A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity.

Three major types of GABAergic interneurons, parvalbumin-, somatostatin-, and vasoactive intestinal peptide-expressing (PV, SOM, VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.

Muscarinic Modulation of Synaptic Transmission and Short-Term Plasticity in the Dorsal and Ventral Hippocampus.

Muscarinic neurotransmission is fundamentally involved in supporting several brain functions by modulating flow of information in brain neural circuits including the hippocampus which displays a remarkable functional segregation along its longitudinal axis. However, how muscarinic neuromodulation contributes to the functional segregation along the hippocampus remains unclear. In this study we show that the nonselective muscarinic receptor agonist carbachol similarly suppresses basal synaptic transmission in the dorsal and ventral CA1 hippocampal field, in a concentration-depended manner. Furthermore, using a ten-pulse stimulation train of varying frequency we found that carbachol changes the frequency filtering properties more in ventral than dorsal hippocampus by facilitating synaptic inputs at a wide range of input frequencies in the ventral compared with dorsal hippocampus. Using the M2 receptor antagonist gallamine and the M4 receptor antagonist tropicamide, we found that M2 receptors are involved in controlling basal synaptic transmission and short-term synaptic plasticity (STSP) in the ventral but not the dorsal hippocampus, while M4 receptors participate in modulating basal synaptic transmission and STSP in both segments of the hippocampus. These results were corroborated by the higher protein expression levels of M2 receptors in the ventral compared with dorsal hippocampus. We conclude that muscarinic transmission modulates excitatory synaptic transmission and short-term synaptic plasticity along the entire rat hippocampus by acting through M4 receptors and recruiting M2 receptors only in the ventral hippocampus. Furthermore, M4 receptors appear to exert a permissive role on the actions of M2 receptors on STSP in the ventral hippocampus. This dorsoventral differentiation of muscarinic modulation is expected to have important implications in information processing along the endogenous hippocampal circuitry.

Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus.

OBJECTIVE: Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies. MATERIALS AND METHODS: Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. RESULTS: Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. CONCLUSIONS: The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Region-Related Differences in Short-Term Synaptic Plasticity and Synaptotagmin-7 in the Male and Female Hippocampus of a Rat Model of Fragile X Syndrome.

Fragile X syndrome (FXS) is an intellectual developmental disorder characterized, inter alia, by deficits in the short-term processing of neural information, such as sensory processing and working memory. The primary cause of FXS is the loss of fragile X messenger ribonucleoprotein (FMRP), which is profoundly involved in synaptic function and plasticity. Short-term synaptic plasticity (STSP) may play important roles in functions that are affected by FXS. Recent evidence points to the crucial involvement of the presynaptic calcium sensor synaptotagmin-7 (Syt-7) in STSP. However, how the loss of FMRP affects STSP and Syt-7 have been insufficiently studied. Furthermore, males and females are affected differently by FXS, but the underlying mechanisms remain elusive. The aim of the present study was to investigate possible changes in STSP and the expression of Syt-7 in the dorsal (DH) and ventral (VH) hippocampus of adult males and females in a Fmr1-knockout (KO) rat model of FXS. We found that the paired-pulse ratio (PPR) and frequency facilitation/depression (FF/D), two forms of STSP, as well as the expression of Syt-7, are normal in adult KO males, but the PPR is increased in the ventral hippocampus of KO females (6.4 ± 3.7 vs. 18.3 ± 4.2 at 25 ms in wild type (WT) and KO, respectively). Furthermore, we found no gender-related differences, but did find robust region-dependent difference in the STSP (e.g., the PPR at 50 ms: 50.0 ± 5.5 vs. 17.6 ± 2.9 in DH and VH of WT male rats; 53.1 ± 3.6 vs. 19.3 ± 4.6 in DH and VH of WT female rats; 48.1 ± 2.3 vs. 19.1 ± 3.3 in DH and VH of KO male rats; and 51.2 ± 3.3 vs. 24.7 ± 4.3 in DH and VH of KO female rats). AMPA receptors are similarly expressed in the two hippocampal segments of the two genotypes and in both genders. Also, basal excitatory synaptic transmission is higher in males compared to females. Interestingly, we found more than a twofold higher level of Syt-7, not synaptotagmin-1, in the dorsal compared to the ventral hippocampus in the males of both genotypes (0.43 ± 0.1 vs. 0.16 ± 0.02 in DH and VH of WT male rats, and 0.6 ± 0.13 vs. 0.23 ± 0.04 in DH and VH of KO male rats) and in the WT females (0.97 ± 0.23 vs. 0.31 ± 0.09 in DH and VH). These results point to the susceptibility of the female ventral hippocampus to FMRP loss. Importantly, the different levels of Syt-7, which parallel the higher score of the dorsal vs. ventral hippocampus on synaptic facilitation, suggest that Syt-7 may play a pivotal role in defining the striking differences in STSP along the long axis of the hippocampus.

Short-Term Synaptic Plasticity: Microscopic Modelling and (Some) Computational Implications.

Synaptic transmission is transiently adjusted on a spike-by-spike basis, with the adjustments persisting from hundreds of milliseconds up to seconds. Such a short-term plasticity has been suggested to significantly augment the computational capabilities of neuronal networks by enhancing their dynamical repertoire. In this chapter, after reviewing the basic physiology of chemical synaptic transmission, we present a general framework-inspired by the quantal model-to build simple, yet quantitatively accurate models of repetitive synaptic transmission. We also discuss different methods to obtain estimates of the model's parameters from experimental recordings. Next, we show that, indeed, new dynamical regimes appear in the presence of short-term synaptic plasticity. In particular, model neuronal networks exhibit the co-existence of a stable fixed point and a stable limit cycle in the presence of short-term synaptic facilitation. It has been suggested that this dynamical regime is especially relevant in working memory processes. We provide, then, a short summary of the synaptic theory of working memory and discuss some of its specific predictions in the context of experiments. We conclude the chapter with a short outlook.

Modeling the Short-Term Dynamics of in Vivo Excitatory Spike Transmission.

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.SIGNIFICANCE STATEMENT Although synaptic dynamics have been extensively studied and modeled using intracellular recordings of postsynaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here, we developed a model that, using only observations of presynaptic and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity (STP) and nonsynaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

Differential Short-Term Plasticity of PV and SST Neurons Accounts for Adaptation and Facilitation of Cortical Neurons to Auditory Tones.

Cortical responses to sensory stimuli are strongly modulated by temporal context. One of the best studied examples of such modulation is sensory adaptation. We first show that in response to repeated tones pyramidal (Pyr) neurons in male mouse auditory cortex (A1) exhibit facilitating and stable responses, in addition to adapting responses. To examine the potential mechanisms underlying these distinct temporal profiles, we developed a reduced spiking model of sensory cortical circuits that incorporated the signature short-term synaptic plasticity (STP) profiles of the inhibitory parvalbumin (PV) and somatostatin (SST) interneurons. The model accounted for all three temporal response profiles as the result of dynamic changes in excitatory/inhibitory balance produced by STP, primarily through shifts in the relative latency of Pyr and inhibitory neurons. Transition between the three response profiles was possible by changing the strength of the inhibitory PV→Pyr and SST→Pyr synapses. The model predicted that a unit's latency would be related to its temporal profile. Consistent with this prediction, the latency of stable units was significantly shorter than that of adapting and facilitating units. Furthermore, because of the history-dependence of STP the model generated a paradoxical prediction: that inactivation of inhibitory neurons during one tone would decrease the response of A1 neurons to a subsequent tone. Indeed, we observed that optogenetic inactivation of PV neurons during one tone counterintuitively decreased the spiking of Pyr neurons to a subsequent tone 400 ms later. These results provide evidence that STP is critical to temporal context-dependent responses in the sensory cortex.SIGNIFICANCE STATEMENT Our perception of speech and music depends strongly on temporal context, i.e., the significance of a stimulus depends on the preceding stimuli. Complementary neural mechanisms are needed to sometimes ignore repetitive stimuli (e.g., the tic of a clock) or detect meaningful repetition (e.g., consecutive tones in Morse code). We modeled a neural circuit that accounts for diverse experimentally-observed response profiles in auditory cortex (A1) neurons, based on known forms of short-term synaptic plasticity (STP). Whether the simulated circuit reduced, maintained, or enhanced its response to repeated tones depended on the relative dominance of two different types of inhibitory cells. The model made novel predictions that were experimentally validated. Results define an important role for STP in temporal context-dependent perception.

Functional Postnatal Maturation of the Medial Olivocochlear Efferent-Outer Hair Cell Synapse.

The organ of Corti, the auditory mammalian sensory epithelium, contains two types of mechanotransducer cells, inner hair cells (IHCs) and outer hair cells (OHCs). IHCs are involved in conveying acoustic stimuli to the CNS, while OHCs are implicated in the fine tuning and amplification of sounds. OHCs are innervated by medial olivocochlear (MOC) cholinergic efferent fibers. The functional characteristics of the MOC-OHC synapse during maturation were assessed by electrophysiological and pharmacological methods in mouse organs of Corti at postnatal day 11 (P11)-P13, hearing onset in altricial rodents, and at P20-P22 when the OHCs are morphologically and functionally mature. Synaptic currents were recorded in whole-cell voltage-clamped OHCs while electrically stimulating the MOC fibers. A progressive increase in the number of functional MOC-OHC synapses, as well as in their strength and efficacy, was observed between P11-13 and P20-22. At hearing onset, the MOC-OHC synapse presented facilitation during MOC fibers high-frequency stimulation that disappeared at mature stages. In addition, important changes were found in the VGCC that are coupled to transmitter release. Ca2+ flowing in through L-type VGCCs contribute to trigger ACh release together with P/Q- and R-type VGCCs at P11-P13, but not at P20-P22. Interestingly, N-type VGCCs were found to be involved in this process at P20-P22, but not at hearing onset. Moreover, the degree of compartmentalization of calcium channels with respect to BK channels and presynaptic release components significantly increased from P11-P13 to P20-P22. These results suggest that the MOC-OHC synapse is immature at the onset of hearing.SIGNIFICANCE STATEMENT The functional expression of both VGCCs and BK channels, as well as their localization with respect to the presynaptic components involved in transmitter release, are key elements in determining synaptic efficacy. In this work, we show dynamic changes in the expression of VGCCs and Ca2+-dependent BK K+ channels coupled to ACh release at the MOC-OHC synapse and their shift in compartmentalization during postnatal maturation. These processes most likely set the short-term plasticity pattern and reliability of the MOC-OHC synapse on high-frequency activity.

Presenting a Neuroid model of wind-up based on dynamic synapse.

The treatment of chronic pain depends mainly on our understanding of the mechanisms such as central sensitization which is involved in it. Wind-up of spinal cord is one of the most important phenomena in the study of central sensitization which has received considerable attention in recent years. Wind-up is a form of short-term synaptic plasticity (STP) that can lead to central sensitivity. Although several models have been proposed for wind-up, none of them are based on the experimental evidence. In this study, a new network model is introduced according to the gate control theory of pain. Neuroids are used as neuron models in which their parameters are captured from available experimental data. Adjusting the weights of the network is based on the short-term synaptic plasticity. The results of the time and frequency domain show that the model can well simulate wind-up behavior. This model can be used for analyzing, predicting and controlling chronic pain in the future.

Time-coded neurotransmitter release at excitatory and inhibitory synapses.

Communication between neurons at chemical synapses is regulated by hundreds of different proteins that control the release of neurotransmitter that is packaged in vesicles, transported to an active zone, and released when an input spike occurs. Neurotransmitter can also be released asynchronously, that is, after a delay following the spike, or spontaneously in the absence of a stimulus. The mechanisms underlying asynchronous and spontaneous neurotransmitter release remain elusive. Here, we describe a model of the exocytotic cycle of vesicles at excitatory and inhibitory synapses that accounts for all modes of vesicle release as well as short-term synaptic plasticity (STSP). For asynchronous release, the model predicts a delayed inertial protein unbinding associated with the SNARE complex assembly immediately after vesicle priming. Experiments are proposed to test the model's molecular predictions for differential exocytosis. The simplicity of the model will also facilitate large-scale simulations of neural circuits.

Layer-Dependent Short-Term Synaptic Plasticity Between Excitatory Neurons in the C2 Barrel Column of Mouse Primary Somatosensory Cortex.

Neurons process information through spatiotemporal integration of synaptic input. Synaptic transmission between any given pair of neurons is typically a dynamic process with presynaptic action potentials (APs) evoking depressing or facilitating postsynaptic potentials when presynaptic APs occur within hundreds of milliseconds of each other. In order to understand neocortical function, it is therefore important to investigate such short-term synaptic plasticity at synapses between different types of neocortical neurons. Here, we examine short-term synaptic dynamics between excitatory neurons in different layers of the mouse C2 barrel column through in vitro whole-cell recordings. We find layer-dependent short-term plasticity, with depression being dominant at many synaptic connections. Interestingly, however, presynaptic layer 2 neurons predominantly give rise to facilitating excitatory synaptic output at short interspike intervals of 10 and 30 ms. Previous studies have found prominent burst firing of excitatory neurons in supragranular layers of awake mice. The facilitation we observed in the synaptic output of layer 2 may, therefore, be functionally relevant, possibly serving to enhance the postsynaptic impact of burst firing.

Models of Short-Term Synaptic Plasticity.

We focus on dynamical descriptions of short-term synaptic plasticity. Instead of focusing on the molecular machinery that has been reviewed recently by several authors, we concentrate on the dynamics and functional significance of synaptic plasticity, and review some mathematical models that reproduce different properties of the dynamics of short term synaptic plasticity that have been observed experimentally. The complexity and shortcomings of these models point to the need of simple, yet physiologically meaningful models. We propose a simplified model to be tested in synapses displaying different types of short-term plasticity.

Short-term depression, temporal summation, and onset inhibition shape interval tuning in midbrain neurons.

A variety of synaptic mechanisms can contribute to single-neuron selectivity for temporal intervals in sensory stimuli. However, it remains unknown how these mechanisms interact to establish single-neuron sensitivity to temporal patterns of sensory stimulation in vivo. Here we address this question in a circuit that allows us to control the precise temporal patterns of synaptic input to interval-tuned neurons in behaviorally relevant ways. We obtained in vivo intracellular recordings under multiple levels of current clamp from midbrain neurons in the mormyrid weakly electric fish Brienomyrus brachyistius during stimulation with electrosensory pulse trains. To reveal the excitatory and inhibitory inputs onto interval-tuned neurons, we then estimated the synaptic conductances underlying responses. We found short-term depression in excitatory and inhibitory pathways onto all interval-tuned neurons. Short-interval selectivity was associated with excitation that depressed less than inhibition at short intervals, as well as temporally summating excitation. Long-interval selectivity was associated with long-lasting onset inhibition. We investigated tuning after separately nullifying the contributions of temporal summation and depression, and found the greatest diversity of interval selectivity among neurons when both mechanisms were at play. Furthermore, eliminating the effects of depression decreased sensitivity to directional changes in interval. These findings demonstrate that variation in depression and summation of excitation and inhibition helps to establish tuning to behaviorally relevant intervals in communication signals, and that depression contributes to neural coding of interval sequences. This work reveals for the first time how the interplay between short-term plasticity and temporal summation mediates the decoding of temporal sequences in awake, behaving animals.

Dynamic representation of spectral edges in guinea pig primary auditory cortex.

The central representation of a given acoustic motif is thought to be strongly context dependent, i.e., to rely on the spectrotemporal past and present of the acoustic mixture in which it is embedded. The present study investigated the cortical representation of spectral edges (i.e., where stimulus energy changes abruptly over frequency) and its dependence on stimulus duration and depth of the spectral contrast in guinea pig. We devised a stimulus ensemble composed of random tone pips with or without an attenuated frequency band (AFB) of variable depth. Additionally, the multitone ensemble with AFB was interleaved with periods of silence or with multitone ensembles without AFB. We have shown that the representation of the frequencies near but outside the AFB is greatly enhanced, whereas the representation of frequencies near and inside the AFB is strongly suppressed. These cortical changes depend on the depth of the AFB: although they are maximal for the largest depth of the AFB, they are also statistically significant for depths as small as 10 dB. Finally, the cortical changes are quick, occurring within a few seconds of stimulus ensemble presentation with AFB, and are very labile, disappearing within a few seconds after the presentation without AFB. Overall, this study demonstrates that the representation of spectral edges is dynamically enhanced in the auditory centers. These central changes may have important functional implications, particularly in noisy environments where they could contribute to preserving the central representation of spectral edges.

A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity.

Determining the order of sensory events separated by a few hundred milliseconds is critical to many forms of sensory processing, including vocalization and speech discrimination. Although many experimental studies have recorded from auditory order-sensitive and order-selective neurons, the underlying mechanisms are poorly understood. Here we demonstrate that universal properties of cortical synapses-short-term synaptic plasticity of excitatory and inhibitory synapses-are well suited for the generation of order-selective neural responses. Using computational models of canonical disynaptic circuits, we show that the dynamic changes in the balance of excitation and inhibition imposed by short-term plasticity lead to the generation of order-selective responses. Parametric analyses predict that among the forms of short-term plasticity expressed at excitatory-to-excitatory, excitatory-to-inhibitory, and inhibitory-to-excitatory synapses, the single most important contributor to order-selectivity is the paired-pulse depression of inhibitory postsynaptic potentials (IPSPs). A topographic model of the auditory cortex that incorporates short-term plasticity accounts for both context-dependent suppression and enhancement in response to paired tones. Together these results provide a framework to account for an important computational problem based on ubiquitous synaptic properties that did not yet have a clearly established computational function. Additionally, these studies suggest that disynaptic circuits represent a fundamental computational unit that is capable of processing both spatial and temporal information.

New insights into short-term synaptic facilitation at the frog neuromuscular junction.

Short-term synaptic facilitation occurs during high-frequency stimulation, is known to be dependent on presynaptic calcium ions, and persists for tens of milliseconds after a presynaptic action potential. We have used the frog neuromuscular junction as a model synapse for both experimental and computer simulation studies aimed at testing various mechanistic hypotheses proposed to underlie short-term synaptic facilitation. Building off our recently reported excess-calcium-binding-site model of synaptic vesicle release at the frog neuromuscular junction (Dittrich M, Pattillo JM, King JD, Cho S, Stiles JR, Meriney SD. Biophys J 104: 2751-2763, 2013), we have investigated several mechanisms of short-term facilitation at the frog neuromuscular junction. Our studies place constraints on previously proposed facilitation mechanisms and conclude that the presence of a second class of calcium sensor proteins distinct from synaptotagmin can explain known properties of facilitation observed at the frog neuromuscular junction. We were further able to identify a novel facilitation mechanism, which relied on the persistent binding of calcium-bound synaptotagmin molecules to lipids of the presynaptic membrane. In a real physiological context, both mechanisms identified in our study (and perhaps others) may act simultaneously to cause the experimentally observed facilitation. In summary, using a combination of computer simulations and physiological recordings, we have developed a stochastic computer model of synaptic transmission at the frog neuromuscular junction, which sheds light on the facilitation mechanisms in this model synapse.

Differential cortical activation of the striatal direct and indirect pathway cells: reconciling the anatomical and optogenetic results by using a computational method.

The corticostriatal system is considered to be crucially involved in learning and action selection. Anatomical studies have shown that two types of corticostriatal neurons, intratelencephalic (IT) and pyramidal tract (PT) cells, preferentially project to dopamine D1 or D2 receptor-expressing striatal projection neurons, respectively. In contrast, an optogenetic study has shown that stimulation of IT axons evokes comparable responses in D1 and D2 cells and that stimulation of PT axons evokes larger responses in D1 cells. Since the optogenetic study applied brief stimulation only, however, the overall impacts of repetitive inputs remain unclear. Moreover, the apparent contradiction between the anatomical and optogenetic results remains to be resolved. I addressed these issues by using a computational approach. Specifically, I constructed a model of striatal response to cortical inputs, with parameters regarding short-term synaptic plasticity and anatomical connection strength for each connection type. Under the constraint of the optogenetic results, I then explored the parameters that best explain the previously reported paired-pulse ratio of response in D1 and D2 cells to cortical and intrastriatal stimulations, which presumably recruit different compositions of IT and PT fibers. The results indicate that 1) IT→D1 and PT→D2 connections are anatomically stronger than IT→D2 and PT→D1 connections, respectively, consistent with the previous findings, and that 2) IT→D1 and PT→D2 synapses entail short-term facilitation, whereas IT→D2 and PT→D1 synapses would basically show depression, and thereby 3) repetitive IT or PT inputs have larger overall impacts on D1 or D2 cells, respectively, supporting a recently proposed hypothesis on the roles of corticostriatal circuits in reinforcement learning.

Thalamic synaptic transmission of sensory information modulated by synergistic interaction of adenosine and serotonin.

The thalamic synapses relay peripheral sensory information to the cortex, and constitute an important part of the thalamocortical network that generates oscillatory activities responsible for different vigilance (sleep and wakefulness) states. However, the modulation of thalamic synaptic transmission by potential sleep regulators, especially by combination of regulators in physiological scenarios, is not fully characterized. We found that somnogen adenosine itself acts similar to wake-promoting serotonin, both decreasing synaptic strength as well as short-term depression, at the retinothalamic synapse. We then combined the two modulators considering the coexistence of them in the hypnagogic (sleep-onset) state. Adenosine plus serotonin results in robust synergistic inhibition of synaptic strength and dramatic transformation of short-term synaptic depression to facilitation. These synaptic effects are not achievable with a single modulator, and are consistent with a high signal-to-noise ratio but a low level of signal transmission through the thalamus appropriate for slow-wave sleep. This study for the first time demonstrates that the sleep-regulatory modulators may work differently when present in combination than present singly in terms of shaping information flow in the thalamocortical network. The major synaptic characters such as the strength and short-term plasticity can be profoundly altered by combination of modulators based on physiological considerations.

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks.

Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot's control unit, i.e., as a cyborg's brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.