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Information Processing in Neuronal Circuits and Systems

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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Neuroscience".

Deadline for manuscript submissions: closed (7 May 2021) | Viewed by 31925

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Prof. Dr. Claudio R. Mirasso

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Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
Interests: theoretical studies of information processing in the brain; dynamics of neuronal circuits; biomedical time series analysis; neuro-inspired devices

Prof. Dr. Alireza Valizadeh

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Department of Physics, Institute of Advanced Studies in Basic Sciences (IASBS), Iran
Interests: theoretical neuroscience; dynamics of the neuronal networks; brain oscillations; mechanisms of communication in neural circuits

Special Issue Information

The question of how the brain integrates the multiplicity of local processes dispersed throughout its entire anatomy is a fundamental question in neuroscience. Such integration is mediated by information exchange between brain areas, and other parts of the nervous system, through routes that can change rapidly to provide a flexible effective connectivity despite the rigidity of the anatomical connections.

The flexibility of the information routing and effective connectivity are consequences of the multistability of the collective dynamics of the neuronal networks. In other words, a single structural connection can support plenty of degenerate dynamic states for each of which a special pattern of information transfer between the nodes can take place. Collective neuronal oscillations are hypothesized to provide such a basis for the dynamic communication between brain regions. In this Special Issue, the notion of information transfer and routing in neuronal circuits and systems will be addressed with a focus on shedding light on the mechanisms that regulate information transmission in the brain and in the nervous system in general.

Prof. Dr. Claudio R. Mirasso
Prof. Dr. Alireza Valizadeh
Guest Editors

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Keywords

information processing
information routing
brain oscillations
functional connectivity
effective connectivity
neural communication
communication through coherence
brain circuits
neural systems and circuits
neuronal modulation
neural networks

Published Papers (10 papers)

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Editorial

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4 pages, 208 KiB

Open AccessEditorial

Editorial to the Special Issue “Information Processing in Neuronal Circuits and Systems”

by Alireza Valizadeh and Claudio Mirasso

Biology 2023, 12(3), 359; https://0-doi-org.brum.beds.ac.uk/10.3390/biology12030359 - 24 Feb 2023

Viewed by 928

Abstract

The nervous system processes sensory information through a hierarchical structure with multiple processing stages [...] Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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26 pages, 666 KiB

Open AccessArticle

Computational Modeling of Information Propagation during the Sleep–Waking Cycle

by Farhad Razi, Rubén Moreno-Bote and Belén Sancristóbal

Biology 2021, 10(10), 945; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100945 - 22 Sep 2021

Cited by 1 | Viewed by 2840

Abstract

Non-threatening familiar sounds can go unnoticed during sleep despite the fact that they enter our brain by exciting the auditory nerves. Extracellular cortical recordings in the primary auditory cortex of rodents show that an increase in firing rate in response to pure tones during deep phases of sleep is comparable to those evoked during wakefulness. This result challenges the hypothesis that during sleep cortical responses are weakened through thalamic gating. An alternative explanation comes from the observation that the spatiotemporal spread of the evoked activity by transcranial magnetic stimulation in humans is reduced during non-rapid eye movement (NREM) sleep as compared to the wider propagation to other cortical regions during wakefulness. Thus, cortical responses during NREM sleep remain local and the stimulus only reaches nearby neuronal populations. We aim at understanding how this behavior emerges in the brain as it spontaneously shifts between NREM sleep and wakefulness. To do so, we have used a computational neural-mass model to reproduce the dynamics of the sensory auditory cortex and corresponding local field potentials in these two brain states. Following the synaptic homeostasis hypothesis, an increase in a single parameter, namely the excitatory conductance

{\bar{g}}_{AMPA}

, allows us to place the model from NREM sleep into wakefulness. In agreement with the experimental results, the endogenous dynamics during NREM sleep produces a comparable, even higher, response to excitatory inputs to the ones during wakefulness. We have extended the model to two bidirectionally connected cortical columns and have quantified the propagation of an excitatory input as a function of their coupling. We have found that the general increase in all conductances of the cortical excitatory synapses that drive the system from NREM sleep to wakefulness does not boost the effective connectivity between cortical columns. Instead, it is the inter-/intra-conductance ratio of cortical excitatory synapses that should raise to facilitate information propagation across the brain. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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24 pages, 3057 KiB

Open AccessArticle

A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity

by Alejandro Santos-Mayo, Stephan Moratti, Javier de Echegaray and Gianluca Susi

Biology 2021, 10(8), 801; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10080801 - 19 Aug 2021

Cited by 2 | Viewed by 3287

Abstract

Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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18 pages, 1070 KiB

Open AccessArticle

Mechanisms of Flexible Information Sharing through Noisy Oscillations

by Arthur S. Powanwe and Andre Longtin

Biology 2021, 10(8), 764; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10080764 - 10 Aug 2021

Cited by 4 | Viewed by 1630

Abstract

Brain areas must be able to interact and share information in a time-varying, dynamic manner on a fast timescale. Such flexibility in information sharing has been linked to the synchronization of rhythm phases between areas. One definition of flexibility is the number of local maxima in the delayed mutual information curve between two connected areas. However, the precise relationship between phase synchronization and information sharing is not clear, nor is the flexibility in the face of the fixed structural connectivity and noise. Here, we consider two coupled oscillatory excitatory-inhibitory networks connected through zero-delay excitatory connections, each of which mimics a rhythmic brain area. We numerically compute phase-locking and delayed mutual information between the phases of excitatory local field potential (LFPs) of the two networks, which measures the shared information and its direction. The flexibility in information sharing is shown to depend on the dynamical origin of oscillations, and its properties in different regimes are found to persist in the presence of asymmetry in the connectivity as well as system heterogeneity. For coupled noise-induced rhythms (quasi-cycles), phase synchronization is robust even in the presence of asymmetry and heterogeneity. However, they do not show flexibility, in contrast to noise-perturbed rhythms (noisy limit cycles), which are shown here to exhibit two local information maxima, i.e., flexibility. For quasi-cycles, phase difference and information measures for the envelope-phase dynamics obtained from previous analytical work using the Stochastic Averaging Method (SAM) are found to be in good qualitative agreement with those obtained from the original dynamics. The relation between phase synchronization and communication patterns is not trivial, particularly in the noisy limit cycle regime. There, complex patterns of information sharing can be observed for a single value of the phase difference. The mechanisms reported here can be extended to I-I networks since their phase synchronizations are similar. Our results set the stage for investigating information sharing between several connected noisy rhythms in neural and other complex biological networks. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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14 pages, 3066 KiB

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Optimal Input Representation in Neural Systems at the Edge of Chaos

by Guillermo B. Morales and Miguel A. Muñoz

Biology 2021, 10(8), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10080702 - 23 Jul 2021

Cited by 7 | Viewed by 3075

Abstract

Shedding light on how biological systems represent, process and store information in noisy environments is a key and challenging goal. A stimulating, though controversial, hypothesis poses that operating in dynamical regimes near the edge of a phase transition, i.e., at criticality or the “edge of chaos”, can provide information-processing living systems with important operational advantages, creating, e.g., an optimal trade-off between robustness and flexibility. Here, we elaborate on a recent theoretical result, which establishes that the spectrum of covariance matrices of neural networks representing complex inputs in a robust way needs to decay as a power-law of the rank, with an exponent close to unity, a result that has been indeed experimentally verified in neurons of the mouse visual cortex. Aimed at understanding and mimicking these results, we construct an artificial neural network and train it to classify images. We find that the best performance in such a task is obtained when the network operates near the critical point, at which the eigenspectrum of the covariance matrix follows the very same statistics as actual neurons do. Thus, we conclude that operating near criticality can also have—besides the usually alleged virtues—the advantage of allowing for flexible, robust and efficient input representations. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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14 pages, 2111 KiB

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Functional Interactions between Entorhinal Cortical Pathways Modulate Theta Activity in the Hippocampus

by Víctor J. López-Madrona and Santiago Canals

Biology 2021, 10(8), 692; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10080692 - 21 Jul 2021

Cited by 7 | Viewed by 2744

Abstract

Theta oscillations organize neuronal firing in the hippocampus during context exploration and memory formation. Recently, we have shown that multiple theta rhythms coexist in the hippocampus, reflecting the activity in their afferent regions in CA3 (Schaffer collaterals) and the entorhinal cortex layers II (EC-II, perforant pathway) and III (EC-III, temporoammonic pathway). Frequency and phase coupling between theta rhythms were modulated by the behavioral state, with synchronized theta rhythmicity preferentially occurring in tasks involving memory updating. However, information transmission between theta generators was not investigated. Here, we used source separation techniques to disentangle the current generators recorded in the hippocampus of rats exploring a known environment with or without a novel stimulus. We applied analytical tools based on Granger causality and transfer entropy to investigate linear and non-linear directed interactions, respectively, between the theta activities. Exploration in the novelty condition was associated with increased theta power in the generators with EC origin. We found a significant directed interaction from the Schaffer input over the EC-III input in CA1, and a bidirectional interaction between the inputs in the hippocampus originating in the EC, likely reflecting the connection between layers II and III. During novelty exploration, the influence of the EC-II over the EC-III generator increased, while the Schaffer influence decreased. These results associate the increase in hippocampal theta activity and synchrony during novelty exploration with an increase in the directed functional connectivity from EC-II to EC-III. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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14 pages, 1769 KiB

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Modeling Post-Scratching Locomotion with Two Rhythm Generators and a Shared Pattern Formation

by Jesus A. Tapia, Argelia Reid, John Reid, Saul M. Dominguez-Nicolas and Elias Manjarrez

Biology 2021, 10(7), 663; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10070663 - 14 Jul 2021

Cited by 1 | Viewed by 2134

Abstract

This study aimed to present a model of post-scratching locomotion with two intermixed central pattern generator (CPG) networks, one for scratching and another for locomotion. We hypothesized that the rhythm generator layers for each CPG are different, with the condition that both CPGs share their supraspinal circuits and their motor outputs at the level of their pattern formation networks. We show that the model reproduces the post-scratching locomotion latency of 6.2 ± 3.5 s, and the mean cycle durations for scratching and post-scratching locomotion of 0.3 ± 0.09 s and 1.7 ± 0.6 s, respectively, which were observed in a previous experimental study. Our findings show how the transition of two rhythmic movements could be mediated by information exchanged between their CPG circuits through routes converging in a common pattern formation layer. This integrated organization may provide flexible and effective connectivity despite the rigidity of the anatomical connections in the spinal cord circuitry. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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11 pages, 13450 KiB

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EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness

by Jorge Pretel, Joaquín J. Torres and Joaquín Marro

Biology 2021, 10(7), 647; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10070647 - 11 Jul 2021

Cited by 3 | Viewed by 2085

Abstract

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Moreover, near this phase transition, our model system exhibits waves with a strong component in the so-called delta-theta domain that coexist with fast oscillations. These findings provide a simple explanation for the observed delta-gamma and theta-gamma modulation in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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16 pages, 870 KiB

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Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

by Daniel Miner, Florentin Wörgötter, Christian Tetzlaff and Michael Fauth

Biology 2021, 10(7), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10070577 - 24 Jun 2021

Cited by 1 | Viewed by 1695

Abstract

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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Review

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Anatomy and Neural Pathways Modulating Distinct Locomotor Behaviors in Drosophila Larva

by Swetha B. M. Gowda, Safa Salim and Farhan Mohammad

Biology 2021, 10(2), 90; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10020090 - 25 Jan 2021

Cited by 10 | Viewed by 9605

Abstract

The control of movements is a fundamental feature shared by all animals. At the most basic level, simple movements are generated by coordinated neural activity and muscle contraction patterns that are controlled by the central nervous system. How behavioral responses to various sensory inputs are processed and integrated by the downstream neural network to produce flexible and adaptive behaviors remains an intense area of investigation in many laboratories. Due to recent advances in experimental techniques, many fundamental neural pathways underlying animal movements have now been elucidated. For example, while the role of motor neurons in locomotion has been studied in great detail, the roles of interneurons in animal movements in both basic and noxious environments have only recently been realized. However, the genetic and transmitter identities of many of these interneurons remains unclear. In this review, we provide an overview of the underlying circuitry and neural pathways required by Drosophila larvae to produce successful movements. By improving our understanding of locomotor circuitry in model systems such as Drosophila, we will have a better understanding of how neural circuits in organisms with different bodies and brains lead to distinct locomotion types at the organism level. The understanding of genetic and physiological components of these movements types also provides directions to understand movements in higher organisms. Full article

(This article belongs to the Special Issue Information Processing in Neuronal Circuits and Systems)

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