increase in axonal cAMP levels. Our results demonstrate an unexpected role for axonal CB1 receptors in axon-specific, and context-dependent, inhibitory synapse formation.SIGNIFICANCE STATEMENT Coordination between excitation and inhibition is required for proper brain function throughout life. It was previously shown that new inhibitory synapses can be formed in response to strong excitation to maintain this coordination, and this was mediated by endocannabinoid signaling via CB1 receptors. As activation of CB1 receptors generally results in the suppression of synaptic transmission, it remained unclear how CB1 receptors can mediate the formation of inhibitory synapses. Here we show that CB1 receptors on inhibitory axons signal via unconventional intracellular pathways and that inhibitory bouton formation is triggered by an increase in axonal cAMP levels and requires PKA activity. Our findings point to a central role for axonal cAMP signaling in activity-dependent inhibitory synapse formation.

Amyloid-β Oligomers Induce Only Mild Changes to Inhibitory Bouton Dynamics

The amyloid-β protein precursor is highly expressed in a subset of inhibitory neuron in the hippocampus, and inhibitory neurons have been suggested to play an important role in early Alzheimer’s disease plaque load. Here we investigated bouton dynamics in axons of hippocampal interneurons in two independent amyloidosis models. Short-term (24 h) amyloid-β (Aβ)-oligomer application to organotypic hippocampal slices slightly increased inhibitory bouton dynamics, but bouton density and dynamics were unchanged in hippocampus slices of young-adult App^{NL - F - G}-mice, in which Aβ levels are chronically elevated. These results indicate that loss or defective adaptation of inhibitory synapses are not a major contribution to Aβ-induced hyperexcitability.

My Neighbour Hetero-deconstructing the mechanisms underlying heterosynaptic plasticity

Synapses change in strength following patterns of activity, but in many cases seemingly inactive neighbouring synapses also undergo changes in strength. These heterosynaptic changes occur across developmental time-points in various brain circuits in different species, but their precise molecular mechanisms are not well understood. Additionally, heterosynaptic changes can mirror homosynaptic plasticity or occur in opposition to homosynaptic changes. In this review we consider what useful functionality heterosynaptic dynamics could potentially endow the circuit with, and the underlying signalling events that implement heterosynaptic changes. We discuss what unanswered questions remain, and what the future looks like for understanding the logic of synaptic plasticity.