Cognitive Neurodynamics

We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative analysis, in each channel, we obtain the competition degree [C]d, given by the ratio of strength of DP ([S]DP) to strength of IP ([S]IP) (i.e., [C]d = [S]DP /[S]IP). Then, a desired action is selected in the channel with the largest [C]d. Desired action selection is made mainly due to strong focused inhibitory projection to the output nucleus, SNr (substantia nigra pars reticulata) via the DP in the corresponding channel. Unlike the case of DP, there are two types of IPs; intra-channel IP and inter-channel IP, due to widespread diffusive excitation from the STN (subthalamic nucleus). The intra-channel IP serves a function of brake to suppress the desired action selection. In contrast, the inter-channel IP to the SNr in the neighboring channels suppresses competing actions, leading to highlight the desired action selection. In this way, function of the inter-channel IP is opposite to that of the intra-channel IP. However, to the best of our knowledge, no quantitative analysis for such functions of the DP and the two IPs was made. Here, through direct calculations of the DP and the intra- and the inter-channel IP presynaptic currents into the SNr in each channel, we obtain the competition degree of each channel to determine a desired action, and then functions of the DP and the intra- and inter-channel IPs are quantitatively made clear. PACS numbers87.19.lj, 87.19.lu, 87.19.rs

EEG signals of healthy individuals and epileptic patients, when treated as time series of evolving dynamical systems, are found to display characteristic differences in the behavior of the unstable periodic orbits (UPO), marking the transition from regular periodic variations to self-similar dynamics. The UPO, manifesting as broad resonances in the Fourier power spectra, are quite prominent in their presence in the normal signals and are either absent or considerably weakened with a shift towards lower frequency in the epileptic condition. The weighted average and visibility power computed for the UPO region are found to distinguish epileptic seizure from healthy individuals EEG. Remarkably, the unstable periodic motion for healthy ones is well described by damped harmonic motion, the orbits displaying smooth dynamics. In contrast, the epileptic cases show bi-stability and piecewise linear motion for the larger orbits, exhibiting large sudden jumps in the velocity (referred to the rate of change of the EEG potentials), characteristically different from the healthy cases, highlighting the efficacy of the UPO as biomarkers. For both the regions, 8-14Hz UPO and 40-45Hz resonance, we used data driven analysis to derive the system dynamics in terms of sinusoidal functions, which reveal the presence of higher harmonics, confirming nonlinearity of the underlying system and leading to quantification of the discernible differences between the healthy and epileptic patients. The gamma wave region in the 40-45Hz range, connecting the conscious and the unconscious states of the brain, reveals well-structured coherence phenomena, in addition to the prominent resonance, which potentially can be used as a biomarker for the epileptic seizure. The wavelet scalogram analysis for both UPO and 40-45Hz region also clearly differentiates the healthy condition from epileptic seizure, confirming the above dynamical picture, depicting the higher harmonic generation, and intermixing of different modes in these two regions of interest. SignificanceUnstable periodic orbits are demonstrated as faithful biomarkers for detecting seizure, being prominently present in the Fourier power spectra of the EEG signals of the healthy individuals and either being absent or significantly suppressed for the epileptic cases, showing distinctly different behavior for the unstable orbits, in the two cases. A phase space study, with EEG potential and its rate of change as coordinate and corresponding velocity, clearly delineates the dynamics in healthy and diseased individuals, demonstrating the absence or weakening of UPO, that can be a reliable bio-signature for the epileptic seizure. The phase-space analysis in the gamma region also shows specific signatures in the form of coherent oscillations and higher harmonic generation, further confirmed through wavelet analysis.

Dopamine (DA) signals play critical roles in reward-related behavior, decision making, and learning. Yet the mainstream notion that DA signals are encoded by the temporal dynamics of individual DA cell activity is increasingly contested with data supporting that DA signals prefer to be encoded by the spatial organization of DA neuron populations. However, how distributed and parallel excitatory afferent inputs simultaneously induce burst synchronization (BS) is unclear. Our previous work implies that the burst could presumably transition from an integrator to a resonator if the excitatory inputs increase further. Here the responses of networked DA neurons to different intensity of excitatory inputs are investigated. It is found that as NMDA conductance increases, the network will transition from resting state to burst asynchronization (BA) state and then to BS state, showing a bounded BA and BS region in the NMDA conductance space. Furthermore, it is found that as muscarinic receptors modulated Ca2+ dependent cationic (CAN) conductance increases, both boundaries between resting and BA, and between BA and BS gradually decrease. Phase plane analysis on DA reduced model unveils that the burst transition to a resonator underpins the changes in the network dynamics. Slow-fast dissection analysis on DA full model uncovers that the underlying mechanism of the roles and synergy of NMDA and muscarinic receptors in inducing the burst transition emerge from the enlargement of nonlinear positive feedback relationship between more Ca2+ influx provided by additional NMDA current and more ICAN modulated by added muscarinic receptors. Moreover, the lag in DA volume transmission has no effect on excitatory inputs-elicited resonator BS except for requiring more excitatory inputs. These findings shed new lights on understanding the collective behavior of DA cells population regulated by the distributed excitatory inputs, and might provide a new perspective for understanding the abnormal DA release in pathological states. Author summaryThe importance of DA signals is beyond doubt, so their encoding mechanism has very important biological significance and draws widespread attention. Yet the mainstream notion that DA cells individual provide a uniform, broadly distributed signal is increasingly contested with data supporting both homogeneity across dopamine cell activity and diversity in DA signals in target regions. Our article proposes that diverse distributed and parallel excitatory inputs can not only regulate the temporal dynamics of individual DA cell activity, but also simultaneously and synergistically regulate the network dynamics of DA cell populations by changing the local dynamics of DA cells, namely the burst transition from integrators to resonators. According to our perspective, many data that are difficult to interpret by the notion of the DA neuron individual coding can be well explained, such as burst asynchronization coding DA ramping signals, the scale of burst synchronization coding the amplitude of phase DA release, inhibitory DA autoreceptors facilitating resonator burst synchronization by postinhibitory rebound, etc. This study aims to elucidate the working mechanism of the DA system in physiological states such as positive reinforcement, and then to provide a new research perspective and foundation for understanding the abnormal DA release in pathological states.

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than its unidirectional counterpart in terms of accuracy in predicting the brain states.

Perceptual alternation in human binocular rivalry is more likely to occur during certain respiratory phases. In this paper, we show that the respiration dependence of perceptual alternations can be reproduced by a randomly connected recurrent neural network coupled with respiration relevant information via a neuromodulator of norepinephrine (NA). We considered two models of NA modulations; NA increases or decreases the nonlinearity of the activation function of neurons, and we found that the shape of the likelihood function of perceptual alternation depends only on respiratory phase, regardless of whether NA increases or decreases neural nonlinearity. Our results suggest that periodic neuromodulation facilitates the switching of competing neural states in specific phases and that this effect is independent of the excitatory or inhibitory effect of NA.

The spatial distribution of electroencephalography (EEG) oscillatory power and its temporal transitions are widely recognized as indicators of cognitive processes and pathological conditions, termed as microstates. These microstates reflect whole-brain neural network dynamics, including deep brain regions, and are closely associated with large-scale networks such as the default mode network. The conventional approach to microstate analysis relies on the envelope of EEG oscillations, which corresponds to the instantaneous amplitude. In this study, we aimed to extend conventional microstate analysis by integrating the instantaneous amplitude (power component) and instantaneous frequency data derived from the Hilbert transform. While our previous studies demonstrated that instantaneous frequency also reflects brain activity, this study highlights that integrating both features enables a more comprehensive assessment of aging effects. This integration allows the identification of brain states that cannot be detected using conventional power-based microstate analysis. Our findings suggest that this approach expands and enhances traditional microstate analysis and offers a novel index for detecting brain states. This method has the potential to provide new insights into neural network dynamics and can be applied to the study of cognitive processes and pathological conditions.

Both the imbalance of neuronal excitation and inhibition, and the network disorganization may lead to hyperactivity in epilepsy. However, the insufficiency of seizure data poses the challenge of elucidating the network mechanisms behind the frequent and recurrent abnormal discharges. Our study of two extensive intracranial EEG datasets revealed that the seizure onset zone exhibits recurrent synchronous activation of interictal events. These synchronized discharges formed repetitive sequential patterns, indicative of a stable and intricate network structure within the seizure onset zone (SOZ). We hypothesized that the frequent replay of interictal sequential activity shapes the structure of the epileptic network, which in turn supports the occurrence of these discharges. The Hopfield-Kuramoto oscillator network model was employed to characterize the formation and evolution of the epileptic network, encoding the interictal sequential patterns into the network structure using the Hebbian rule. This model successfully replicated patient-specific interictal sequential activity. Dynamic change of the network connections was further introduced to build an adaptive Kuramoto model to simulate the interictal to ictal transition. The Kuramoto oscillator network with adaptive connections (KONWAC) model we proposed essentially combines two scales of Hebbian plasticity, shaping both the stereotyped propagation and the ictal transition in epileptic networks through the interplay of regularity and uncertainty in interictal discharges.

The rapid computation of re-playable memories within the hippocampus in the form of spike sequences is a near computer-like operation. Information can be encoded once during the initial experience, and replayed numerous times after in a compressed-time representation [1-8]. Theta oscillations, sharp-wave ripples, and attractor dynamics have been posited to collectively play a role in the formation and replay of memories. However, the precise interplay between these dynamical states remains elusive. Here, we show that the memory formation dynamics and operations of the hippocampus are not just computer-like, but map directly onto the dynamics and operations of a disk-drive. We constructed a tripartite spiking neural network model where the hippocampus is explicitly described as a disk drive with a rotating disk, an actuator arm, and a read/write head. In this Neural Disk Drive (NDD) model, hippocampal oscillations map to disk rotations in the rotating disk network while attractor dynamics in the actuator arm network point to "tracks" (spike assemblies) on the disk. The read/write head then writes information onto these tracks, which have temporally-structured spikes. Tracks can be replayed during hippocampal ripples for consolidation. We confirmed the existence of interneuron-ring-sequences, predicted by the rotating disk network, in experimental data. Our results establish the hippocampus as a brain region displaying explicit, computer-like operations. Based on the known interactions between the hippocampus and other brain areas, we anticipate that our results may lead to additional models that revisit the hypothesis that the brain performs explicit, computer-like operations.

Complex spiral traveling waves observed experimentally occur across the cortex. The underlying mechanisms responsible for generating such mesoscopic activity are not well understood. Understanding how local cortical neuronal populations interact to produce emergent spiral dynamics during cognitive processing remains unknown. Therefore, to bridge this gap, a spatiotemporal cortical field rate model of local cortical circuits, composed of excitatory and three distinct time-scale inhibitory populations, is proposed. This model is extended to a two-dimensional cortical sheet, consisting of both nonlinear local interactions and diffusive global coupling, with distance-dependent axonal delays. Simulation results indicate mixed-mode oscillations occur in the local circuits, which may represent the coexistence of multiple rhythms and show the emergence of complex dynamics, such as rotating spirals with annihilation events. Spiral waves differentially respond to the strength of the grating input stimulus and exhibit working memory-like characteristics. Also hypothesize that local patterns, such as planar, source, sink, or concentric across the cortex, might be an inherent integral part of the spiral state dynamics.

Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

This study presents a deep probabilistic spiking neural network designed to extract discriminative spatiotemporal features from EEG signals associated with rightward and leftward auditory attention. The network self-organizes its synaptic weights and inter-layer connectivity through a biologically inspired learning rule shaped by probabilistic feedback inhibition and spike-based synchronization dynamics. To characterize the models behavior and identify optimal operating conditions, we systematically examined the effects of key parameters including inhibition strength, synchronization parameters, and preprocessing thresholds on network stability and classification performance. Simulation results show that feedback inhibition is essential for preventing uncontrolled synaptic growth, maintaining sparse connectivity, and enabling stable learning. Strong inhibition suppresses connectivity and weight development, whereas weak inhibition leads to excessive synaptic expansion. An intermediate inhibition regime achieves a balance between adaptability and stability, resulting in the most effective feature extraction. Output-layer neurons exhibited heterogeneous activation dynamics, reflecting diverse encoding of EEG input patterns. The excitatory firing probability was tightly modulated by the inhibition parameter, confirming its central role in shaping network responses. Synchronization parameters further influenced synaptic dynamics, producing nonlinear effects on spike coordination and weight evolution. Classification accuracy peaked at intermediate synchronization levels, revealing an optimal regime where inhibition and synchronization jointly support efficient classification of rightward and leftward EEG data. Additional analyses demonstrated that synaptic pruning substantially improves accuracy across all inhibition levels and that the model performs best when input spikes are generated using a preprocessing threshold of 0.3. Moreover, experimental results demonstrate that the developed neural network model achieves an average accuracy of 90% while utilizing only 10% of the available EEG data. Overall, the findings show that the proposed spiking neural network achieves its highest classification performance under moderate inhibition, intermediate synchronization, sparse connectivity, and appropriate pruning. These results highlight the promise of biologically inspired spiking architectures for decoding EEG-based attentional states, particularly in settings with limited training data and strong temporal structure.

Motor cortex modeling is crucial for understanding movement planning and execution. While interconnected recurrent neural networks have successfully described the dynamics of neural population activity, most existing methods utilize continuous signal-based neural networks, which do not reflect the biological spike neural signal. To address this limitation, we propose a recurrent spike neural network to simulate motor cortical activity during an arm-reaching task. Specifically, our model is built upon integrate-and-fire spiking neurons with conductance-based synapses. We carefully designed the interconnections of neurons with two different firing time scales - "fast" and "slow" neurons. Experimental results demonstrate the effectiveness of our method, with the models neuronal activity in good agreement with monkeys motor cortex data at both single-cell and population levels. Quantitative analysis reveals a correlation coefficient 0.89 between the models and real data. These results suggest the possibility of multiple timescales in motor cortical control.

The ability to anticipate changes in blood glucose (BG) concentration would have a great impact on Type 1 diabetics (T1D). In order to create T1D treatment plans, patients collect a BG concentration time series. It has been demonstrated that various types of recurrent neural networks, such as Long Short Term Memory (LSTM), have success forecasting T1D BG concentrations. However, limited work has been done to characterize the T1D time series or set limits on neural networks predictive capacity. In this work, a T1D patients 14 day BG concentration time series is studied. First, I test the time series stationarity. Then I use auto-correlation analysis, spectral analysis, and Gaussian process regression to characterize the T1D BG time series. Finally, the LSTMs prediction quality is quantified and interpreted at different prediction intervals. The success or failure of the LSTMs predictions are interpreted using the characterization of the time series.

Model tuning with the optimization of pipeline configuration is a well-established practice for the development of machine learning models. However, this often entails an exhaustive search process, especially as the parameter space expands with increasing model complexity. In the emerging field of quantum machine learning (QML), there is limited literature on the effects of configuration parameters, especially quantum-specific ones, and their choices on model performance. To address this gap, here we present a study exploring the impacts of data scaling and configuration parameters in quantum neural network (QNN) development using beta regression. Our experiments with two benchmark datasets showed that a well-tuned QNN can achieve predictive performance comparable to its classical counterparts. Our findings also demonstrate useful reference points of QNN model tuning to support a more efficient parameter optimization process.

This is a study outlining the regularity of action potential spikes. Through a stochastic study, we observed a series of strong correlations between the intervals of tonically firing spikes generated by injecting constant currents of varying intensities into layer V pyramidal neurons of the ferret medial prefrontal cortex. Based on this, we derived a formulaic relationship for the interspike intervals (ISIs). According to this formula, an ISI can be expressed as a product of two factors: the timing precursor and the scale factor. Those arise from a linear relationship between activities of ion channels that modulate spike frequency adaptation and spike timing. Using this rule, we successfully predicted spike timing and demonstrated that the spike timing can be determined by the linear combination of various ion channel activities, reflecting different cellular signaling pathways such as G-protein coupled receptor (GPCR) activation. These findings not only aid studies on cellular signaling but also expand our insight into neural coding, while increasing research efficacy through neural modeling. Significant StatementWhile the action potential (AP) pattern may appear simple at first glance, no rule has been discovered in the nearly 100 years since it was first recorded. Building on this finding, we have developed a method to intuitively measure the activity of various ion channels responsible for determining spike timing from the AP spikes, as well as the associated intracellular and extracellular signaling pathways.

Recently, studies have shown that the alpha band (8-13 Hz) EEG signals enable the decoding of auditory spatial attention. However, deep learning methods typically requires a large amount of training data. Inspired by "sparse coding" in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The model is composed of three neural layers, two of them are spiking neurons. We formulate a new learning rule that is based on firing rate of pre-synaptic and post-synaptic neurons in the first layer and the second layer of spiking neurons. The third layer consists of 10 spiking neurons that the pattern of their firing rate after training is used in test phase of the method. The proposed method extracts the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train network to detect the auditory spatial attention. In addition, a computational approach is presented to find the best single-trial EEG data as training samples of leftward and rightward attention EEG. In this model, the role of using low connectivity rate of the layers and specific range of learning parameters in sparse coding is studied. Importantly, unlike most prior model, our method requires 10% of EEG data as training data and has shown 90% accuracy in average. This study suggests new insights into the role of sparse coding in both biological networks and brain-inspired machine learning.

Although most previous studies in cognitive neuroscience have focused on the change of the neuronal firing rate under various conditions, there has been increasing evidence that indicates the importance of neuronal oscillatory activities in cognition. In the visual cortex, specific time-frequency bands are thought to have selectivity to visual stimuli. Furthermore, several recent studies have shown that several time-frequency bands are related to frequency-specific feedforward or feedback processing in inter-areal communication. However, few studies have investigated detailed visual selectivity of each time-frequency band, especially in the primate inferior temporal cortex (ITC). In this work, we analyze frequency-specific electrocorticography (ECoG) activities in the primate ITC by training encoding models that predict frequency-specific amplitude from hierarchical visual features extracted from a deep convolutional neural network (CNNs). We find that ECoG activities in two specific time-frequency bands, theta (around 5 Hz) and gamma (around 20-25 Hz) bands, are better predicted from CNN features than the other bands. Furthermore, theta- and gamma-band activities are better predicted from higher and lower layers in CNNs, respectively. Our visualization analysis using CNN-based encoding models qualitatively show that theta- and gamma-band encoding models have selectivity to higher- and lower-level visual features, respectively. Our results suggest that neuronal oscillatory activities in theta and gamma bands carry distinct information in the hierarchy of visual features, and that distinct levels of visual information are multiplexed in frequency-specific brain signals.

Accumulating evidence suggest that general anesthetics with diverse chemical structure reduce cerebral metabolism with consequent reduction of intracellular adenosine triphosphate (ATP) levels. How cerebral hypometabolism is associated with the typical electroencephalographic (EEG) changes under general anesthesia remains largely unknown.. We hypothesized that the deficit in ATP production would reduce high-frequency activity, increase low-frequency activity, and cause burst suppression, which are common dose-dependent anesthetic effects on the EEG. To test the hypothesis, we developed a novel neural network model consisting of leaky integrate-and-fire neurons with additional dependency on ATP dynamics. The effect of varying rate of ATP production on neuronal and population activity patterns was simulated under various excitatory/inhibitory balance conditions. A decrease of ATP production suppressed neuronal spiking and enhanced synchronization of neurons over a range of excitatory/inhibitory synaptic strength ratios. As anticipated, the initially asynchronous fast activity was replaced by globally desynchronized slow oscillation and, on further decrease of ATP production, changed into burst suppression with enhanced global synchronization. This study substantiates a novel biophysical mechanism for anesthetic-induced EEG changes through a relationship between energy production and synchronization of neural network.

Periodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8-12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear entrainment and resonance, and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

Electroencephalography (EEG) microstates constitute temporal map configurations that reflect the whole brain electrical state. The dynamics of EEG microstates may serve as an effective discretization method for capturing spatiotemporally continuous neural dynamics with high temporal resolution. In this study, we employed polarity-sensitive microstate analysis to investigate whole-brain state transitions during audiovisual oddball tasks. Moreover, we examined how sensory modality and its coupling, types of response to target stimuli, and the physical presence or absence of target stimuli affected EEG dynamics. The results demonstrated that the abovementioned factors affected both behavioral indices and the event-related potential (ERP) components, particularly the P300. Importantly, when considering the topographical polarity of map configuration, transitions to microstate E-, which originates within 300 to 600 ms after stimulus onset and coincides with the typical latency of the P300 component potentially reflect attentional and conscious processes that are associated with the P300. These novel insights into the dynamic transitions of whole brain states during cognitive processes complement the results of traditional ERP analyses.