Functional roles of short-term synaptic plasticity with an emphasis on inhibition

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

Bayesian longitudinal tensor response regression for modeling neuroplasticity

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.

Short-term plasticity in the spinal nociceptive system

ABSTRACT

Somatosensory information is delivered to neuronal networks of the dorsal horn (DH) of the spinal cord by the axons of primary afferent neurons that encode the intensity of peripheral sensory stimuli under the form of a code based on the frequency of action potential firing. The efficient processing of these messages within the DH involves frequency-tuned synapses, a phenomenon linked to their ability to display activity-dependent forms of short-term plasticity (STP). By affecting differently excitatory and inhibitory synaptic transmissions, these STP properties allow a powerful gain control in DH neuronal networks that may be critical for the integration of nociceptive messages before they are forwarded to the brain, where they may be ultimately interpreted as pain. Moreover, these STPs can be finely modulated by endogenous signaling molecules, such as neurosteroids, adenosine, or GABA. The STP properties of DH inhibitory synapses might also, at least in part, participate in the pain-relieving effect of nonpharmacological analgesic procedures, such as transcutaneous electrical nerve stimulation, electroacupuncture, or spinal cord stimulation. The properties of target-specific STP at inhibitory DH synapses and their possible contribution to electrical stimulation-induced reduction of hyperalgesic and allodynic states in chronic pain will be reviewed and discussed.

Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.

River zebrafish combine behavioral plasticity and generalized morphology with specialized sensory and metabolic physiology to survive in a challenging environment

Phenotypes that allow animals to detect, weather, and predict changes efficiently are essential for survival in fluctuating environments. Some phenotypes may remain specialized to suit an environment perfectly, while others become more plastic or generalized, shifting flexibly to match current context or adopting a form that can utilize a wide range of contexts. Here, we tested the differences in behavior, morphology, sensory and metabolic physiology between wild zebrafish (Danio rerio) in highly variable fast-flowing rivers and still-water sites. We found that river zebrafish moved at higher velocities than did still-water fish, had lower oxygen demands, and responded less vigorously to small changes in flow rate, as we might expect for fish that are well-suited to high-flow environments. River zebrafish also had less streamlined bodies and were more behaviorally plastic than were still-water zebrafish, both features that may make them better-suited to a transitional lifestyle. Our results suggest that zebrafish use distinct sensory mechanisms and metabolic physiology to reduce energetic costs of living in fast-flowing water while relying on morphology and behavior to create flexible solutions to a challenging habitat. Insights on animals' reliance on traits with different outcomes provide a framework to better understand their survival in future environmental fluctuations.

Recovery kinetics of short-term depression of GABAergic and glutamatergic synapses at layer 2/3 pyramidal cells in the mouse barrel cortex

Introduction

Short-term synaptic plasticity (STP) is a widespread mechanism underlying activity-dependent modifications of cortical networks.

Methods

To investigate how STP influences excitatory and inhibitory synapses in layer 2/3 of mouse barrel cortex, we combined whole-cell patch-clamp recordings from visually identified pyramidal neurons (PyrN) and parvalbumin-positive interneurons (PV-IN) of cortical layer 2/3 in acute slices with electrical stimulation of afferent fibers in layer 4 and optogenetic activation of PV-IN.

Results

These experiments revealed that electrical burst stimulation (10 pulses at 10 Hz) of layer 4 afferents to layer 2/3 neurons induced comparable short-term depression (STD) of glutamatergic postsynaptic currents (PSCs) in PyrN and in PV-IN, while disynaptic GABAergic PSCs in PyrN showed a stronger depression. Burst-induced depression of glutamatergic PSCs decayed within <4 s, while the decay of GABAergic PSCs required >11 s. Optogenetically-induced GABAergic PSCs in PyrN also demonstrated STD after burst stimulation, with a decay of >11 s. Excitatory postsynaptic potentials (EPSPs) in PyrN were unaffected after electrical burst stimulation, while a selective optogenetic STD of GABAergic synapses caused a transient increase of electrically evoked EPSPs in PyrN.

Discussion

In summary, these results demonstrate substantial short-term plasticity at all synapses investigated and suggest that the prominent STD observed in GABAergic synapses can moderate the functional efficacy of glutamatergic STD after repetitive synaptic stimulations. This mechanism may contribute to a reliable information flow toward the integrative layer 2/3 for complex time-varying sensory stimuli.

A computational view of short-term plasticity and its implications for E-I balance

Short-term plasticity (STP) and excitatory-inhibitory balance (EI balance) are both ubiquitous building blocks of brain circuits across the animal kingdom. The synapses involved in EI are also subject to short-term plasticity, and several experimental studies have shown that their effects overlap. Recent computational and theoretical work has begun to highlight the functional implications of the intersection of these motifs. The findings are nuanced: while there are general computational themes, such as pattern tuning, normalization, and gating, much of the richness of these interactions comes from region- and modality specific tuning of STP properties. Together these findings point towards the STP-EI balance combination as being a versatile and highly efficient neural building block for a wide range of pattern-specific responses.

Short-term synaptic plasticity in emerging devices for neuromorphic computing

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

What the hippocampus tells the HPA axis: Hippocampal output attenuates acute stress responses via disynaptic inhibition of CRF+ PVN neurons

The hippocampus exerts inhibitory feedback on the release of glucocorticoids. Because the major hippocampal efferent projections are excitatory, it has been hypothesized that this inhibition is mediated by populations of inhibitory neurons in the hypothalamus or elsewhere. These regions would be excited by hippocampal efferents and project to corticotropin-releasing factor (CRF) cells in the paraventricular nucleus of the hypothalamus (PVN). A direct demonstration of the synaptic responses elicited by hippocampal outputs in PVN cells or upstream GABAergic interneurons has not been provided previously. Here, we used viral vectors to express channelrhodopsin (ChR) and enhanced yellow fluorescent protein (EYFP) in pyramidal cells in the ventral hippocampus (vHip) in mice expressing tdTomato in GABA- or CRF-expressing neurons. We observed dense innervation of the bed nucleus of the stria terminalis (BNST) by labeled vHip axons and sparse labeling within the PVN. Using whole-cell voltage-clamp recording in parasagittal brain slices containing the BNST and PVN, photostimulation of vHip terminals elicited rapid excitatory postsynaptic currents (EPSCs) and longer-latency inhibitory postsynaptic currents (IPSCs) in both CRF+ and GAD + cells. The ratio of synaptic excitation and inhibition was maintained in CRF + cells during 20 Hz stimulus trains. Photostimulation of hippocampal afferents to the BNST and PVN in vivo inhibited the rise in blood glucocorticoid levels produced by acute restraint stress. We thus provide functional evidence suggesting that hippocampal output to the BNST contributes to a net inhibition of the hypothalamic-pituitary axis, providing further mechanistic insights into this process using methods with enhanced spatial and temporal resolution.

Synaptic Dynamics Convey Differential Sensitivity to Input Pattern Changes in Two Muscles Innervated by the Same Motor Neurons

Postsynaptic responses depend on input patterns as well as short-term synaptic plasticity, summation, and postsynaptic membrane properties, but the interactions of those dynamics with realistic input patterns are not well understood. We recorded the responses of the two pyloric dilator (PD) muscles, cpv2a and cpv2b, that are innervated by and receive identical periodic bursting input from the same two motor neurons in the lobster Homarus americanus. Cpv2a and cpv2b showed quantitative differences in membrane nonlinearities and synaptic summation. At a short timescale, responses in both muscles were dominated by facilitation, albeit with different frequency and time dependence. Realistic burst stimulations revealed more substantial differences. Across bursts, cpv2a showed transient depression, whereas cpv2b showed transient facilitation. Steady-state responses to bursting input also differed substantially. Neither muscle had a monotonic dependence on frequency, but cpv2b showed particularly pronounced bandpass filtering. Cpv2a was sensitive to changes in both burst frequency and intra-burst spike frequency, whereas, despite its much slower responses, cpv2b was largely insensitive to changes in burst frequency. Cpv2a was sensitive to both burst duration and number of spikes per burst, whereas cpv2b was sensitive only to the former parameter. Neither muscle showed consistent sensitivity to changes in the overall spike interval structure, but cpv2b was surprisingly sensitive to changes in the first intervals in each burst, a parameter known to be regulated by dopamine (DA) modulation of spike propagation of the presynaptic axon. These findings highlight how seemingly minor circuit output changes mediated by neuromodulation could be read out differentially at the two synapses.

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.

Sensory Adaptation in the Whisker-Mediated Tactile System: Physiology, Theory, and Function

In the natural environment, organisms are constantly exposed to a continuous stream of sensory input. The dynamics of sensory input changes with organism's behaviour and environmental context. The contextual variations may induce >100-fold change in the parameters of the stimulation that an animal experiences. Thus, it is vital for the organism to adapt to the new diet of stimulation. The response properties of neurons, in turn, dynamically adjust to the prevailing properties of sensory stimulation, a process known as "neuronal adaptation." Neuronal adaptation is a ubiquitous phenomenon across all sensory modalities and occurs at different stages of processing from periphery to cortex. In spite of the wealth of research on contextual modulation and neuronal adaptation in visual and auditory systems, the neuronal and computational basis of sensory adaptation in somatosensory system is less understood. Here, we summarise the recent finding and views about the neuronal adaptation in the rodent whisker-mediated tactile system and further summarise the functional effect of neuronal adaptation on the response dynamics and encoding efficiency of neurons at single cell and population levels along the whisker-mediated touch system in rodents. Based on direct and indirect pieces of evidence presented here, we suggest sensory adaptation provides context-dependent functional mechanisms for noise reduction in sensory processing, salience processing and deviant stimulus detection, shift between integration and coincidence detection, band-pass frequency filtering, adjusting neuronal receptive fields, enhancing neural coding and improving discriminability around adapting stimuli, energy conservation, and disambiguating encoding of principal features of tactile stimuli.

Inhibitory interneurons with differential plasticities at their connections tune excitatory/inhibitory balance in the spinal nociceptive system

ABSTRACT

Networks of the dorsal-horn of the spinal-cord process nociceptive information from the periphery. In these networks, the excitation/inhibition balance is critical to shape this nociceptive information and to gate it to the brain where it is interpreted as pain. Our aim was to define whether short-term plasticity of inhibitory connections could tune this inhibition/excitation balance by differentially controlling excitatory and inhibitory microcircuits. To this end, we used spinal-cord slices from adult mice expressing enhanced green fluorescent protein (eGFP) under the GAD65 promoter and recorded from both eGFP+ (putative inhibitory) and eGFP- (putative excitatory) neurons of lamina II while stimulating single presynaptic GABAergic interneurons at various frequencies. Our results indicate that GABAergic neurons of lamina II simultaneously contact eGFP- and eGFP+ neurons, but these connections display very different frequency-dependent short-term plasticities. Connections onto eGFP- interneurons displayed limited frequency-dependent changes, and strong time-dependent summation of inhibitory synaptic currents that was however subjected to a tonic activity-dependent inhibition involving A1 adenosine receptors. In contrast, GABAergic connections onto eGFP+ interneurons expressed pronounced frequency-dependent depression, thus favoring disinhibition at these synapses by a mechanism involving the activation of GABAB autoreceptors at low frequency. Interestingly, the balance favors inhibition at frequencies associated with intense pain whether it favors excitation at frequencies associated with low pain. Therefore, these target- and frequency-specific plasticities allow to tune the balance between inhibition and disinhibition while processing frequency-coded information from primary afferents. These short-term plasticities and their modulation by A1 and GABAB receptors might represent an interesting target in pain-alleviating strategies.

The Imbalanced Plasticity Hypothesis of Schizophrenia-Related Psychosis: A Predictive Perspective

A considerable number of studies have attempted to account for the psychotic aspects of schizophrenia in terms of the influential predictive coding (PC) hypothesis. We argue that the prediction-oriented perspective on schizophrenia-related psychosis may benefit from a mechanistic model that: 1) gives due weight to the extent to which alterations in short- and long-term synaptic plasticity determine the degree and the direction of the functional disruption that occurs in psychosis; and 2) addresses the distinction between the two central syndromes of psychosis in schizophrenia: disorganization and reality-distortion. To accomplish these goals, we propose the Imbalanced Plasticity Hypothesis - IPH, and demonstrate that it: 1) accounts for commonalities and differences between disorganization and reality distortion in terms of excessive (hyper) or insufficient (hypo) neuroplasticity, respectively; 2) provides distinct predictions in the cognitive and electrophysiological domains; and 3) is able to reconcile conflicting PC-oriented accounts of psychosis.

Kv1.1 channels mediate network excitability and feed-forward inhibition in local amygdala circuits

K_v1.1 containing potassium channels play crucial roles towards dampening neuronal excitability. Mice lacking K_v1.1 subunits (Kcna1^−/−) display recurrent spontaneous seizures and often exhibit sudden unexpected death. Seizures in Kcna1^−/− mice resemble those in well-characterized models of temporal lobe epilepsy known to involve limbic brain regions and spontaneous seizures result in enhanced cFos expression and neuronal death in the amygdala. Yet, the functional alterations leading to amygdala hyperexcitability have not been identified. In this study, we used Kcna1^−/− mice to examine the contributions of K_v1.1 subunits to excitability in neuronal subtypes from basolateral (BLA) and central lateral (CeL) amygdala known to exhibit distinct firing patterns. We also analyzed synaptic transmission properties in an amygdala local circuit predicted to be involved in epilepsy-related comorbidities. Our data implicate K_v1.1 subunits in controlling spontaneous excitatory synaptic activity in BLA pyramidal neurons. In the CeL, K_v1.1 loss enhances intrinsic excitability and impairs inhibitory synaptic transmission, notably resulting in dysfunction of feed-forward inhibition, a critical mechanism for controlling spike timing. Overall, we find inhibitory control of CeL interneurons is reduced in Kcna1^−/− mice suggesting that basal inhibitory network functioning is less able to prevent recurrent hyperexcitation related to seizures.

Cortical Microcircuit Mechanisms of Mismatch Negativity and Its Underlying Subcomponents

In the neocortex, neuronal processing of sensory events is significantly influenced by context. For instance, responses in sensory cortices are suppressed to repetitive or redundant stimuli, a phenomenon termed “stimulus-specific adaptation” (SSA). However, in a context in which that same stimulus is novel, or deviates from expectations, neuronal responses are augmented. This augmentation is termed “deviance detection” (DD). This contextual modulation of neural responses is fundamental for how the brain efficiently processes the sensory world to guide immediate and future behaviors. Notably, context modulation is deficient in some neuropsychiatric disorders such as schizophrenia (SZ), as quantified by reduced “mismatch negativity” (MMN), an electroencephalography waveform reflecting a combination of SSA and DD in sensory cortex. Although the role of NMDA-receptor function and other neuromodulatory systems on MMN is established, the precise microcircuit mechanisms of MMN and its underlying components, SSA and DD, remain unknown. When coupled with animal models, the development of powerful precision neurotechnologies over the past decade carries significant promise for making new progress into understanding the neurobiology of MMN with previously unreachable spatial resolution. Currently, rodent models represent the best tool for mechanistic study due to the vast genetic tools available. While quantifying human-like MMN waveforms in rodents is not straightforward, the “oddball” paradigms used to study it in humans and its underlying subcomponents (SSA/DD) are highly translatable across species. Here we summarize efforts published so far, with a focus on cortically measured SSA and DD in animals to maintain relevance to the classically measured MMN, which has cortical origins. While mechanistic studies that measure and contrast both components are sparse, we synthesize a potential set of microcircuit mechanisms from the existing rodent, primate, and human literature. While MMN and its subcomponents likely reflect several mechanisms across multiple brain regions, understanding fundamental microcircuit mechanisms is an important step to understand MMN as a whole. We hypothesize that SSA reflects adaptations occurring at synapses along the sensory-thalamocortical pathways, while DD depends on both SSA inherited from afferent inputs and resulting disinhibition of non-adapted neurons arising from the distinct physiology and wiring properties of local interneuronal subpopulations and NMDA-receptor function.

Short-term depression shapes information transmission in a constitutively active GABAergic synapse

Short-term depression is a low-pass filter of synaptic information, reducing synaptic information transfer at high presynaptic firing frequencies. Consequently, during elevated presynaptic firing, little information passes to the postsynaptic neuron. However, many neurons fire at relatively high frequencies all the time. Does depression silence their synapses? We tested this apparent contradiction in the indirect pathway of the basal ganglia. Using numerical modeling and whole-cell recordings from single entopeduncular nucleus (EP) neurons in rat brain slices, we investigated how different firing rates of globus pallidus (GP) neurons affect information transmission to the EP. Whole-cell recordings showed significant variability in steady-state depression, which decreased as stimulation frequency increased. Modeling predicted that this variability would translate into different postsynaptic noise levels during constitutive presynaptic activity. Our simulations further predicted that individual GP-EP synapses mediate gain control. However, when we consider the integration of multiple inputs, the broad range of GP firing rates would enable different modes of information transmission. Finally, we predict that changes in dopamine levels can shift the action of GP neurons from rate coding to gain modulation. Our results thus demonstrate how short-term depression shapes information transmission in the basal ganglia in particular and via GABAergic synapses in general.

Short-term dynamics of input and output of CA1 network greatly differ between the dorsal and ventral rat hippocampus

Background

The functional heterogeneity of the hippocampus along its longitudinal axis at the level of behavior is an established concept; however, the neurobiological mechanisms are still unknown. Diversifications in the functioning of intrinsic hippocampal circuitry including short-term dynamics of synaptic inputs and neuronal output, that are important determinants of information processing in the brain, may profoundly contribute to functional specializations along the hippocampus. The objectives of the present study were the examination of the role of the GABA_A receptor-mediated inhibition, the μ-opioid receptors and the effect of stimulation intensity on the dynamics of both synaptic input and neuronal output of CA1 region in the dorsal and ventral hippocampus. We used recordings of field potentials from adult rat hippocampal slices evoked by brief repetitive activation of Schaffer collaterals.

Results

We find that the local CA1 circuit of the dorsal hippocampus presents a remarkably increased dynamic range of frequency-dependent short-term changes in both input and output, ranging from strong facilitation to intense depression at low and high stimulation frequencies respectively. Furthermore, the input–output relationship in the dorsal CA1 circuit is profoundly influenced by frequency and time of presynaptic activation. Strikingly, the ventral hippocampus responds mostly with depression, displaying a rather monotonous input–output relationship over frequency and time. Partial blockade of GABA_A receptor-mediated transmission (by 5 μM picrotoxin) profoundly influences input and output dynamics in the dorsal hippocampus but affected only the neuronal output in the ventral hippocampus. M-opioid receptors control short-term dynamics of input and output in the dorsal hippocampus but they play no role in the ventral hippocampus.

Conclusion

The results demonstrate that information processing by CA1 local network is highly diversified between the dorsal and ventral hippocampus. Transient detection of incoming patterns of activity and frequency-dependent sustained signaling of amplified neuronal information may be assigned to the ventral and dorsal hippocampal circuitry respectively. This disparity should have profound implications for the functional roles ascribed to distinct segments along the long axis of the hippocampus.

Electronic supplementary material

The online version of this article (10.1186/s12868-019-0517-5) contains supplementary material, which is available to authorized users.

Translating striatal activity from brain slice to whole animal neurophysiology: A guide for neuroscience research integrating diverse levels of analysis

An important goal of this review is highlighting research in neuroscience as examples of multilevel functional and anatomical analyses addressing basic science issues and applying results to the understanding of diverse disorders. The research of Dr. Michael Levine, a leader in neuroscience, exemplifies this approach by uncovering fundamental properties of basal ganglia function and translating these findings to clinical applications. The review focuses on neurophysiological research connecting results from in vitro and in vivo recordings. A second goal is to utilize these research connections to produce novel, accurate descriptions for corticostriatal processing involved in varied, complex functions. Medium spiny neurons in striatum act as integrators combining input with baseline activity creating motivational "events." Basic research on corticostriatal synapses is described and links developed to issues with clinical relevance such as inhibitory gating, self-injurious behavior, and relative reward valuation. Work is highlighted on dopamine-glutamate interactions. Individual medium spiny neurons express both D₁ and D₂ receptors and encode information in a bivalent manner depending upon the mix of receptors involved. Current work on neurophysiology of reward processing has taken advantage of these basic approaches at the cellular and molecular levels. Future directions in studying physiology of reward processing and action sequencing could profit by incorporating the divergent ways dopamine modulates incoming neurochemical signals. Primary investigators leading research teams should mirror Mike Levine's efforts in "climbing the mountain" of scientific inquiry by performing analyses at different levels of inquiry, integrating the findings, and building comprehensive answers to problems unsolvable without this bold approach.

Short-term plasticity at cerebellar granule cell to molecular layer interneuron synapses expands information processing

Information processing by cerebellar molecular layer interneurons (MLIs) plays a crucial role in motor behavior. MLI recruitment is tightly controlled by the profile of short-term plasticity (STP) at granule cell (GC)-MLI synapses. While GCs are the most numerous neurons in the brain, STP diversity at GC-MLI synapses is poorly documented. Here, we studied how single MLIs are recruited by their distinct GC inputs during burst firing. Using slice recordings at individual GC-MLI synapses of mice, we revealed four classes of connections segregated by their STP profile. Each class differentially drives MLI recruitment. We show that GC synaptic diversity is underlain by heterogeneous expression of synapsin II, a key actor of STP and that GC terminals devoid of synapsin II are associated with slow MLI recruitment. Our study reveals that molecular, structural and functional diversity across GC terminals provides a mechanism to expand the coding range of MLIs.

Stability of working memory in continuous attractor networks under the control of short-term plasticity

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.

The ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby “pinning down” memorized positions. Here, we present a general theory that accurately predicts the extent of this “pinning down” by short-term plasticity in a broad class of biologically plausible network models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a novel theoretical link from the microscopic substrate of working memory—neurons and synaptic connections—to observable behavioral correlates, for example the susceptibility to distracting stimuli.

Long-range inhibitory intersection of a retrosplenial thalamocortical circuit by apical tuft-targeting CA1 neurons

Hippocampus, granular retrosplenial cortex (RSCg), and anterior thalamic nuclei (ATN) interact to mediate diverse cognitive functions. To identify cellular mechanisms underlying hippocampo-thalamo-retrosplenial interactions, we investigated the potential circuit suggested by projections to RSCg layer 1 (L1) from GABAergic CA1 neurons and ATN. We find that CA1→RSCg projections stem from GABAergic neurons with a distinct morphology, electrophysiology, and molecular profile. Their long-range axons inhibit L5 pyramidal neurons in RSCg via potent synapses onto apical tuft dendrites in L1. These inhibitory inputs intercept L1-targeting thalamocortical excitatory inputs from ATN to coregulate RSCg activity. Subicular axons, in contrast, excite proximal dendrites in deeper layers. Short-term plasticity differs at each connection. Chemogenetically abrogating CA1→RSCg or ATN→RSCg connections oppositely affects the encoding of contextual fear memory. Our findings establish retrosplenial-projecting CA1 neurons as a distinct class of long-range dendrite-targeting GABAergic neuron and delineate an unusual cortical circuit specialized for integrating long-range inhibition and thalamocortical excitation.

Two central pattern generators from the crab, Cancer borealis, respond robustly and differentially to extreme extracellular pH

The activity of neuronal circuits depends on the properties of the constituent neurons and their underlying synaptic and intrinsic currents. We describe the effects of extreme changes in extracellular pH – from pH 5.5 to 10.4 – on two central pattern generating networks, the stomatogastric and cardiac ganglia of the crab, Cancer borealis. Given that the physiological properties of ion channels are known to be sensitive to pH within the range tested, it is surprising that these rhythms generally remained robust from pH 6.1 to pH 8.8. The pH sensitivity of these rhythms was highly variable between animals and, unexpectedly, between ganglia. Animal-to-animal variability was likely a consequence of similar network performance arising from variable sets of underlying conductances. Together, these results illustrate the potential difficulty in generalizing the effects of environmental perturbation across circuits, even within the same animal.

Short-Term Synaptic Plasticity as a Mechanism for Sensory Timing

The ability to detect time intervals and temporal patterns is critical to some of the most fundamental computations the brain performs, including the ability to communicate and appraise a dynamically changing environment. Many of these computations take place on the scale of tens to hundreds of milliseconds. Electrophysiological evidence shows that some neurons respond selectively to duration, interval, rate, or order. Because the time constants of many time-varying neural and synaptic properties, including short-term synaptic plasticity (STP), are also in the range of tens to hundreds of milliseconds, they are strong candidates to underlie the formation of temporally selective neurons. Neurophysiological studies indicate that STP is indeed one of the mechanisms that contributes to temporal selectivity, and computational models demonstrate that neurons embedded in local microcircuits exhibit temporal selectivity if their synapses undergo STP. Converging evidence suggests that some forms of temporal selectivity emerge from the dynamic changes in the balance of excitation and inhibition imposed by STP.

Auditory midbrain coding of statistical learning that results from discontinuous sensory stimulation

Detecting regular patterns in the environment, a process known as statistical learning, is essential for survival. Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short (seconds to minutes) temporal windows. Here, we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously, in a temporally sparse manner, across windows of minutes to hours. Using a combination of behavioral, electrophysiological, and molecular approaches, we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding. Analysis of population activity (structural tuning) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies. The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination. Gain modulation was accompanied by changes in gene and protein expression, indicative of long-term plasticity. Physiological changes were largely independent of corticofugal feedback, and no changes were seen in upstream cochlear nucleus responses, suggesting a key role of the auditory midbrain in sensory gating. Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning. Using longer timescales than previously explored, the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns, a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through.

Some things are learned simply because they are there and not because they are relevant at that moment in time. This is particularly true of surrounding sounds, which we process automatically and continuously, detecting their repetitive patterns or singularities. Learning about rewards and punishment is typically attributed to cortical structures in the brain and known to occur over long time windows. Learning of surrounding regularities, on the other hand, is attributed to subcortical structures and has been shown to occur in seconds. The brain can, however, also detect the regularity in sounds that are discontinuously repeated across intervals of minutes and hours. For example, we learn to identify people by the sound of their steps through an unconscious process involving repeated but isolated exposures to the coappearance of sound and person. Here, we show that a subcortical structure, the auditory midbrain, can code such temporally spread regularities. Neurons in the auditory midbrain changed their response pattern in mice that heard a fixed tone whenever they went into one room in the environment they lived in. Learning of temporally spread sound patterns can, therefore, occur in subcortical structures.

Effect of Neuromodulation of Short-term Plasticity on Information Processing in Hippocampal Interneuron Synapses

Neurons in a micro-circuit connected by chemical synapses can have their connectivity affected by the prior activity of the cells. The number of synapses available for releasing neurotransmitter can be decreased by repetitive activation through depletion of readily releasable neurotransmitter (NT), or increased through facilitation, where the probability of release of NT is increased by prior activation. These competing effects can create a complicated and subtle range of time-dependent connectivity. Here we investigate the probabilistic properties of facilitation and depression (FD) for a presynaptic neuron that is receiving a Poisson spike train of input. We use a model of FD that is parameterized with experimental data from a hippocampal basket cell and pyramidal cell connection, for fixed frequency input spikes at frequencies in the range of theta (3-8 Hz) and gamma (20-100 Hz) oscillations. Hence our results will apply to micro-circuits in the hippocampus that are responsible for the interaction of theta and gamma rhythms associated with learning and memory. A control situation is compared with one in which a pharmaceutical neuromodulator (muscarine) is employed. We apply standard information-theoretic measures such as entropy and mutual information, and find a closed form approximate expression for the probability distribution of release probability. We also use techniques that measure the dependence of the response on the exact history of stimulation the synapse has received, which uncovers some unexpected differences between control and muscarine-added cases.

Synaptic Computation Enabled by Joule Heating of Single-Layered Semiconductors for Sound Localization

Synaptic computation, which is vital for information processing and decision making in neural networks, has remained technically challenging to be demonstrated without using numerous transistors and capacitors, though significant efforts have been made to emulate the biological synaptic transmission such as short-term and long-term plasticity and memory. Here, we report synaptic computation based on Joule heating and versatile doping induced metal-insulator transition in a scalable monolayer-molybdenum disulfide (MoS₂) device with a biologically comparable energy consumption (∼10 fJ). A circuit with our tunable excitatory and inhibitory synaptic devices demonstrates a key function for realizing the most precise temporal computation in the human brain, sound localization: detecting an interaural time difference by suppressing sound intensity- or frequency-dependent synaptic connectivity. This Letter opens a way to implement synaptic computing in neuromorphic applications, overcoming the limitation of scalability and power consumption in conventional CMOS-based neuromorphic devices.

Control of Excitation/Inhibition Balance in a Hippocampal Circuit by Calcium Sensor Protein Regulation of Presynaptic Calcium Channels

Activity-dependent regulation controls the balance of synaptic excitation to inhibition in neural circuits, and disruption of this regulation impairs learning and memory and causes many neurological disorders. The molecular mechanisms underlying short-term synaptic plasticity are incompletely understood, and their role in inhibitory synapses remains uncertain. Here we show that regulation of voltage-gated calcium (Ca²⁺) channel type 2.1 (Ca_V2.1) by neuronal Ca²⁺ sensor (CaS) proteins controls synaptic plasticity and excitation/inhibition balance in a hippocampal circuit. Prevention of CaS protein regulation by introducing the IM-AA mutation in Ca_V2.1 channels in male and female mice impairs short-term synaptic facilitation at excitatory synapses of CA3 pyramidal neurons onto parvalbumin (PV)-expressing basket cells. In sharp contrast, the IM-AA mutation abolishes rapid synaptic depression in the inhibitory synapses of PV basket cells onto CA1 pyramidal neurons. These results show that CaS protein regulation of facilitation and inactivation of Ca_V2.1 channels controls the direction of short-term plasticity at these two synapses. Deletion of the CaS protein CaBP1/caldendrin also blocks rapid depression at PV-CA1 synapses, implicating its upregulation of inactivation of Ca_V2.1 channels in control of short-term synaptic plasticity at this inhibitory synapse. Studies of local-circuit function revealed reduced inhibition of CA1 pyramidal neurons by the disynaptic pathway from CA3 pyramidal cells via PV basket cells and greatly increased excitation/inhibition ratio of the direct excitatory input versus indirect inhibitory input from CA3 pyramidal neurons to CA1 pyramidal neurons. This striking defect in local-circuit function may contribute to the dramatic impairment of spatial learning and memory in IM-AA mice.SIGNIFICANCE STATEMENT Many forms of short-term synaptic plasticity in neuronal circuits rely on regulation of presynaptic voltage-gated Ca²⁺ (Ca_V) channels. Regulation of Ca_V2.1 channels by neuronal calcium sensor (CaS) proteins controls short-term synaptic plasticity. Here we demonstrate a direct link between regulation of Ca_V2.1 channels and short-term synaptic plasticity in native hippocampal excitatory and inhibitory synapses. We also identify CaBP1/caldendrin as the calcium sensor interacting with Ca_V2.1 channels to mediate rapid synaptic depression in the inhibitory hippocampal synapses of parvalbumin-expressing basket cells to CA1 pyramidal cells. Disruption of this regulation causes altered short-term plasticity and impaired balance of hippocampal excitatory to inhibitory circuits.

Phase-locking and bistability in neuronal networks with synaptic depression

We consider a recurrent network of two oscillatory neurons that are coupled with inhibitory synapses. We use the phase response curves of the neurons and the properties of short-term synaptic depression to define Poincaré maps for the activity of the network. The fixed points of these maps correspond to phase-locked modes of the network. Using these maps, we analyze the conditions that allow short-term synaptic depression to lead to the existence of bistable phase-locked, periodic solutions. We show that bistability arises when either the phase response curve of the neuron or the short-term depression profile changes steeply enough. The results apply to any Type I oscillator and we illustrate our findings using the Quadratic Integrate-and-Fire and Morris-Lecar neuron models.

A gradient of frequency-dependent synaptic properties along the longitudinal hippocampal axis

Background

The hippocampus is a functionally heterogeneous brain structure and specializations of the intrinsic neuronal network may crucially support the functional segregation along the longitudinal axis of the hippocampus. Short-term synaptic plasticity plays fundamental roles in information processing and may be importantly involved in diversifying the properties of local neuronal network along the hippocampus long axis. Therefore, we aimed to examine the properties of the cornu ammonis 1 (CA1) synapses along the entire dorsoventral axis of the rat hippocampus using field excitatory postsynaptic potentials from transverse rat hippocampal slices and a frequency stimulation paradigm.

Results

Applying a ten-pulse stimulus train at frequencies from 0.1 to 100 Hz to the Schaffer collaterals we found a gradually diversified pattern of frequency-dependent synaptic effects along the dorsoventral hippocampus axis. The first conditioned response was facilitated along the whole hippocampus for stimulus frequencies 10–40 Hz. However, steady-state responses or averaged responses generally ranged from maximum synaptic facilitation in the most dorsal segment of the hippocampus to maximum synaptic depression in the most ventral segment of the hippocampus. In particular, dorsal synapses facilitated for stimulus frequency up to 50 Hz while they depressed at higher frequencies (75–100 Hz). Facilitation at dorsal synapses was maximal at stimulus frequency of 20 Hz. On the contrary, the most ventral synapses showed depression regardless of the stimulus frequency, only displaying a transient facilitation at the beginning of 10–50 Hz stimulation. Importantly, the synapses in the medial hippocampus displayed a transitory behavior. Finally, as a whole the hippocampal synapses maximally facilitated at 20 Hz and increasingly depressed at 50–100 Hz.

Conclusion

The short-term synaptic dynamics change gradually along the hippocampal long axis in a frequency-dependent fashion conveying distinct properties of information processing to successive segments of the structure, thereby crucially supporting functional segregation along the dorsoventral axis of the hippocampus.

Intrinsic physiology of inhibitory neurons changes over auditory development

During auditory development, changes in membrane properties promote the ability of excitatory neurons in the brain stem to code aspects of sound, including the level and timing of a stimulus. Some of these changes coincide with hearing onset, suggesting that sound-driven neural activity produces developmental plasticity of ion channel expression. While it is known that the coding properties of excitatory neurons are modulated by inhibition in the mature system, it is unknown whether there are also developmental changes in the membrane properties of brain stem inhibitory neurons. We investigated the primary source of inhibition in the avian auditory brain stem, the superior olivary nucleus (SON). The present studies test the hypothesis that, as in excitatory neurons, the membrane properties of these inhibitory neurons change after hearing onset. We examined SON neurons at different stages of auditory development: embryonic days 14-16 (E14-E16), a time at which cochlear ganglion neurons are just beginning to respond to sound; later embryonic stages (E18-E19); and after hatching (P0-P2). We used in vitro whole cell patch electrophysiology to explore physiological changes in SON. Age-related changes were observed at the level of a single spike and in multispiking behavior. In particular, tonic behavior, measured as a neuron's ability to sustain tonic firing over a range of current steps, became more common later in development. Voltage-clamp recordings and biophysical models were employed to examine how age-related increases in ion currents enhance excitability in SON. Our findings suggest that concurrent increases in sodium and potassium currents underlie the emergence of tonic behavior. NEW & NOTEWORTHY This article is the first to examine heterogeneity of neuronal physiology in the inhibitory nucleus of the avian auditory system and demonstrate that tonic firing here emerges over development. By pairing computer simulations with physiological data, we show that increases in both sodium and potassium channels over development are necessary for the emergence of tonic firing.