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 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.

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 Circuit models of low dimensional shared variability in cortical networks

2017 ◽  
Author(s):  
Chengcheng Huang ◽  
Douglas A. Ruff ◽  
Ryan Pyle ◽  
Robert Rosenbaum ◽  
Marlene R. Cohen ◽  
...  
Keyword(s):  
Brain Area ◽  
Inhibitory Neurons ◽  
Cortical Circuits ◽  
Cortical Networks ◽  
Visual Neurons ◽  
Spatial And Temporal Scales ◽  
External Sources ◽  
Inhibitory Coupling ◽  
Low Dimensional

AbstractTrial-to-trial variability is a reflection of the circuitry and cellular physiology that makeup a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. Previous model cortical networks are at loss to explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons internally generate low dimensional shared variability that captures the properties of in vivo population recordings along the visual pathway. Shifting spatial attention into the receptive field of visual neurons has been shown to reduce low dimensional shared variability within a brain area, yet increase the variability shared between areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation, providing support for our theory of cortical variability. Our work provides a critical and previously missing mechanistic link between observed cortical circuit structure and realistic population-wide shared neuronal variability and its modulation.

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 Aberrant development of excitatory circuits to inhibitory neurons in the primary visual cortex after neonatal binocular enucleation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rongkang Deng ◽  
Joseph P. Y. Kao ◽  
Patrick O. Kanold
Keyword(s):  
Visual Cortex ◽  
Nmda Receptors ◽  
Laser Scanning ◽  
Inhibitory Neurons ◽  
Gabaergic Interneurons ◽  
Cortical Circuits ◽  
Retinal Input ◽  
Layer 4 ◽  
Ampa And Nmda Receptors ◽  
Laser Scanning Photostimulation

AbstractThe development of GABAergic interneurons is important for the functional maturation of cortical circuits. After migrating into the cortex, GABAergic interneurons start to receive glutamatergic connections from cortical excitatory neurons and thus gradually become integrated into cortical circuits. These glutamatergic connections are mediated by glutamate receptors including AMPA and NMDA receptors and the ratio of AMPA to NMDA receptors decreases during development. Since previous studies have shown that retinal input can regulate the early development of connections along the visual pathway, we investigated if the maturation of glutamatergic inputs to GABAergic interneurons in the visual cortex requires retinal input. We mapped the spatial pattern of glutamatergic connections to layer 4 (L4) GABAergic interneurons in mouse visual cortex at around postnatal day (P) 16 by laser-scanning photostimulation and investigated the effect of binocular enucleations at P1/P2 on these patterns. Gad2-positive interneurons in enucleated animals showed an increased fraction of AMPAR-mediated input from L2/3 and a decreased fraction of input from L5/6. Parvalbumin-expressing (PV) interneurons showed similar changes in relative connectivity. NMDAR-only input was largely unchanged by enucleation. Our results show that retinal input sculpts the integration of interneurons into V1 circuits and suggest that the development of AMPAR- and NMDAR-only connections might be regulated differently.

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.

scholarly journals Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons

2016 ◽  
Vol 115 (1) ◽  
pp. 457-469 ◽  
Author(s):  
Mahmood S. Hoseini ◽  
Ralf Wessel
Keyword(s):  
Single Neuron ◽  
Experimental Studies ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Peak Frequency ◽  
Spiking Neurons ◽  
Network Activity ◽  
Cortical Circuits ◽  
Model Network ◽  
Low Rate

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.

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