Information Processing In Neuronal Networks Research Articles

Beyond-local neural information processing in neuronal networks

Beyond-local neural information processing in neuronal networks

Caffeic acid recovers ischemia-induced synaptic dysfunction without direct effects on excitatory synaptic transmission and plasticity in mouse hippocampal slices

Caffeic acid is a polyphenolic compound present in a vast array of dietary components. We previously showed that caffeic acid reduces the burden of brain ischemia joining evidence by others that it can attenuate different brain diseases. However, it is unknown if caffeic acid affects information processing in neuronal networks. Thus, we now used electrophysiological recordings in mouse hippocampal slices to test if caffeic acid directly affected synaptic transmission, plasticity and dysfunction caused by oxygen-glucose deprivation (OGD), an in vitro ischemia model. Caffeic acid (1–10 μM) was devoid of effect on synaptic transmission and paired-pulse facilitation in Schaffer collaterals-CA1 pyramidal synapses. Also, the magnitude of either hippocampal long-term potentiation (LTP) or the subsequent depotentiation were not significantly modified by 10 μM caffeic acid. However, caffeic acid (10 μM) increased the recovery of synaptic transmission upon re-oxygenation following 7 min of OGD. Furthermore, caffeic acid (10 μM) also recovered plasticity after OGD, as heralded by the increased magnitude of LTP after exposure. These findings show that caffeic acid does not directly affect synaptic transmission and plasticity but can indirectly affect other cellular targets to correct synaptic dysfunction. Unraveling the molecular mechanisms of action of caffeic acid may allow the design of hitherto unrecognized novel neuroprotective strategies.

Dendritic axon origin enables information gating by perisomatic inhibition in pyramidal neurons.

Information processing in neuronal networks involves the recruitment of selected neurons into coordinated spatiotemporal activity patterns. This sparse activation results from widespread synaptic inhibition in conjunction with neuron-specific synaptic excitation. We report the selective recruitment of hippocampal pyramidal cells into patterned network activity. During ripple oscillations in awake mice, spiking is much more likely in cells in which the axon originates from a basal dendrite rather than from the soma. High-resolution recordings in vitro and computer modeling indicate that these spikes are elicited by synaptic input to the axon-carrying dendrite and thus escape perisomatic inhibition. Pyramidal cells with somatic axon origin can be activated during ripple oscillations by blocking their somatic inhibition. The recruitment of neurons into active ensembles is thus determined by axonal morphological features.

From mechanisms to functions: The role of theta and gamma coherence in the intrahippocampal circuits.

Brain rhythms are essential for information processing in neuronal networks. Oscillations recorded in different brain regions can be synchronized and have a constant phase difference, that is, they can be coherent. Coherence between local field potential (LFP) signals from different brain regions may be correlated with the performance of cognitive tasks, indicating that these regions of the brain are jointly involved in the information processing. Why does coherence occur and how is it related to the information transfer between different regions of the hippocampal formation? In this article, we discuss possible mechanisms of theta and gamma coherence and its role in the hippocampus-dependent attention and memory processes, since theta and gamma rhythms are most pronounced in these processes. We review in vivo studies of interactions between different regions of the hippocampal formation in theta and gamma frequency bands. The key propositions of the review are as follows: (1) coherence emerges from synchronous postsynaptic currents in principal neurons as a result of synchronization of neuronal spike activity; (2) the synchronization of neuronal spike patterns in two regions of the hippocampal formation can be realized through induction or resonance; (3) coherence at a specific time point reflects the transfer of information between the regions of the hippocampal formation; (4) the physiological roles of theta and gamma coherence are different due to their different functions and mechanisms of generation. All hippocampal neurons are involved in theta activity, and theta coherence arranges the firing order of principal neurons throughout the hippocampal formation. In contrast, gamma coherence reflects the coupling of active neuronal ensembles. Overall, the coherence of LFPs between different areas of the brain is an important physiological process based on the synchronized neuronal firing, and it is essential for cooperative information processing.

Plants have neither synapses nor a nervous system

The alleged existence of so-called synapses or equivalent structures in plants provided the basis for the concept of Plant Neurobiology (Baluska et al., 2005; Brenner et al., 2006). More recently, supporters of this controversial theory have even speculated that the phloem acts as a kind of nerve system serving long distance electrical signaling (Mediano et al., 2021; Baluska and Mancuso, 2021). In this review we have critically examined the literature cited by these authors and arrive at a completely different conclusion. Plants do not have any structures resembling animal synapses (neither chemical nor electrical). While they certainly do have complex cell contacts and signaling mechanisms, none of these structures provides a basis for neuronal-like synaptic transmission. Likewise, the phloem is undoubtedly a conduit for the propagation of electrical signaling, but the characteristics of this process are in no way comparable to the events underlying information processing in neuronal networks. This has obvious implications in regard to far-going speculations into the realms of cognition, sentience and consciousness.

Modular structure in C. elegans neural network and its response to external localized stimuli

Synchronization plays a key role in information processing in neuronal networks. Response of specific groups of neurons are triggered by external stimuli, such as visual, tactile or olfactory inputs. Neurons, however, can be divided into several categories, such as by physical location, functional role or topological clustering properties. Here we study the response of the electric junction C. elegans network to external stimuli using the partially forced Kuramoto model and applying the force to specific groups of neurons. Stimuli were applied to three topological modules, two ganglia, specified by their anatomical localization, and to the functional groups composed of all sensory and motoneurons. We found that topological modules do not contain purely anatomical groups or functional classes, corroborating previous results, and that stimulating different classes of neurons lead to very different responses, measured in terms of synchronization and phase velocity correlations. In all cases the modular structure hindered full synchronization, protecting the system from seizures. The responses to stimuli applied to topological and functional modules showed pronounced patterns of correlation or anti-correlation with other modules that were not observed when the stimulus was applied to a ganglion with mixed functional neurons.

A databank for intracellular electrophysiological mapping of the adult somatosensory cortex.

BackgroundNeurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the fourth postnatal week. However, the dynamics of the neuronal integration in adulthood is largely unknown. Electrophysiological characterization of the active properties of these neurons throughout adulthood will help to address the biophysical and computational principles of the neuronal integration.FindingsHere, we provide a database of whole-cell intracellular recordings from 315 neurons located in the supragranular layers (L2/3) of the primary somatosensory cortex in adult mice (9–45 weeks old) from both sexes (females, N = 195; males, N = 120). Data include 361 somatic current-clamp (CC) and 476 voltage-clamp (VC) experiments, recorded using a step-and-hold protocol (CC, N = 257; VC, N = 46), frozen noise injections (CC, N = 104) and triangular voltage sweeps (VC, 10 (N = 132), 50 (N = 146) and 100 ms (N = 152)), from regular spiking (N = 169) and fast-spiking neurons (N = 66).ConclusionsThe data can be used to systematically study the properties of somatic integration and the principles of action potential generation across sexes and across electrically characterized neuronal classes in adulthood. Understanding the principles of the somatic transformation of postsynaptic potentials into action potentials will shed light onto the computational principles of intracellular information transfer in single neurons and information processing in neuronal networks, helping to recreate neuronal functions in artificial systems.

Optimizing information processing in neuronal networks beyond critical states.

Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.

Astroglial Vesicular Trafficking in Neurodegenerative Diseases.

The neocortex represents one of the largest estates of the human brain. This structure comprises ~30-40 billions of neurones and even more of non-neuronal cells. Astrocytes, highly heterogeneous homoeostatic glial cells, are fundamental for housekeeping of the brain and contribute to information processing in neuronal networks. Gray matter astrocytes tightly enwrap synapses, contact blood vessels and, naturally, are also in contact with the extracellular space, where convection of fluid takes place. Thus astrocytes receive signals from several distinct extracellular domains and can get excited by numerous mechanisms, which regulate cytosolic concentration of second messengers, such as Ca2+ and cAMP. Excited astrocytes often secrete diverse substances (generally referred to as gliosignalling molecules) that include classical neurotransmitters such as glutamate and ATP or neuromodulators such as D-serine or neuropeptides. Astrocytic secretion occurs through several mechanisms: by diffusion through membrane channels, by translocation via plasmalemmal transporters or by vesicular exocytosis. Vesicular release of gliosignalling molecules appears fundamentally similar to that operating in neurones, since it depends on the SNARE proteins-dependent merger of the vesicle membrane with the plasmalemma. However, the coupling between the stimulus and astroglial vesicular secretion is at least one order of magnitude slower than that in neurones. Here we review mechanisms of astrocytic excitability and the molecular, anatomical and physiological properties of vesicular apparatus mediating the release of gliosignalling molecules in health and in the neurodegenerative pathology.

On the properties of input-to-output transformations in neuronal networks.

Information processing in neuronal networks in certain important cases can be considered as maps of binary vectors, where ones (spikes) and zeros (no spikes) of input neurons are transformed into spikes and no spikes of output neurons. A simple but fundamental characteristic of such a map is how it transforms distances between input vectors into distances between output vectors. We advanced earlier known results by finding an exact solution to this problem for McCulloch-Pitts neurons. The obtained explicit formulas allow for detailed analysis of how the network connectivity and neuronal excitability affect the transformation of distances in neurons. As an application, we explored a simple model of information processing in the hippocampus, a brain area critically implicated in learning and memory. We found network connectivity and neuronal excitability parameter values that optimize discrimination between similar and distinct inputs. A decrease of neuronal excitability, which in biological neurons may be associated with decreased inhibition, impaired the optimality of discrimination.

Diverse impact of acute and long-term extracellular proteolytic activity on plasticity of neuronal excitability.

Learning and memory require alteration in number and strength of existing synaptic connections. Extracellular proteolysis within the synapses has been shown to play a pivotal role in synaptic plasticity by determining synapse structure, function, and number. Although synaptic plasticity of excitatory synapses is generally acknowledged to play a crucial role in formation of memory traces, some components of neural plasticity are reflected by nonsynaptic changes. Since information in neural networks is ultimately conveyed with action potentials, scaling of neuronal excitability could significantly enhance or dampen the outcome of dendritic integration, boost neuronal information storage capacity and ultimately learning. However, the underlying mechanism is poorly understood. With this regard, several lines of evidence and our most recent study support a view that activity of extracellular proteases might affect information processing in neuronal networks by affecting targets beyond synapses. Here, we review the most recent studies addressing the impact of extracellular proteolysis on plasticity of neuronal excitability and discuss how enzymatic activity may alter input-output/transfer function of neurons, supporting cognitive processes. Interestingly, extracellular proteolysis may alter intrinsic neuronal excitability and excitation/inhibition balance both rapidly (time of minutes to hours) and in long-term window. Moreover, it appears that by cleavage of extracellular matrix (ECM) constituents, proteases may modulate function of ion channels or alter inhibitory drive and hence facilitate active participation of dendrites and axon initial segments (AISs) in adjusting neuronal input/output function. Altogether, a picture emerges whereby both rapid and long-term extracellular proteolysis may influence some aspects of information processing in neurons, such as initiation of action potential, spike frequency adaptation, properties of action potential and dendritic backpropagation.

Fast gamma oscillations are generated intrinsically in CA1 without the involvement of fast-spiking basket cells.

Information processing in neuronal networks relies on the precise synchronization of ensembles of neurons, coordinated by the diverse family of inhibitory interneurons. Cortical interneurons can be usefully parsed by embryonic origin, with the vast majority arising from either the caudal or medial ganglionic eminences (CGE and MGE). Here, we examine the activity of hippocampal interneurons during gamma oscillations in mouse CA1, using an in vitro model where brief epochs of rhythmic activity were evoked by local application of kainate. We found that this CA1 KA-evoked gamma oscillation was faster than that in CA3 and, crucially, did not appear to require the involvement of fast-spiking basket cells. In contrast to CA3, we also found that optogenetic inhibition of pyramidal cells in CA1 did not significantly affect the power of the oscillation, suggesting that excitation may not be essential for gamma genesis in this region. We found that MGE-derived interneurons were generally more active than CGE interneurons during CA1 gamma, although a group of CGE-derived interneurons, putative trilaminar cells, were strongly phase-locked with gamma oscillations and, together with MGE-derived axo-axonic and bistratified cells, provide attractive candidates for being the driver of this locally generated, predominantly interneuron-driven model of gamma oscillations.

Anatomical constraints on lateral competition in columnar cortical architectures.

Competition is a well-studied and powerful mechanism for information processing in neuronal networks, providing noise rejection, signal restoration, decision making and associative memory properties, with relatively simple requirements for network architecture. Models based on competitive interactions have been used to describe the shaping of functional properties in visual cortex, as well as the development of functional maps in columnar cortex. These models require competition within a cortical area to occur on a wider spatial scale than cooperation, usually implemented by lateral inhibitory connections having a longer range than local excitatory connections. However, measurements of cortical anatomy reveal that the spatial extent of inhibition is in fact more restricted than that of excitation. Relatively few models reflect this, and it is unknown whether lateral competition can occur in cortical-like networks that have a realistic spatial relationship between excitation and inhibition. Here we analyze simple models for cortical columns and perform simulations of larger models to show how the spatial scales of excitation and inhibition can interact to produce competition through disynaptic inhibition. Our findings give strong support to the direct coupling effect-that the presence of competition across the cortical surface is predicted well by the anatomy of direct excitatory and inhibitory coupling and that multisynaptic network effects are negligible. This implies that for networks with short-range inhibition and longer-range excitation, the spatial extent of competition is even narrower than the range of inhibitory connections. Our results suggest the presence of network mechanisms that focus on intra-rather than intercolumn competition in neocortex, highlighting the need for both new models and direct experimental characterizations of lateral inhibition and competition in columnar cortex.

Homeostatic structural plasticity increases the efficiency of small-world networks.

In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization.

Channelopathies and dendritic dysfunction in fragile X syndrome

Dendritic spine abnormalities and the metabotropic glutamate receptor theory put the focus squarely on synapses and protein synthesis as the cellular locus of fragile X syndrome. Synapses however, are only partly responsible for information processing in neuronal networks. Neurotransmitter triggered excitatory postsynaptic potentials (EPSPs) are shaped and integrated by dendritic voltage-gated ion channels. These EPSPs, and in some cases the resultant dendritic spikes, are further modified by dendritic voltage-gated ion channels as they propagate to the soma. If the resultant somatic depolarization is large enough, action potential(s) will be triggered and propagate both orthodromically down the axon, where it may trigger neurotransmitter release, and antidromically back into the dendritic tree, where it can activate and modify dendritic voltage-gated and receptor activated ion channels. Several channelopathies, both soma-dendritic (L-type calcium channels, Slack potassium channels, h-channels, A-type potassium channels) and axo-somatic (BK channels and delayed rectifier potassium channels) were identified in the fmr1-/y mouse model of fragile X syndrome. Pathological function of these channels will strongly influence the excitability of individual neurons as well as overall network function. In this chapter we discuss the role of voltage-gated ion channels in neuronal processing and describe how identified channelopathies in models of fragile X syndrome may play a role in dendritic pathophysiology.

Neuronal network information processing through heterogeneities and resonance frequency shifts

How can groups of neurons selectively encode different memories? We investigated a possible mechanism for the selective activation of regions of a network based on the resonance properties of individual neurons and heterogeneities in the network connectivity. In network simulations of coupled resonate and fire neurons, we incorporated the experimentally observed phenomena of resonance frequency shift based on membrane voltage changes. We aim to understand to what extent the resonance frequency shift allows for the separation of signals. We find that formation of neuronal subgroups, whether through higher connectivity strength or number of connections can lead to different activation properties from the rest of the network.

Activity-dependent Synaptic Plasticity and Metaplasticity in Spinal Motor Networks

Throughout life, neuronal network properties are modulated according to both external and internal stimuli. These adaptive capabilities of the central nervous system (CNS) have been generically termed "plasticity". One prominent form of CNS plasticity is the capability of synapses to change their strength. Synaptic strength is not a constant value but depends at each moment on the synapse's past activity. These changes in transmission efficacy are called activity-dependent synaptic plasticity (ADSP) and result in an increase (potentiation) or a decrease (depression) in synaptic strength. The ability of synapses to express one type of ADSP can change as a function of previous plasticity and previous activation of synapses. This plasticity of synaptic plasticity has been termed metaplasticity. ADSP and metaplasticity are now regarded as essential mechanisms for normal information processing in neuronal networks. Rhythmic activities such as locomotion are generated by rhythmically active central neuronal networks called central pattern generators (CPGs) that possess the intrinsic ability to generate rhythmic and organized activity in the absence of sensory inputs. The CPG activity arises from a complex dynamic interaction between synaptic transmission, intrinsic membrane properties and neuromodulatory inputs. A growing body of evidence suggests that the spinal cord matches the plastic and metaplastic properties found in other parts of the CNS, both under normal conditions and after spinal cord injury. Here, findings describing ADSP and its neuromodulation in vertebrate sensorimotor networks are reviewed, followed by a discussion of the potential role of ADSP and neuromodulation in the physiology and pathophysiology of motor circuit assembly.

Photopatterning of self-assembled poly (ethylene) glycol monolayer for neuronal network fabrication

The ability to culture individual neurons and direct their connections on functional interfaces provides a platform for investigating information processing in neuronal networks. Numerous methods have been used to design ordered neuronal networks on microelectrode arrays (MEAs) for neuronal electrical activities recording. However, so far, no method has been implemented, which simultaneously provides high-resolution neuronal patterns and low-impedance microelectrode. To achieve this goal, we employed a chemical vapor-deposited, non-fouling poly (ethylene) glycol (PEG) self-assembled monolayer to provide a cell repellant background on the MEAs. Photolithography, together with plasma etching of the PEG monolayer, was used to fabricate different patterns on MEAs. No electrode performance degradation was observed after the whole process. Dissociated cortical neurons were cultured on the modified MEAs, and the patterns were maintained for more than 3 weeks. Spontaneous and evoked neuronal activities were recorded. All of the results demonstrate this surface engineering strategy allows successful patterning of neurons on MEAs, and is useful for future studies of information processing in defined neuronal networks on a chip.

Dependence of delay-induced coherence resonance on time-periodic coupling strength in Newman-Watts neuronal networks

Recently, delay-induced coherence resonance (CR) in neuronal networks with fixed coupling strength has received much attention. In this paper, we study delay-induced CR in Newman-Watts neuronal networks with time-periodic coupling strength, mainly investigating how CR changes with the varying frequency of time-periodic coupling strength. We find that delay-induced CR become more frequent as the frequency is increased. When the frequency exceeds a threshold value, time delay can induce multiple CR more than for constant coupling strength. Furthermore, delay-induced CR occurs more abruptly and becomes more pronounced for time-periodic coupling strength than for constant coupling strength. These results show that delay-induced CR strongly depends on the coupling strength of neurons, and time delay can optimize spiking coherence more frequently and precisely in the presence of time-periodic coupling strength. This implies that time-periodic coupling strength could be more helpful for time delay to enhance and optimize the spiking coherence, and thus it may play a more efficient role in improving the time precision of information processing in neuronal networks.

Multiple coherence resonances by time-periodic coupling strength in scale-free networks of bursting neurons

In this paper, we study the effect of time-periodic coupling strength (TPCS) and network connection degree ⟨k⟩ on the temporal coherence of the chaotic bursting of the scale-free networks of thermo-sensitive neurons. It is found that the chaotic bursting becomes ordered and can exhibit coherence resonance (CR) when TPCS amplitude e0 or the network connection degree ⟨k⟩ is varied. In particular, the neuronal bursting may exhibit multiple CR (MCR) behavior when TPCS frequency ω is varied. It is also found that, as ⟨k⟩ is increased, the value of e0 for the MCR decreases, but the frequency for the MCR almost keeps unchanged. These results show that the chaotic bursting can be tamed and the bursting temporal coherence can be enhanced and even optimized by TPCS and network connection degree. Furthermore, TPCS can repetitively enhance and even optimize the temporal coherence of the neuronal bursting behavior. These findings may help to better understand the roles of TPCS and network connection degree for improving the time precision of the information processing in neuronal networks.