Networks Of Inhibitory Neurons Research Articles

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Assessment and modulation of cortical inhibition using transcranial magnetic stimulation

Abstract:Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique, which is used for diagnostic, therapeutic and scientific purposes in the field of neurology and psychiatry. It is based on the physical principle of electromagnetic induction and allows for the local activation of cortical areas through the intact skull of conscious humans. When applied repeatedly (repetitive TMS; rTMS) sustained changes of cortical excitability can be observed. Hence, TMS resembles a promising approach for assessing and modulating neuronal networks in a non-invasive manner. However, despite its broad clinical application, the cellular and molecular mechanisms of rTMS-based therapies remain not well understood. Established therapeutic concepts assume that pathologically altered cortical excitability is normalised, which may involve ‘long-term potentiation’ or ‘long-term depression’ of excitatory synapses. Indeed, animal studies demonstrate that rTMS induces long-term changes of excitatory neurotransmission. However, it is unclear through which mechanisms synaptic changes, which are caused by external electromagnetic activation of the cortex and therefore are not specific for context or behaviour, could have a positive impact on complex brain function. More recent findings suggest that not only excitatory but also inhibitory neuronal networks are modulated by rTMS. It was shown for example that 10 Hz rTMS leads to a calcium-dependent long-term depression of inhibitory GABAergic synapses. Since the reduction of inhibitory neurotransmission (= disinhibition) is considered important for the expression of associative plasticity at excitatory synapses, it is conceivable that rTMS-induced disinhibition may promote context- and behaviour-specific synaptic changes. Hence, the model of

Normalized Neural Representations of Complex Odors.

The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of the processing in the olfactory bulb, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Comparing these predictions to experiments will help us to understand the role of global inhibition in shaping normalized odor representations in the olfactory bulb.

Formation of GABA-Aα1 and GABA-B1 receptor-mediated inhibitory network in the ventrolateral part of the solitary tract nucleus during the early postnatal period under normal conditions and prenatal serotonin deficiency

Time course of formation of the inhibitory receptor network (GABA-Aα1 and GABA-B1) in the respiratory subnuclei (ventral and lateral) of the solitary tract (NTS) during the early postnatal period was studied on laboratory Wistar rats under normal conditions and prenatal serotonin deficiency. It was found that in normal rats the maturation of the inhibitory receptor networks in both NTS subnuclei occurs within the first three postnatal weeks. Some features of their formation were noticed. The dynamics of changes in intensity of GABA-Аα1 expression in the ventral and lateral subnuclei proceeds in a similar way. During the first postnatal week, GABA-Aα1 expression is low. In the neuropil, the network of GABA-Aα1-containing presynaptic terminals and synapses is poorly developed. Within the second week, the number of GABA-Aα1-expressing neurons increases in both subnuclei with a simultaneous rise in the density of the network of terminals and synaptic structures. By the end of the third week, the number of GABA-Aα1-expressing neurons decreases, but the network density continues to increase. GABA-B1 expression in the ventral and lateral subnuclei occurs also simultaneously, although with some distinctions. During the first postnatal week, intensity of GABA-B1 expression is weak. In the neuropil, few GABA-B1-containing terminals form a loose network with sporadic synaptic structures. During the second week, expression of the receptor increases, being particularly considerable in the ventral subnuclei. Simultaneously, the density of the presynaptic terminals increases. By the end of the third week, the number of GABA-B1-expressing neurons in the ventral subnuclei decreases, while in the lateral subnuclei remains almost intact and the network density increases. The data obtained show that prenatal serotonin deficiency leads to malformation and impaired maturation of the GABAergic inhibitory neuronal network in the ventrolateral part of NTS.

Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons.

The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state.

A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Stochastic modeling of spontaneous bursting activity to simulate neural responses of in-vitro networks on multielectrode arrays.

The study of neuronal bursting activity, observed in cell-culture, is physiologically important because is correlated with synaptic transmission, plasticity, and information processing. However, besides strong ethical issues related to the use of animal models, there are many limitations due to experimental setup and neural signaling acquisition. In this study, we propose a stochastic modeling approach to simulate neural dynamics observed in networks of neocortical neurons from an ex vivo normal mouse. Specifically, we devised a stochastic version of the Izhichevich's model of cortical neurons, and simulated a network of excitatory and inhibitory neurons also accounting for cell signaling delays. No specific learning rules were used throughout the simulation time. Results show spontaneous bursting activity while mimicking balanced and hyper-excitable networks through modulation of the inhibitory synaptic weights. Furthermore, we validate our findings comparing the simulated cumulative probability functions of the firing spike histograms with the ones obtained in cultured networks of dissociated cortical neurons from ex vivo mice.

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ...

The Inhibitory Neuronal Circuit of Spinal Cord in Neuropathic Pain

Aim of review: Inhibitory interneurons, including GABAergic neurons and glycine neurons, regulate nociceptive information and non-nociceptive information in spinal dorsal horn. Emerging evidence showed that disinhibition of inhibitory interneurons of neuronal circuit in spinal dorsal horn is a pivotal mechanism of neuropathic pain after nerve injury.Method: In this view, we summarized the recent researches of the structure of inhibitory neurons in spinal dorsal horn and disinhibition of inhibitory interneurons after nerve injury and discussed the primary mechanism.Recent findings: Much progress has been made with the construction of inhibitory neuronal network in spinal dorsal horn and the dysfunction of inhibitory interneurons in these networks since inhibitory interneurons in spinal dorsal horn firstly integrate nociceptive information and non-nociceptive information from primary afferent fiber and separate non-nociceptive stimuli from nociceptive information. Disinhibitory of inhibitory interneurons underlies hyperalgesia and allodynia after nerve injury.Summary: Loss of inhibitory function of neurons in inhibitory network in the dorsal horn contributes to hyperalgesia and allodynia. The findings of these inhibitory networks provide a new evidence for preventing and curing neuropathic pain. Citation: Hui-Qun Fu, Tian-Long Wang. The inhibitory neuronal circuit of spinal cord in neuropathic pain. J Anesth Perioper Med 2016; 3: 132-141. doi: 10.24015/JAPM.2016.0018This is an open-access article, published by Evidence Based Communications (EBC). This work is licensed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium or format for any lawful purpose. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity.

Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells or human inducible pluripotent stem cells that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid to FOXG1(+) neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuronal cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent on N-methyl-D-aspartate (NMDA) receptors, nitric oxide (NO), and poly(ADP-ribose) polymerase (PARP) (a cell death pathway called parthanatos that is distinct from apoptosis, necroptosis, and other forms of cell death). Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons.

Editorial: Limbic Encephalitis: Autoimmune Impact on Inhibitory GABAergic Neuronal Pathways in Temporal Lobe Epilepsy.

The Editorial on the Research Topic Limbic Encephalitis: Autoimmune Impact on Inhibitory GABAergic Neuronal Pathways in Temporal Lobe Epilepsy Mesial temporal lobe epilepsy (mTLE), a common adult epilepsy syndrome, is generally acquired. Recent data have demonstrated autoimmune inflammation predominantly affecting the limbic structures of the brain as a cause of adult mTLE development (1–3). Patients usually present with mesial temporal lobe seizures with interictal temporal epileptiform activity and slowing, episodic memory disturbance, and a variety of other behavioral, emotional, and cognitive changes. Magnetic resonance imaging typically exhibits volume and signal changes of the amygdala and hippocampus, and specific anti-neuronal antibodies binding to either intracellular or plasma membrane neuronal antigens can be detected in sera and cerebrospinal fluid. Glutamic acid decarboxylase (GAD) (4) and γ-aminobutyric acid (GABA)-B receptors (5) have recently been identified as distinct neuronal antigens in limbic encephalitis. This suggests a possible involvement of disturbed inhibitory GABAergic signaling in the etiology of seizures and neuropsychiatric symptoms characterizing this form of autoimmune epilepsy. Due to restricted access to their target antigen, autoantibodies directed toward intracellular GAD are believed to bear limited pathogenic potential, and neurons may be affected by autoreactive T cells instead (1). In contrast, autoantibodies against plasma membrane GABA-B receptors have been suggested to exert direct pathogenic effects (6), and the role of autoreactive T cells in these disorders is unclear at present (1). In the Frontiers in Neurology Research Topic “Limbic Encephalitis: Autoimmune Impact on Inhibitory GABAergic Neuronal Pathways in Temporal Lobe Epilepsy,” authors provide a survey on clinical and scientific aspects of autoimmune inflammation in mTLE. Ehling et al. review and discuss the role of CD8+ T cells–neuron interactions, whereas Seebohm et al. summarize the current knowledge and discuss technical approaches to study the autoantibody–receptor interactions in limbic encephalitis. Haselmann et al. present a methodical approach to investigate the impact of stereotactically injected human IgG fractions on GABAergic signaling in intact murine hippocampal network ex vivo. Stemmler et al. could not detect any effect of serum of a patient with confirmed GAD antibody-associated limbic encephalitis on GABAergic neurotransmission in murine cultured hippocampal networks, challenging the view that the presence of such autoantibodies compromise inhibitory network function. Widman et al. report a correlation of the CD8+ T cells in the cerebrospinal fluid with clinical and paraclinical measures of disease activity together with an unambiguous response to treatment with basiliximab, a chimeric mouse–human monoclonal antibody to the α-chain of the interleukin-2 receptor (CD25) on T cells in a patient with GAD antibody-associated limbic encephalitis. This strongly argues in favor of a pathogenic role of CD8+ T cells in this form of autoimmune temporal lobe epilepsy. The amygdala is central for the generation of adequate homoeostatic behavioral responses to emotionally significant external stimuli following processing in a variety of parallel neuronal circuits. Melzer et al. hypothesize that adaptive cellular and humoral autoimmunity may target and modulate distinct inhibitory or excitatory neuronal networks within the amygdala and thereby strongly impact processing of emotional stimuli and corresponding behavioral responses in patients with limbic encephalitis. Indeed, Schroder et al. show defective modulation of sympathetic ­autonomic responses during emotional stimulation in limbic encephalitis probably due to impaired functioning of the amygdala. Witt et al. present a case of GAD antibody-associated limbic encephalitis with predominant involvement of the left amygdala that presented with retrograde episodic memory impairment, characterized by loss of emotional attachment and autonoetic awareness of usually highly emotional autobiographical memories consistent with the known role of the amygdala as part of the basolateral limbic circuit relevant for emotional valence of memories. Furthermore, a special form of anterograde episodic memory impairment was present called accelerated long-term forgetting, consisting of accelerated loss or impaired access to newly encoded episodes. These cognitive and emotional disturbances occurred in the absence of overt epileptic seizures, suggesting a direct causal role of autoimmune inflammation within temporomesial structures.

Spinal cellular and network properties modulate pain perception

Through the example the modulation of pain perception by a steroid hormone, I’m going to show you a multiscale interaction between two systems of an organism. Indeed, we will see how a the endocrine system via a molecule circulating in the blood will modulate its target molecule in the nervous system that will, in turn, change cellular activities at one stage of the central nervous system that could alter the behavior of this organism.Indeed our body collects somatosensory information from our environment that will be transmitted to the central nervous system by the peripheral nervous system. Amongst these sensory information there are nociceptive sensory inputs. A nociceptive stimuli detected at the level of the skin will be relayed by the spinal cord to the cortex where it will be interpreted as pain. The dorsal horn of the spinal cord is not a mere relay of the primary nociceptive input. At the level of the superficial layers of the dorsal horn, the nociceptive inputs are processed by a network of excitatory and inhibitory neurons before being transmitted via an ascending pathway to the brain where it will be interpreted as pain. At that level nociceptive processing is tuned by GABAA receptor-mediated inhibition in the spinal cord dorsal horn. These GABAergic inhibitory postsynaptic currents (IPSCs) are known to be modulated by spinally reduced steroids.Corticosterone (CORT) is a glucocorticoid produced by adrenal glands under the control of the hypothalamic-pituitary-adrenal axis. Circulating CORT can enter the central nervous system and be reduced to neuroactive reduced steroids, which modulate GABAA receptors. In the dorsal spinal cord, GABAergic transmission modulates integration of nociceptive information. It has been shown that enhancing spinal inhibitory transmission alleviates hyperalgesia and allodynia. Therefore, the spinal neuronal network is a pivotal target to counteract pain symptoms. Thus, any increase of spinal reduced steroids production enhancing GABAergic inhibition should reduce nociceptive messages integration and pain response.Previously, it has been shown that high levels of plasma glucocorticoids give rise to analgesia. However to our knowledge nothing has been reported regarding a direct non genomic modulation of neuronal spinal activity by peripheral CORT. In the present study, we used combined in vivo and in vitro electrophysiology approaches, associated with the measure of nociceptive mechanical sensitivity and plasma corticosterone level measurement to assess the impact of circulating CORT on rat nociception. We showed that CORT plasma level elevation produced analgesia via the reduction of nociceptive fiber mediated spinal responses. CORT is spinally reduced in the neuroactive metabolite THDOC that specifically enhances lamina II GABAergic synaptic transmission. The main consequence is a reduction of lamina II network excitability reflecting a selective decrease in processing of nociceptive inputs. The depressed neuronal activity at the spinal level then in turn leads to a weaker nociceptive message transmission to supraspinal structures and hence to an alleviation of pain.

Optimal signal detection with neuronal diversity: balancing the gullible and the prudent neurons

Network connectivity have been shown to play an important role in shaping the neuronal dynamics [1-5]. A complementary remarkable feature of neuronal systems is the large degree of morphological and functional diversity. Despite some recent efforts in understanding the role of neuronal diversity embedded in a network [6-9], the benefits of cellular variability to distinguish input varying over orders of magnitude remain elusive. We utilize a simple quiescent-active-refractory-quiescent model, which is amenable to mathematical analysis [10], interacting in a (non-structured) random network with diversity in the parameter that controls the propensity of the neurons to fire in response to input from their neighbors. We consider a simple binomial distribution, a uniform distribution, and a more realistic gamma distribution. As depicted in Figure ​Figure1,1, we show that the capability of the network to distinguish the amount of external input can be improved by two orders of magnitude (20 dB) in the presence of diversity. We explain how diversity enhances the network capabilities, and identify the cases in which one specialized sub-population outperforms the rest of the network and the cases in which the average network outperforms any sub-population. Finally, we show the robustness of our results in a balanced cortical network of excitatory and inhibitory neurons. Figure 1 Top: Illustrative random networks with neuronal diversity in the threshold parameter θ. Bottom: Maximal dynamic range Δmax (as defined in ref. [6]) reached by networks with binomial (left), uniform (center), and gamma (right) distributions ...

Surround suppression and normalization in a model of coupled balanced cortical networks with short-term synaptic plasticity

The input currents to neurons in the cortex are under many circumstances highly variable with a mean intensity considerably below the firing threshold. As a consequence, the spiking activity of cortical neurons is strongly fluctuating such that the coefficient of variation of the inter-spike interval distribution of individual neurons is approximately equal to 1, implying almost Poisson-like spiking. In addition to this characteristic activity, the connectivity of excitatory and inhibitory neurons in cortex is sparse and irregular. It has been shown in models that super-threshold excitatory and strong inhibitory input currents, which nearly cancel for individual neurons, can lead to this irregular spiking activity [1]. However, balanced networks of excitatory and inhibitory neurons are characterized by a strictly linear relation between stimulus strength and network firing rate, making it hard to perform more complex computational tasks like the generation of receptive fields, multiple stable activity states or normalization, which for example has been measured in visual cortex (eg. [2,3]). Synapses displaying activity dependent short-term plasticity (STP) have been previously reported to give rise to a non-linear network response with potentially multiple stable states for a given stimulus [4]. In this study, we analyze analytically and numerically the computational properties of two interconnected balanced networks, receiving independent stationary stimuli. This situation can be viewed as a simple instantiation of two interconnected cortical columns. For an illustration of the network topology, see Figure ​Figure1A.1A. We demonstrate that these stimuli are normalized by the system and that increasing the stimulus to one network, suppresses the activity of the neighboring network (see Figure ​Figure1B).1B). Thereby, normalization and suppression are linear in stimulus strength when STP is disabled and becomes non-linear with activity dependent synapses. Figure 1 A. Schematic illustration of the network topology. E1 and I1 represent excitatory and inhibitory neuronal populations of network 1, receiving input from an external population X1. Same for network 2. Coupling of neuronal populations is indicated by the ... References van Vreeswijk C, Sompolinsky H. Chaotic Balanced State in a Model of Cortical Circuits. Neural Comput. 1998;10(6):1321–1371. [PubMed] Carandini M, Heeger DJ, Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci. 1997;17(21):8621–8644. [PubMed] Levitt JB, Kiper DC, Movshon JA. Receptive fields and functional architecture of macaque V2. J Neurophysiol. 1994;71(6):2517–2542. [PubMed] Mongillo G, Hansel D, van Vreeswijk C. Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Phys Rev Lett. 2012;108(15):158101. [PubMed] Articles from BMC Neuroscience are provided here courtesy of BioMed Central Formats: Article | PubReader | ePub (beta) | PDF (246K) | Citation Share Facebook Twitter Google+ You are here: NCBI > Literature > PubMed Central (PMC) Write to the Help Desk External link. Please review our privacy policy. NLM NIH DHHS USA.gov Copyright | Disclaimer | Privacy | Browsers | Accessibility | Contact National Center for Biotechnology Information, U.S. National Library of Medicine 8600 Rockville Pike, Bethesda MD, 20894 USA

Effective connectivity analysis explains metastable states of ongoing activity in cortically embedded systems of coupled synfire chains

In models of the cortex, synaptic connectivity is often assumed to be random within broad constraints such as interlayer connection densities. Alternatively, the connectivity could include richly structured circuitry. A recent study [1] demonstrated this in a model of local cortex of order 1 mm3 in size: a large number of synfire chains (small pools of neurons sequentially linked by feedforward connections) activated by waves (sequences of propagating spike packets) were embedded in a recurrent network of excitatory and inhibitory neurons. The model exhibits stable global dynamics in the asynchronous irregular regime and stable propagation of multiple synfire waves. Background noise generated by waves destabilizes wave propagation, providing a negative feedback signal limiting their number. Here we add inter-chain couplings (each chain branches to two successors) and variability in chain strengths to obtain a recurrent system with a topography. We study emergent patterns of activity propagation in such a system. Ongoing endogenous activity due to branching of waves and regulation of wave activity by noise feedback is typically found. Across model realizations and runs, ongoing activity manifests diverse steady-state patterns and transitions. We argue that steady states arise jointly and consistently with an effective connectivity: strength-dependent chain traversal probabilities averaged over noise fluctuations. By excluding chains with sub-threshold traversal probability we derive a family of effective coupling graphs (ECGs) parameterized by the global activity level. The distribution of wave activity in steady states is largely confined to the islands of circulation: strongly connected components (SCCs) and their associated out-components (OCs) in optimally chosen ECGs. A condensed ECG (cECG) allows the relationship between activity and effective connectivity to be visualized (Figure ​(Figure11). Figure 1 A The cECG with SCCs and OCs partitioned by their overlaps as nodes. B Chain indices of traversal events versus time (ms), permuted so chains in each cECG node are adjacent and colored accordingly. The system is implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model with binary-state pools as basic units. The reduced model exhibits activity patterns very similar to those of the full model and provides a valuable tool for studying the latter. We propose that the principle whereby activity patterns arise in concert with dynamically tuned effective connectivity applies to a broad class of networks with complex topologies.

A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale, sparsely connected neural network

Excessive synchronous firing of neurons is the sign of several neurological disorders such as Parkinson and epilepsy. In addition, growing evidence suggests that astrocytes have significant roles in neural synchronization. Drawing on these concepts and based on the latest studies, a bio-inspired stimulator which essentially is a dynamical model of the astrocyte biophysical model is proposed. The performance of the proposed bio-inspired stimulator is investigated on a large-scale, sparsely connected neural network which models a local cortical population. Next, a multiplier-less digital circuit for the bio-inspired stimulator is designed, and finally, the complete digital circuit of the closed-loop system is implemented in hardware on the ZedBoard development kit. Considering software simulations and hardware FPGA implementation, the proposed bio-inspired stimulator is able to prevent the hyper-synchronous neural firing in a network of excitatory and inhibitory neurons. Based on the obtained results, it is demonstrated that the proposed stimulator has a demand-controlled characteristic and can be a good candidate as a new deep brain stimulation (DBS) technique to effectively suppress the hyper-synchronous neural oscillations.

Limbic Encephalitis: Potential Impact of Adaptive Autoimmune Inflammation on Neuronal Circuits of the Amygdala.

Limbic encephalitis is characterized by adaptive autoimmune inflammation of the gray matter structures of the limbic system. It has recently been identified as a major cause of temporal lobe epilepsy accompanied by progressive declarative – mainly episodic – ­memory disturbance as well as a variety of rather poorly defined emotional and behavioral changes. While autoimmune inflammation of the hippocampus is likely to be responsible for declarative memory disturbance, consequences of autoimmune inflammation of the amygdala are largely unknown. The amygdala is central for the generation of adequate homoeostatic behavioral responses to emotionally significant external stimuli following processing in a variety of parallel neuronal circuits. Here, we hypothesize that adaptive cellular and humoral autoimmunity may target and modulate distinct inhibitory or excitatory neuronal networks within the amygdala, and thereby strongly impact processing of emotional stimuli and corresponding behavioral responses. This may explain some of the rather poorly understood neuropsychiatric symptoms in limbic encephalitis.

Oscillatorylike behavior in feedforward neuronal networks.

We demonstrate how rhythmic activity can arise in neural networks from feedforward rather than recurrent circuitry and, in so doing, we provide a mechanism capable of explaining the temporal decorrelation of γ-band oscillations. We compare the spiking activity of a delayed recurrent network of inhibitory neurons with that of a feedforward network with the same neural properties and axonal delays. Paradoxically, these very different connectivities can yield very similar spike-train statistics in response to correlated input. This happens when neurons are noisy and axonal delays are short. A Taylor expansion of the feedback network's susceptibility-or frequency-dependent gain function-can then be stopped at first order to a good approximation, thus matching the feedforward net's susceptibility. The feedback network is known to display oscillations; these oscillations imply that the spiking activity of the population is felt by all neurons within the network, leading to direct spike correlations in a given neuron. On the other hand, in the output layer of the feedforward net, the interaction between the external drive and the delayed feedforward projection of this drive by the input layer causes indirect spike correlations: spikes fired by a given output layer neuron are correlated only through the activity of the input layer neurons. High noise and short delays partially bridge the gap between these two types of correlation, yielding similar spike-train statistics for both networks. This similarity is even stronger when the delay is distributed, as confirmed by linear response theory.

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.