A fuzzy-oscillatory model of medial prefrontal cortex control function in spatial memory retrieval in human navigation function

Navigation can be broadly defined as the process of moving from an origin to a destination through path-planning. Previous research has shown that navigation is mainly related to the function of the medial temporal lobe (MTL), including the hippocampus (HPC), and medial prefrontal cortex (mPFC), which controls retrieval of the spatial memories from this region. In this study, we suggested a cognitive and computational model of human navigation with a focus on mutual interactions between the hippocampus (HPC) and the mPFC using the concept of synchrony. The Van-der-pol oscillator was used to model the synchronous process of receiving and processing “what stream” information. A fuzzy lookup table system was applied for modeling the controlling function of the mPFC in retrieving spatial information from the HPC. The effect of attention level was also included and simulated. The performance of the model was evaluated using information reported in previous experimental research. Due to the inherent stability of the proposed fuzzy-oscillatory model, it is less sensitive to the exact values of the initial conditions, and therefore, it is shown that it is consistent with the actual human performance in real environments. Analyzing the proposed cognitive and fuzzy-oscillatory computational model demonstrates that the model is able to reproduce certain cognitive and functional disturbances in navigation in related diseases such as Alzheimer’s disease (AD). We have shown that an increase in the bifurcation parameter of the Van-der-pol equation represents an increase in the low-frequency spectral power density and a decrease in the high-frequency spectral power as occurs in AD due to an increase in the amyloid plaques in the brain. These changes in the frequency characteristics of neuronal activity, in turn, lead to impaired recall and retrieval of landmarks information and learned routes upon encountering them. As a result, and because of the wrong frequency code being transmitted, the relevant set of rules in the mPFC is not activated, or another unrelated set will be activated, which leads to forgetfulness and erroneous decisions in routing and eventually losing the route in Alzheimer’s patients.

Sequential and efficient neural-population coding of complex task information

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.

A mathematical model to mimic the shape of event related desynchronization/synchronization

Rhythmic oscillatory activities of the sensory cortex have been observed after a presentation of a stimulus. This activity first drops dramatically and then increases considerably that are respectively named event-related desynchronization (ERD) and event-related synchronization (ERS). There are several effective factors that can alter the ERD and ERS pattern. In this study, a mathematical model was presented that produced ERD and ERS pattern in response to a stimulus. This model works based on the synchronization concepts. The proposed model provided different suggestions about the reason behind the relationship between the encoding of incoming sensory information and the oscillatory activities, effective factors on the characteristics of neuronal units, and how may these factors affect the amplitude and latency of the ERD and ERS.

Are Neurodynamic Organizations A Fundamental Property of Teamwork?

When performing a task it is important for teams to optimize their strategies and actions to maximize value and avoid the cost of surprise. The decisions teams make sometimes have unintended consequences and they must then reorganize their thinking, roles and/or configuration into corrective structures more appropriate for the situation. In this study we ask: What are the neurodynamic properties of these reorganizations and how do they relate to the moment-by-moment, and longer, performance-outcomes of teams?. We describe an information-organization approach for detecting and quantitating the fluctuating neurodynamic organizations in teams. Neurodynamic organization is the propensity of team members to enter into prolonged (minutes) metastable neurodynamic relationships as they encounter and resolve disturbances to their normal rhythms. Team neurodynamic organizations were detected and modeled by transforming the physical units of each team member's EEG power levels into Shannon entropy-derived information units about the team's organization and synchronization. Entropy is a measure of the variability or uncertainty of information in a data stream. This physical unit to information unit transformation bridges micro level social coordination events with macro level expert observations of team behavior allowing multimodal comparisons across the neural, cognitive and behavioral time scales of teamwork. The measures included the entropy of each team member's data stream, the overall team entropy and the mutual information between dyad pairs of the team. Mutual information can be thought of as periods related to team member synchrony. Comparisons between individual entropy and mutual information levels for the dyad combinations of three-person teams provided quantitative estimates of the proportion of a person's neurodynamic organizations that represented periods of synchrony with other team members, which in aggregate provided measures of the overall degree of neurodynamic interactions of the team. We propose that increased neurodynamic organization occurs when a team's operating rhythm can no longer support the complexity of the task and the team needs to expend energy to re-organize into structures that better minimize the "surprise" in the environment. Consistent with this hypothesis, the frequency and magnitude of neurodynamic organizations were less in experienced military and healthcare teams than they were in more junior teams. Similar dynamical properties of neurodynamic organization were observed in models of the EEG data streams of military, healthcare and high school science teams suggesting that neurodynamic organization may be a common property of teamwork. The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks and training protocols.

Developmental Inhibition of Gsk3 Rescues Behavioral and Neurophysiological Deficits in a Mouse Model of Schizophrenia Predisposition

While the genetic basis of schizophrenia is increasingly well characterized, novel treatments will require establishing mechanistic relationships between specific risk genes and core phenotypes. Rare, highly penetrant risk genes such as the 22q11.2 microdeletion are promising in this regard. Df(16)A(+/-) mice, which carry a homologous microdeletion, have deficits in hippocampal-prefrontal connectivity that correlate with deficits in spatial working memory. These mice also have deficits in axonal development that are accompanied by dysregulated Gsk3β signaling and can be rescued by Gsk3 antagonists. Here we show that developmental inhibition of Gsk3 rescues deficits in hippocampal-prefrontal connectivity, task-related neural activity, and spatial working memory behavior in Df(16)A(+/-) mice. Taken together, these results provide mechanistic insight into how the microdeletion results in cognitive deficits, and they suggest possible targets for novel therapies.

Modeling the neurodynamic organizations and interactions of teams

Across-brain neurodynamic organizations arise when teams perform coordinated tasks. We describe a symbolic electroencephalographic (EEG) approach that identifies when team neurodynamic organizations occur and demonstrate its utility with scientific problem solving and submarine navigation tasks. Each second, neurodynamic symbols (NS) were created showing the 1-40 Hz EEG power spectral densities for each team member. These data streams contained a performance history of the team's across-brain neurodynamic organizations. The degree of neurodynamic organization was calculated each second from a moving window average of the Shannon entropy over the task. Decreased NS entropy (i.e., greater neurodynamic organization) was prominent in the ~16 Hz EEG bins during problem solving, while during submarine navigation, the maximum NS entropy decreases were ~10 Hz and were associated with establishing the ship's location. Decreased NS entropy also occurred in the 20-40 Hz bins of both teams and was associated with uncertainty or stress. The highest mutual information levels, calculated from the EEG values of team dyads, were associated with decreased NS entropy, suggesting a link between these two measures. These studies show entropy and mutual information mapping of symbolic EEG data streams from teams can be useful for identifying organized across-brain team activation patterns.