scholarly journals Feedforward inhibition allows input summation to vary in recurrent cortical networks

2017 ◽  
Author(s):  
Mark H. Histed
Keyword(s):  
Cortical Network ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Input Output ◽  
Cortical Networks ◽  
Sensory Stimuli ◽  
Cortical Input ◽  
V1 Neurons ◽  
Internal Network ◽  
Feedforward Inhibition

AbstractBrain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found V1 neurons’ average responses were primarily additive (linear). We used a recurrent cortical network model to determine if these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. The model showed cortical input-output transformations can be changed from linear to sublinear with moderate (∼20%) strengthening of connections between inhibitory neurons, but this change depends on the presence of feedforward inhibition. Thus, feedforward inhibition, a common feature of cortical circuitry, enables networks to flexibly change their spiking responses via changes in recurrent connectivity.Significance statementBrains are made up of neural networks that process information by receiving input activity and transforming those inputs into output activity. We use optogenetic manipulations in awake mice to expose how a transformation in a cortical network depends on internal network activity. Combining numerical simulations with our observations uncovers that transformation depend critically on feedforward inhibition – the fact that inputs to the cortex often make strong connections on both excitatory and inhibitory neurons.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

Integration of Broadband Conductance Input in Rat Somatosensory Cortical Inhibitory Interneurons: An Inhibition-Controlled Switch Between Intrinsic and Input-Driven Spiking in Fast-Spiking Cells

2009 ◽  
Vol 101 (2) ◽  
pp. 1056-1072 ◽  
Author(s):  
T. Tateno ◽  
H.P.C. Robinson
Keyword(s):  
Cell Types ◽  
Gamma Oscillations ◽  
External Input ◽  
Spike Timing ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Dependent Manner ◽  
Input Output ◽  
Complex Activity ◽  
Fast Spiking

Quantitative understanding of the dynamics of particular cell types when responding to complex, natural inputs is an important prerequisite for understanding the operation of the cortical network. Different types of inhibitory neurons are connected by electrical synapses to nearby neurons of the same type, enabling the formation of synchronized assemblies of neurons with distinct dynamical behaviors. Under what conditions is spike timing in such cells determined by their intrinsic dynamics and when is it driven by the timing of external input? In this study, we have addressed this question using a systematic approach to characterizing the input–output relationships of three types of cortical interneurons (fast spiking [FS], low-threshold spiking [LTS], and nonpyramidal regular-spiking [NPRS] cells) in the rat somatosensory cortex, during fluctuating conductance input designed to mimic natural complex activity. We measured the shape of average conductance input trajectories preceding spikes and fitted a two-component linear model of neuronal responses, which included an autoregressive term from its own output, to gain insight into the input–output relationships of neurons. This clearly separated the contributions of stimulus and discharge history, in a cell-type dependent manner. Unlike LTS and NPRS cells, FS cells showed a remarkable switch in dynamics, from intrinsically driven spike timing to input-fluctuation–controlled spike timing, with the addition of even a small amount of inhibitory conductance. Such a switch could play a pivotal role in the function of FS cells in organizing coherent gamma oscillations in the local cortical network. Using both pharmacological perturbations and modeling, we show how this property is a consequence of the particular complement of voltage-dependent conductances in these cells.

Correlations and Population Dynamics in Cortical Networks

2008 ◽  
Vol 20 (9) ◽  
pp. 2185-2226 ◽  
Author(s):  
Birgit Kriener ◽  
Tom Tetzlaff ◽  
Ad Aertsen ◽  
Markus Diesmann ◽  
Stefan Rotter
Keyword(s):  
Mean Field ◽  
Power Spectra ◽  
Membrane Potentials ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Connectivity Matrix ◽  
Cortical Networks ◽  
Population Activity ◽  
Neuronal Populations ◽  
Experimental Findings

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

scholarly journals Robust associative learning is sufficient to explain structural and dynamical properties of local cortical circuits

2018 ◽  
Author(s):  
Danke Zhang ◽  
Chi Zhang ◽  
Armen Stepanyants
Keyword(s):  
Associative Learning ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Cortical Circuits ◽  
Cortical Networks ◽  
Connection Weight ◽  
Postsynaptic Potential ◽  
Weight Distributions ◽  
Network Properties ◽  
Dynamical Properties

ABSTRACTThe ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memory sequences of varying lengths and compared network properties to those observed experimentally. We show that when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overrepresentations of specific 3-neuron motifs, distributions of connection numbers in clusters of 3–8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. What is more, memories loaded into the network can be retrieved even in the presence of noise comparable to the baseline variations in the postsynaptic potential. Confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning.

scholarly journals Altered network properties in C9ORF72 repeat expansion cortical neurons are due to synaptic dysfunction

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Emma M. Perkins ◽  
Karen Burr ◽  
Poulomi Banerjee ◽  
Arpan R. Mehta ◽  
Owen Dando ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Synaptic Vesicle ◽  
Cortical Neurons ◽  
Electrode Array ◽  
Repeat Expansion ◽  
Cortical Network ◽  
Synaptic Function ◽  
Network Activity ◽  
Cortical Function ◽  
Network Function

Abstract Background Physiological disturbances in cortical network excitability and plasticity are established and widespread in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) patients, including those harbouring the C9ORF72 repeat expansion (C9ORF72RE) mutation – the most common genetic impairment causal to ALS and FTD. Noting that perturbations in cortical function are evidenced pre-symptomatically, and that the cortex is associated with widespread pathology, cortical dysfunction is thought to be an early driver of neurodegenerative disease progression. However, our understanding of how altered network function manifests at the cellular and molecular level is not clear. Methods To address this we have generated cortical neurons from patient-derived iPSCs harbouring C9ORF72RE mutations, as well as from their isogenic expansion-corrected controls. We have established a model of network activity in these neurons using multi-electrode array electrophysiology. We have then mechanistically examined the physiological processes underpinning network dysfunction using a combination of patch-clamp electrophysiology, immunocytochemistry, pharmacology and transcriptomic profiling. Results We find that C9ORF72RE causes elevated network burst activity, associated with enhanced synaptic input, yet lower burst duration, attributable to impaired pre-synaptic vesicle dynamics. We also show that the C9ORF72RE is associated with impaired synaptic plasticity. Moreover, RNA-seq analysis revealed dysregulated molecular pathways impacting on synaptic function. All molecular, cellular and network deficits are rescued by CRISPR/Cas9 correction of C9ORF72RE. Our study provides a mechanistic view of the early dysregulated processes that underpin cortical network dysfunction in ALS-FTD. Conclusion These findings suggest synaptic pathophysiology is widespread in ALS-FTD and has an early and fundamental role in driving altered network function that is thought to contribute to neurodegenerative processes in these patients. The overall importance is the identification of previously unidentified defects in pre and postsynaptic compartments affecting synaptic plasticity, synaptic vesicle stores, and network propagation, which directly impact upon cortical function.

Endocannabinoid system in the neurodevelopment of GABAergic interneurons: implications for neurological and psychiatric disorders

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chang-geng Song ◽  
Xin Kang ◽  
Fang Yang ◽  
Wan-qing Du ◽  
Jia-jia Zhang ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Endocannabinoid System ◽  
Autism Spectrum ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Gabaergic Interneurons ◽  
Neural Stem Progenitor Cells ◽  
The Central Nervous System ◽  
Interneuron Development

Abstract In mature mammalian brains, the endocannabinoid system (ECS) plays an important role in the regulation of synaptic plasticity and the functioning of neural networks. Besides, the ECS also contributes to the neurodevelopment of the central nervous system. Due to the increase in the medical and recreational use of cannabis, it is inevitable and essential to elaborate the roles of the ECS on neurodevelopment. GABAergic interneurons represent a group of inhibitory neurons that are vital in controlling neural network activity. However, the role of the ECS in the neurodevelopment of GABAergic interneurons remains to be fully elucidated. In this review, we provide a brief introduction of the ECS and interneuron diversity. We focus on the process of interneuron development and the role of ECS in the modulation of interneuron development, from the expansion of the neural stem/progenitor cells to the migration, specification and maturation of interneurons. We further discuss the potential implications of the ECS and interneurons in the pathogenesis of neurological and psychiatric disorders, including epilepsy, schizophrenia, major depressive disorder and autism spectrum disorder.

scholarly journals Novel neuronal and astrocytic mechanisms in thalamocortical loop dynamics

2002 ◽  
Vol 357 (1428) ◽  
pp. 1675-1693 ◽  
Author(s):  
Vincenzo Crunelli ◽  
Kate L. Blethyn ◽  
David W. Cope ◽  
Stuart W. Hughes ◽  
H. Rheinallt Parri ◽  
...  
Keyword(s):  
Low Voltage ◽  
Electrical Coupling ◽  
Low Frequency ◽  
Network Activity ◽  
Thalamic Neuron ◽  
Cellular Mechanisms ◽  
Brain Areas ◽  
Sensory Stimuli ◽  
Loop Dynamics ◽  
Intracellular Ca

In this review, we summarize three sets of findings that have recently been observed in thalamic astrocytes and neurons, and discuss their significance for thalamocortical loop dynamics. (i) A physiologically relevant ‘window’ component of the low–voltage–activated, T–type Ca 2+ current ( I Twindow ) plays an essential part in the slow (less than 1 Hz) sleep oscillation in adult thalamocortical (TC) neurons, indicating that the expression of this fundamental sleep rhythm in these neurons is not a simple reflection of cortical network activity. It is also likely that I Twindow underlies one of the cellular mechanisms enabling TC neurons to produce burst firing in response to novel sensory stimuli. (ii) Both electrophysiological and dye–injection experiments support the existence of gap junction–mediated coupling among young and adult TC neurons. This finding indicates that electrical coupling–mediated synchronization might be implicated in the high and low frequency oscillatory activities expressed by this type of thalamic neuron. (iii) Spontaneous intracellular Ca 2+ ([Ca 2+ ] i ) waves propagating among thalamic astrocytes are able to elicit large and long–lasting N –methyl–D–aspartate–mediated currents in TC neurons. The peculiar developmental profile within the first two postnatal weeks of these astrocytic [Ca 2+ ] i transients and the selective activation of these glutamate receptors point to a role for this astrocyte–to–neuron signalling mechanism in the topographic wiring of the thalamocortical loop. As some of these novel cellular and intracellular properties are not restricted to thalamic astrocytes and neurons, their significance may well apply to (patho)physiological functions of glial and neuronal elements in other brain areas.

scholarly journals Facilitating the propagation of spiking activity in feedforward networks by including feedback

2019 ◽  
Author(s):  
Hedyeh Rezaei ◽  
Ad Aertsen ◽  
Arvind Kumar ◽  
Alireza Valizadeh
Keyword(s):  
Resonance Frequency ◽  
External Input ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Modular System ◽  
Feedforward Networks ◽  
Successful Transmission ◽  
Successful Communication ◽  
Resonance Frequencies ◽  
Feedback Connections

AbstractTransient oscillations in the network activity upon sensory stimulation have been reported in different sensory areas. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys (‘pulse packets’), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules’ resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three sever constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build-up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.Author summaryThe cortex is organized as a modular system, with the modules (cortical areas) communicating via weak long-range connections. It has been suggested that the intrinsic resonance properties of population activities in these areas might contribute to enabling successful communication. A module’s intrinsic resonance appears in the damped oscillatory response to an incoming spike volley, enabling successful communication during the peaks of the oscillation. Such communication can be exploited in feedforward networks, provided the participating networks have similar resonance frequencies. This, however, is not necessarily true for cortical networks. Moreover, the communication is slow, as it takes several oscillation cycles to build up the response in the downstream network. Also, only periodic trains of spikes volleys (and not single volleys) with matching intervals can propagate. Here, we present a novel mechanism that alleviates these shortcomings and enables propagation of synchronous spiking across weakly connected networks with not necessarily identical resonance frequencies. In this framework, an individual spike volley can propagate by local amplification through reverberation in a loop between two successive networks, connected by feedforward and feedback connections: the resonance pair. This overcomes the need for activity build-up in downstream networks, causing the volley to propagate distinctly faster and more reliably.

scholarly journals Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lea Fritschi ◽  
Johanna Hedlund Lindmar ◽  
Florian Scheidl ◽  
Kerstin Lenk
Keyword(s):  
Computational Models ◽  
Glutamate Release ◽  
Signal Transmission ◽  
Computational Modelling ◽  
Atp Release ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Neuronal Network Activity ◽  
Cell Densities ◽  
Synapse Model

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

scholarly journals Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex

2018 ◽  
Author(s):  
Petr Znamenskiy ◽  
Mean-Hwan Kim ◽  
Dylan R. Muir ◽  
Maria Florencia Iacaruso ◽  
Sonja B. Hofer ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Pyramidal Cells ◽  
Fine Tuning ◽  
Inhibitory Neurons ◽  
Local Network ◽  
Cortical Circuits ◽  
Sensory Stimuli ◽  
Excitatory Neurons ◽  
Strong Excitation

In the cerebral cortex, the interaction of excitatory and inhibitory synaptic inputs shapes the responses of neurons to sensory stimuli, stabilizes network dynamics1 and improves the efficiency and robustness of the neural code2–4. Excitatory neurons receive inhibitory inputs that track excitation5–8. However, how this co-tuning of excitation and inhibition is achieved by cortical circuits is unclear, since inhibitory interneurons are thought to pool the inputs of nearby excitatory cells and provide them with non-specific inhibition proportional to the activity of the local network9–13. Here we show that although parvalbumin-expressing (PV) inhibitory cells in mouse primary visual cortex make connections with the majority of nearby pyramidal cells, the strength of their synaptic connections is structured according to the similarity of the cells’ responses. Individual PV cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This fine-tuning of synaptic weights supports co-tuning of inhibitory and excitatory inputs onto individual pyramidal cells despite dense connectivity between inhibitory and excitatory neurons. Our results indicate that individual PV cells are preferentially integrated into subnetworks of inter-connected, co-tuned pyramidal cells, stabilising their recurrent dynamics. Conversely, weak but dense inhibitory connectivity between subnetworks is sufficient to support competition between them, de-correlating their output. We suggest that the history and structure of correlated firing adjusts the weights of both inhibitory and excitatory connections, supporting stable amplification and selective recruitment of cortical subnetworks.

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