Recurrent Excitation Research Articles

State modulation in spatial networks with three interneuron subtypes.

Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions of cell-type specific activity and synaptic connections to dynamics of a spatially organized spiking neuron network. We find that the firing rates of the somatostatin (SOM) interneurons align closely with the level of network synchrony irrespective of the target of modulatory input. Further analysis reveals that inhibition from SOM to parvalbumin (PV) interneurons must be limited to allow gradual transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM neurons determines the level of synchrony achievable in the network. Our results are consistent with recent experimental findings on cell-type specific manipulations. Overall, our results highlight common dynamic regimes achieved across modulations of different cell populations and identify SOM cells as the main driver of network synchrony.

Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.

Inhibiting presynaptic calcium channel motility in the auditory cortex suppresses synchronized input processing.

The emergent coherent population activity from thousands of stochastic neurons in the brain is believed to constitute a key neuronal mechanism for salient processing of external stimuli and its link to internal states like attention and perception. In the sensory cortex, functional cell assemblies are formed by recurrent excitation and inhibitory influences. The stochastic dynamics of each cell involved is largely orchestrated by presynaptic CAV2.1 voltage-gated calcium channels (VGCCs). Cav2.1 VGCCs initiate the release of neurotransmitters from the presynaptic compartment and are therefore able to add variability into synaptic transmission which can be partly explained by their mobile organization around docked vesicles. To investigate the relevance of Cav2.1 channel surface motility for the input processing in the primary auditory cortex (A1) in vivo, we make use of a new optogenetic system which allows for acute, reversable cross-linking Cav2.1 VGCCs via a photo-cross-linkable cryptochrome mutant, CRY2olig. In order to map neuronal activity across all cortical layers of the A1, we performed laminar current-source density (CSD) recordings with varying auditory stimulus sets in transgenic mice with a citrine tag on the N-terminus of the VGCCs. Clustering VGCCs suppresses overall sensory-evoked population activity, particularly when stimuli lead to a highly synchronized distribution of synaptic inputs. Our findings reveal the importance of membrane dynamics of presynaptic calcium channels for sensory encoding by dynamically adjusting network activity across a wide range of synaptic input strength.

Intrinsic and Synaptic Contributions to Repetitive Spiking in Dentate Granule Cells.

Repetitive firing of granule cells (GCs) in the dentate gyrus (DG) facilitates synaptic transmission to the CA3 region. This facilitation can gate and amplify the flow of information through the hippocampus. High-frequency bursts in the DG are linked to behavior and plasticity, but GCs do not readily burst. Under normal conditions, a single shock to the perforant path in a hippocampal slice typically drives a GC to fire a single spike, and only occasionally more than one spike is seen. Repetitive spiking in GCs is not robust, and the mechanisms are poorly understood. Here, we used a hybrid genetically encoded voltage sensor to image voltage changes evoked by cortical inputs in many mature GCs simultaneously in hippocampal slices from male and female mice. This enabled us to study relatively infrequent double and triple spikes. We found GCs are relatively homogeneous and their double spiking behavior is cell autonomous. Blockade of GABA type A receptors increased multiple spikes and prolonged the interspike interval, indicating inhibitory interneurons limit repetitive spiking and set the time window for successive spikes. Inhibiting synaptic glutamate release showed that recurrent excitation mediated by hilar mossy cells contributes to, but is not necessary for, multiple spiking. Blockade of T-type Ca2+ channels did not reduce multiple spiking but prolonged interspike intervals. Imaging voltage changes in different GC compartments revealed that second spikes can be initiated in either dendrites or somata. Thus, pharmacological and biophysical experiments reveal roles for both synaptic circuitry and intrinsic excitability in GC repetitive spiking.

Inspiratory and sigh breathing rhythms depend on distinct cellular signalling mechanisms in the preBötzinger complex.

Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air-breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. KEY POINTS: A simplified activity-based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population. Inspiration is attributable to a canonical excitatory network oscillator mechanism. Sigh emerges from intracellular calcium signalling. The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated. Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals.

A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia.

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

Metabolic energetics underlying attractors in neural models.

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.

Cholinergic Activation of Corticofugal Circuits in the Adult Mouse Prefrontal Cortex.

Acetylcholine (ACh) promotes neocortical output to the thalamus and brainstem by preferentially enhancing the postsynaptic excitability of layer 5 pyramidal tract (PT) neurons relative to neighboring intratelencephalic (IT) neurons. Less is known about how ACh regulates the excitatory synaptic drive of IT and PT neurons. To address this question, spontaneous excitatory postsynaptic potentials (sEPSPs) were recorded in dual recordings of IT and PT neurons in slices of prelimbic cortex from adult female and male mice. ACh (20 µM) enhanced sEPSP amplitudes, frequencies, rise-times, and half-widths preferentially in PT neurons. These effects were blocked by the muscarinic receptor antagonist atropine (1 µM). When challenged with pirenzepine (1 µM), an antagonist selective for M1-type muscarinic receptors, ACh instead reduced sEPSP frequencies, suggesting that ACh may generally suppress synaptic transmission in the cortex via non-M1 receptors. Cholinergic enhancement of sEPSPs in PT neurons was not sensitive to antagonism of GABA receptors with gabazine (10 µM) and CGP52432 (2.5 µM) but was blocked by tetrodotoxin (1 µM), suggesting that ACh enhances action-potential-dependent excitatory synaptic transmission in PT neurons. ACh also preferentially promoted the occurrence of synchronous sEPSPs in dual recordings of PT neurons relative to IT-PT and IT-IT parings. Finally, selective chemogenetic silencing of hM4Di-expressing PT, but not commissural IT, neurons blocked cholinergic enhancement of sEPSP amplitudes and frequencies in PT neurons. These data suggest that, in addition to selectively enhancing the postsynaptic excitability of PT neurons, M1 receptor activation promotes corticofugal output by amplifying recurrent excitation within networks of PT neurons.

Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales.

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across brain areas. We demonstrated that the neurons with Drift-Diffusion-Model-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e. single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.Significance StatementIn this paper we characterized the perceptual decision-making process across the mouse brain. Our findings shed more light on the decision-making process by analyzing the brain-wide electrophysiological recording dataset. This paper contains a comprehensive analysis to characterize different aspects of the evidence accumulation process, including the distribution of accumulator-like neurons, the timescale of information integration, accumulation architecture, and the relationship between accumulators' timescale and their integration circuit properties.

Increased presynaptic excitability in a migraine with aura mutation.

Migraine is a common and disabling neurological disorder. The headache and sensory amplifications of migraine are attributed to hyperexcitable sensory circuits, but a detailed understanding remains elusive. A mutation in casein kinase 1 delta (CK1δ) was identified in non-hemiplegic familial migraine with aura and advanced sleep phase syndrome. Mice carrying the CK1δT44A mutation were more susceptible to spreading depolarization (the phenomenon that underlies migraine aura), but mechanisms underlying this migraine-relevant phenotype were not known. We used a combination of whole-cell electrophysiology and multiphoton imaging, in vivo and in brain slices, to compare CK1δT44A mice (adult males) to their wild-type littermates. We found that despite comparable synaptic activity at rest, CK1δT44A neurons were more excitable upon repetitive stimulation than wild-type, with a reduction in presynaptic adaptation at excitatory but not inhibitory synapses. The mechanism of this adaptation deficit was a calcium-dependent enhancement of the size of the readily releasable pool of synaptic vesicles, and a resultant increase in glutamate release, in CK1δT44A compared to wild-type synapses. Consistent with this mechanism, CK1δT44A neurons showed an increase in the cumulative amplitude of excitatory post-synaptic currents, and a higher excitation-to-inhibition ratio during sustained activity compared to wild-type. At a local circuit level, action potential bursts elicited in CK1δT44A neurons triggered an increase in recurrent excitation compared to wild-type, and at a network level, CK1δT44A mice showed a longer duration of 'up state' activity, which is dependent on recurrent excitation. Finally, we demonstrated that the spreading depolarization susceptibility of CK1δT44A mice could be returned to wild-type levels with the same intervention (reduced extracellular calcium) that normalized presynaptic adaptation. Taken together, these findings show a stimulus-dependent presynaptic gain of function at glutamatergic synapses in a genetic model of migraine, that accounts for the increased spreading depolarization susceptibility and may also explain the sensory amplifications that are associated with the disease.

Neuropeptide Y Signaling Regulates Recurrent Excitation in the Auditory Midbrain.

Neuropeptides play key roles in shaping the organization and function of neuronal circuits. In the inferior colliculus (IC), which is in the auditory midbrain, Neuropeptide Y (NPY) is expressed by a class of GABAergic neurons that project locally and outside the IC. Most neurons in the IC have local axon collaterals; however, the organization and function of local circuits in the IC remain unknown. We previously found that excitatory neurons in the IC can express the NPY Y1 receptor (Y1R+) and application of the Y1R agonist, [Leu31, Pro34]-NPY (LP-NPY), decreases the excitability of Y1R+ neurons. As NPY signaling regulates recurrent excitation in other brain regions, we hypothesized that Y1R+ neurons form interconnected local circuits in the IC and that NPY decreases the strength of recurrent excitation in these circuits. To test this hypothesis, we used optogenetics to activate Y1R+ neurons in mice of both sexes while recording from other neurons in the ipsilateral IC. We found that nearly 80% of glutamatergic IC neurons express the Y1 receptor, providing extensive opportunities for NPY signaling to regulate local circuits. Additionally, Y1R+ neuron synapses exhibited modest short-term synaptic plasticity, suggesting that local excitatory circuits maintain their influence over computations during sustained stimuli. We further found that application of LP-NPY decreased recurrent excitation in the IC, suggesting that NPY signaling strongly regulates local circuit function in the auditory midbrain. Our findings show that Y1R+ excitatory neurons form interconnected local circuits in the IC, and their influence over local circuits is regulated by NPY signaling.SIGNIFICANCE STATEMENT Local networks play fundamental roles in shaping neuronal computations in the brain. The IC, localized in the auditory midbrain, plays an essential role in sound processing, but the organization of local circuits in the IC is largely unknown. Here, we show that IC neurons that express the Neuropeptide Y1 receptor (Y1R+ neurons) make up most of the excitatory neurons in the IC and form interconnected local circuits. Additionally, we found that NPY, which is a powerful neuromodulator known to shape neuronal activity in other brain regions, decreases the extensive recurrent excitation mediated by Y1R+ neurons in local IC circuits. Thus, our results suggest that local NPY signaling is a key regulator of auditory computations in the IC.

Effect of short-term plasticity on working memory

The way in which persistent firing activity and synaptic plasticity are orchestrated to underlie working memory in recurrent neural networks is not fully understood. Here, we build a continuous attractor network of pyramidal cells and interneurons to simulate an oculomotor delayed response task. Both short-term facilitation (STF) and short-term depression (STD) manifest at synapses between pyramidal cells. The efficacy of individual synapses depends on the time constants of STF and STD as well as the presynaptic firing rate. Self-sustained firing activity (i.e., a bump attractor) during the delay period encodes the cue position. The bump attractor becomes more robust against random drifts and distractions with enhancing STF or reducing STD. Keeping STF and STD at appropriate levels is crucial for optimizing network performance. Our results suggest that, besides slow recurrent excitation and strong global inhibition, short-term plasticity plays a prominent role in facilitating mnemonic behavior.

Exogenous AMPA downregulates gamma-frequency network oscillation in CA3 of rat hippocampal slices

Pharmacologically-induced persistent hippocampal γ oscillation in area CA3 requires activation of α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionate receptors (AMPARs). However, we demonstrated that exogenous AMPA dose-dependently inhibited carbachol (CCH)-induced γ oscillation in the CA3 area of rat hippocampal slices, but the underlying mechanism is not clear. Application of AMPARs antagonist NBQX (1 μM) did not affect γ oscillation power (γ power), nor AMPA-mediated γ power reduction. At 3 μM, NBQX had no effect on γ power but largely blocked AMPA-mediated γ power reduction. Ca2+-permeable AMPA receptor (CP-AMPAR) antagonist IEM1460 or CaMKK inhibitor STO-609 but not CaMKIIα inhibitor KN93 enhanced γ power, indicating that activation of CP-AMPAR or CaMKK negatively modulated CCH-induced γ oscillation. Either CP-AMPAR antagonist or CaMKK inhibitor alone did not affected AMPA-mediated γ power reduction, but co-administration of IEM1460 and NBQX (1 μM) largely prevented AMPA-mediated downregulation of γ suggesting that CP-AMPARs and CI-AMPARs are involved in AMPA downregulation of γ oscillation. The recurrent excitation recorded at CA3 stratum pyramidale was significantly reduced by AMPA application. Our results indicate that AMPA downregulation of γ oscillation may be related to the reduced recurrent excitation within CA3 local neuronal network due to rapid CI-AMPAR and CP-AMPAR activation.

Recurrent Circuits Amplify Corticofugal Signals and Drive Feedforward Inhibition in the Inferior Colliculus.

The inferior colliculus (IC) is a midbrain hub critical for perceiving complex sounds, such as speech. In addition to processing ascending inputs from most auditory brainstem nuclei, the IC receives descending inputs from auditory cortex that control IC neuron feature selectivity, plasticity, and certain forms of perceptual learning. Although corticofugal synapses primarily release the excitatory transmitter glutamate, many physiology studies show that auditory cortical activity has a net inhibitory effect on IC neuron spiking. Perplexingly, anatomy studies imply that corticofugal axons primarily target glutamatergic IC neurons while only sparsely innervating IC GABA neurons. Corticofugal inhibition of the IC may thus occur largely independently of feedforward activation of local GABA neurons. We shed light on this paradox using in vitro electrophysiology in acute IC slices from fluorescent reporter mice of either sex. Using optogenetic stimulation of corticofugal axons, we find that excitation evoked with single light flashes is indeed stronger in presumptive glutamatergic neurons compared with GABAergic neurons. However, many IC GABA neurons fire tonically at rest, such that sparse and weak excitation suffices to significantly increase their spike rates. Furthermore, a subset of glutamatergic IC neurons fire spikes during repetitive corticofugal activity, leading to polysynaptic excitation in IC GABA neurons owing to a dense intracollicular connectivity. Consequently, recurrent excitation amplifies corticofugal activity, drives spikes in IC GABA neurons, and generates substantial local inhibition in the IC. Thus, descending signals engage intracollicular inhibitory circuits despite apparent constraints of monosynaptic connectivity between auditory cortex and IC GABA neurons.SIGNIFICANCE STATEMENT Descending "corticofugal" projections are ubiquitous across mammalian sensory systems, and enable the neocortex to control subcortical activity in a predictive or feedback manner. Although corticofugal neurons are glutamatergic, neocortical activity often inhibits subcortical neuron spiking. How does an excitatory pathway generate inhibition? Here we study the corticofugal pathway from auditory cortex to inferior colliculus (IC), a midbrain hub important for complex sound perception. Surprisingly, cortico-collicular transmission was stronger onto IC glutamatergic compared with GABAergic neurons. However, corticofugal activity triggered spikes in IC glutamate neurons with local axons, thereby generating strong polysynaptic excitation and feedforward spiking of GABAergic neurons. Our results thus reveal a novel mechanism that recruits local inhibition despite limited monosynaptic convergence onto inhibitory networks.

Synaptic and circuit mechanisms prevent detrimentally precise correlation in the developing mammalian visual system.

The developing visual thalamus and cortex extract positional information encoded in the correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of connectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit refinement period to explore the role of synaptic and circuit properties in the regulation of such neural correlations. We find that the NMDA receptor dominance, combined with weak recurrent excitation and inhibition characteristic of this age, prevents the emergence of spike-correlations between thalamocortical neurons on the millisecond timescale. Such precise correlations, which would emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the spatial information contained by thalamic spikes, and therefore we term them 'parasitic' correlations. Our results suggest that developing synapses and circuits evolved mechanisms to compensate for such detrimental parasitic correlations arising from the unrefined and immature circuit.

Scaling down synaptic strength results in changes to the pattern of fictive breathing in a model of the brainstem respiratory network: medullary and pontine sources

Opioids remain a primary vehicle for pain management prescribed by clinicians. However, opioid-induced respiratory depression (OIRD) can cause apneas and be fatal at elevated doses. Two sites of action for these opioids have been suggested in the brainstem, one in the pons and the other in the preBötzinger Complex of the medulla. Additionally, there is electrophysiological evidence suggesting that an inward-rectifying K + current is opened upon μ-opioid receptor (μOR) activation causing neuronal hyperpolarizations. Activation of μORs may cause depletion of glutamate release from excitatory neurons. Our previous simulation efforts have demonstrated the plausibility of presynaptic actions of opioids among inspiratory neurons of the medulla and pons in depressing inspiratory drive or eliciting apnea within specific and singular synaptic connections. Here, we tested the effects of diminishing the strength of excitatory synaptic currents among several different classes of respiratory neurons in a large-scale model of the brainstem respiratory network and whether it would replicate OIRD. We simulated the simultaneous actions of μOR on multiple presynaptic sites among the medullary inspiratory network. We began by scaling down the synaptic conductances of target populations of medullary inspiratory neurons. The core of the model is made up of neurons that produce the fundamental inspiratory rhythm (“I-Driver” neurons), as well as neurons that normally have an inspiratory decrementing (“I-Dec”) or inspiratory augmenting (“I-Aug”) firing pattern and various expiratory phase firing patterns (“E-Dec,” “E-Aug,” “E-Tonic”). I-Driver neurons are analogous to medullary preBötzinger Complex neurons that putatively express μORs. In our model, these neurons project back onto other I-Driver neurons to promote recurrent excitation but also project onto I-Aug and I-Dec populations. Decreasing the synaptic strength from I-Driver neurons to these populations collectively slows fictive breathing until extended apnea occurs. To further understand how this apnea develops we systematically scaled down the synaptic strengths of I-Driver to each of the other specific populations and found that the excitatory connection from I-Driver to I-Dec populations is most influential in producing apnea. However, we also found that excitatory drive from tonic, non-respiratory modulated (NRM) neurons in the pons to the medulla could influence this pattern by grading down synaptic strength. In addition to any direct hyperpolarizing effects of opioids on μOR-expressing neurons, these data support the plausibility that OIRD could arise from changes due to glutamate release. This work was supported by the National Institutes of Health grant 1R01HL155721-01, 1R01HL163008, and T32HL134621. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3

Recurrent connectivity between excitatory neurons and the strength of feedback from inhibitory neurons are critical determinants of the dynamics and computational properties of neuronal circuits. Toward a better understanding of these circuit properties in regions CA1 and CA3 of the hippocampus, we performed optogenetic manipulations combined with large-scale unit recordings in rats under anesthesia and in quiet waking, using photoinhibition and photoexcitation with different light-sensitive opsins. In both regions, we saw striking paradoxical responses: subsets of cells increased firing during photoinhibition, while other cells decreased firing during photoexcitation. These paradoxical responses were more prominent in CA3 than in CA1, but, notably, CA1 interneurons showed increased firing in response to photoinhibition of CA3. These observations were recapitulated in simulations where we modeled both CA1 and CA3 as inhibition-stabilized networks in which strong recurrent excitation is balanced by feedback inhibition. To directly test the inhibition-stabilized model, we performed large-scale photoinhibition directed at (GAD-Cre) inhibitory cells and found that interneurons in both regions increased firing when photoinhibited, as predicted. Our results highlight the often-paradoxical circuit dynamics that are evidenced during optogenetic manipulations and indicate that, contrary to long-standing dogma, both CA1 and CA3 hippocampal regions display strongly recurrent excitation, which is stabilized through inhibition.

Mechanisms regulating the properties of inhibition-based gamma oscillations in primate prefrontal and parietal cortices.

In primates, the dorsolateral prefrontal (DLPFC) and posterior parietal (PPC) cortices are key nodes in the working memory network. The working memory-related gamma oscillations induced in these areas, predominantly in layer 3, exhibit higher frequency in DLPFC. Although these regional differences in oscillation frequency are likely essential for information transfer between DLPFC and PPC, the mechanisms underlying these differences remain poorly understood. We investigated, in rhesus monkey, the DLPFC and PPC layer 3 pyramidal neuron (L3PN) properties that might regulate oscillation frequency and assessed the effects of these properties simulating oscillations in computational models. We found that GABAAR-mediated synaptic inhibition synchronizes L3PNs in both areas, but analysis of GABAAR mRNA levels and inhibitory synaptic currents suggested similar mechanisms of inhibition-mediated synchrony in DLPFC and PPC. Basal dendrite spine density and AMPAR/NMDAR mRNA levels were higher in DLPFC L3PNs, whereas excitatory synaptic currents were similar between areas. Therefore, synaptically evoked excitation might be stronger in DLPFC L3PNs due to a greater quantity of synapses in basal dendrites, a main target of recurrent excitation. Simulations in computational networks showed that oscillation frequency and power increased with increasing recurrent excitation, suggesting a mechanism by which the DLPFC-PPC differences in oscillation properties are generated.

Recurrent network interactions explain tectal response variability and experience-dependent behavior.

Response variability is an essential and universal feature of sensory processing and behavior. It arises from fluctuations in the internal state of the brain, which modulate how sensory information is represented and transformed to guide behavioral actions. In part, brain state is shaped by recent network activity, fed back through recurrent connections to modulate neuronal excitability. However, the degree to which these interactions influence response variability and the spatial and temporal scales across which they operate, are poorly understood. Here, we combined population recordings and modeling to gain insights into how neuronal activity modulates network state and thereby impacts visually evoked activity and behavior. First, we performed cellular-resolution calcium imaging of the optic tectum to monitor ongoing activity, the pattern of which is both a cause and consequence of changes in network state. We developed a minimal network model incorporating fast, short range, recurrent excitation and long-lasting, activity-dependent suppression that reproduced a hallmark property of tectal activity - intermittent bursting. We next used the model to estimate the excitability state of tectal neurons based on recent activity history and found that this explained a portion of the trial-to-trial variability in visually evoked responses, as well as spatially selective response adaptation. Moreover, these dynamics also predicted behavioral trends such as selective habituation of visually evoked prey-catching. Overall, we demonstrate that a simple recurrent interaction motif can be used to estimate the effect of activity upon the incidental state of a neural network and account for experience-dependent effects on sensory encoding and visually guided behavior.

Choice selective inhibition drives stability and competition in decision circuits

During perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, the functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by either stabilizing or destabilizing the saddle-point dynamics underlying decisions in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations.