scholarly journals Temporal signatures of criticality in human cortical excitability as probed by early somatosensory responses

2019 ◽  
Author(s):  
T. Stephani ◽  
G. Waterstraat ◽  
S. Haufe ◽  
G. Curio ◽  
A. Villringer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Neural Activity ◽  
Critical State ◽  
Cortical Excitability ◽  
Temporal Organization ◽  
Neural Responses ◽  
Scale Free ◽  
Spatio Temporal ◽  
Evoked Activity ◽  
The Impact

AbstractBrain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system’s excitability. Yet the spatio-temporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned towards the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel non-invasive approach in 33 male participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory post-synaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials – a hallmark of systems at critical states. As these dynamics covaried with changes in pre-stimulus oscillatory activity in the alpha band (8–13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.Significance StatementVariability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely if dynamics are poised at a so-called critical state characterized by a scale-free spatio-temporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: The impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatio-temporal network dynamics of spontaneous activity on the other.

scholarly journals Dynamic relationships between spontaneous and evoked electrophysiological activity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Soren Wainio-Theberge ◽  
Annemarie Wolff ◽  
Georg Northoff
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Behavioral Outcomes ◽  
Low Frequencies ◽  
Scale Free ◽  
Non Invasive ◽  
Spontaneous Neural Activity ◽  
Electrophysiological Signals ◽  
Evoked Activity ◽  
Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

Taste activity in the parabrachial region in adult rats following neonatal chorda tympani transection

2021 ◽  
Author(s):  
Louis J Martin ◽  
Joseph M. Breza ◽  
Suzanne Sollars
Keyword(s):  
Parabrachial Nucleus ◽  
Chorda Tympani ◽  
Neural Responses ◽  
Functional Plasticity ◽  
Adult Rats ◽  
High Preference ◽  
Gustatory Nerve ◽  
Evoked Activity ◽  
The Impact

The chorda tympani is a gustatory nerve that nerve fails to regenerate if sectioned in rats 10 days of age or younger. This early denervation causes an abnormally high preference for NH4Cl in adult rats, but the impact of neonatal chorda tympani transection on the development of the gustatory hindbrain is unclear. Here, we tested the effect of neonatal chorda tympani transection (CTX) on gustatory responses in the parabrachial nucleus (PbN). We recorded in vivo extracellular spikes in single PbN units of urethane-anesthetized adult rats following CTX at P5 (chronic CTX group) or immediately prior to recording (acute CTX group). Thus, all sampled PbN neurons received indirect input from taste nerves other than the CT. Compared to acute CTX rats, chronic CTX animals had significantly higher responses to stimulation with 0.1 and 0.5 M NH4Cl, 0.1 NaCl, and 0.01 M citric acid. Activity to 0.5 M sucrose and 0.01 M quinine stimulation was not significantly different between groups. Neurons from chronic CTX animals also had larger interstimulus correlations and significantly higher entropy, suggesting that neurons in this group were more likely to be activated by stimulation with multiple tastants. Although neural responses were higher in the PbN of chronic CTX rats compared to acute-sectioned controls, taste-evoked activity was much lower than observed in previous reports, suggesting permanent deficits in taste signaling. These findings demonstrate that the developing gustatory hindbrain exhibits high functional plasticity following early nerve injury.

Wavelength and vulnerability to atrial fibrillation: Insights from a computer model of human atria

EP Europace ◽  
2005 ◽  
Vol 7 (s2) ◽  
pp. S83-S92 ◽  
Author(s):  
Vincent Jacquemet ◽  
Nathalie Virag ◽  
Lukas Kappenberger
Keyword(s):  
Atrial Fibrillation ◽  
Computer Model ◽  
A Priori ◽  
Temporal Organization ◽  
A Priori Information ◽  
Clinical Methods ◽  
Spatio Temporal ◽  
Priori Information ◽  
The Impact ◽  
The Relationship

Abstract Aim To explain the contradictory results related to the concept of critical cardiac wavelength in the initiation and perpetuation of atrial fibrillation (AF). Methods A biophysically based computer model was used to: (1) study the relationship between wavelength and AF perpetuation in the presence of multiple re-entrant wavelets, (2) evaluate the performance of different existing methods for wavelength estimation in the presence of different arrhythmogenic substrates, and (3) document the impact of either heterogeneities in refractoriness or the presence of a mother rotor on wavelength estimation. Results The simulations confirmed that the wavelength must be below a critical value for AF to be sustained, when the perpetuation mechanism relies on multiple re-entrant wavelets. The estimated value of wavelength was not the same for all methods tested and depended in part on the nature of the spatio-temporal organization of the AF dynamics. Conclusion A priori information about the underlying wavelet dynamics is needed for a correct interpretation of the cardiac wavelength as estimated by the current clinical methods.

scholarly journals The Effects of Radiation on Memristor-Based Electronic Spiking Neural Networks

2020 ◽  
Author(s):  
Sumedha Gandharava Dahl
Keyword(s):  
Neural Networks ◽  
Radiation Effects ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Ability ◽  
Pattern Learning ◽  
Neuron Death ◽  
Learning Capability ◽  
Spatio Temporal ◽  
The Impact

In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used as synapses to manipulate conductivity paths in the network. Spike-timing-dependent plasticity (STDP) learning behavior results in pattern learning and is achieved using biphasic shaped pre- and post-synaptic spikes. A TiO2 based non-linear drift memristor model designed in Verilog-A implements synaptic behavior and is modified to include experimentally observed effects of state-altering, ionizing, and off-state degradation radiation on the device. The impact of neuron "death" (disabled neuron circuits) due to radiation is also examined. In general, radiation interaction events distort the STDP learning curve undesirably, favoring synaptic potentiation. At lower short-term flux, the network is able to recover and relearn the pattern with consistent training, although some pixels may be affected due to stability issues. As the radiation flux and duration increases, it can overwhelm the leaky integrate-and-fire (LIF) post-synaptic neuron circuit, and the network does not learn the pattern. On the other hand, in the absence of the pattern, the radiation effects cumulate, and the system never regains stability. Neuron-death simulation results emphasize the importance of non-participating neurons during the learning process, concluding that non-participating afferents contribute to improving the learning ability of the neural network. Instantaneous neuron death proves to be more detrimental for the network compared to when the afferents die over time thus, retaining the network's pattern learning capability.

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