Maxent_Toolbox: Maximum Entropy Toolbox For Matlab, Version 1.0.2

Maoz, Ori; Schneidman, Elad

doi:10.5281/zenodo.191625

Cited by 6 publications

(2 citation statements)

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“…Note that in MEM, the probability of occurrence that is associated with each vector (equation 10) is essentially the normalized exponential of the attributed energy. In this paper, we trained all models using a customized MATLAB function developed based on the package by Ori Maoz and Elad Schneidman (Maoz & Schneidman, 2017) and binarize the psychological measurements at a threshold of 0.1 after z-score normalization (Watanabe & Rees, 2017).…”

Section: Derivation Of Networkmentioning

confidence: 99%

Formalizing psychological interventions through network control theory

Stocker¹,

Koppe²,

Reich³

et al. 2022

Preprint

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Despite the growing deployment of network representation throughout psychological sciences, the question of whether and how networks can systematically describe the effects of psychological interventions remains elusive. Towards this end, we capitalize on recent breakthrough in network control theory, the engineering study of networked interventions, to investigate a representative psychological attitude modification experiment. This study examined 30 healthy participants who answered 11 questions about their attitude toward eating meat. They then received 11 arguments to challenge their attitude on the questions, after which they were asked again the same set of questions. Using this data, we constructed networks that quantify the connections between the responses and tested: 1) if the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network topology as described by control theory, 2) if the size of change in responses relates to whole-network topology that quantifies the “ease” of change as described by control theory, and 3) if responses after intervention could be predicted based on formal results from control theory. We found that 1) the interventions that had higher regional topological relevance (the so-called controllability scores) had stronger effect (r > 0.5), the intervention sensitivities were systematically lower for the interventions that were “easier to control” (r = -0.49), and that the model offered substantial prediction accuracy (r = 0.36). Finally, we compared results based on subjective as well as data-driven models and found that, across all tests, the results from the data-driven approach explained a larger size of effect. Taken together, our results suggest that psychological interventions can be studied using network control theory and that data-driven models provide a reliable medium.

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Section: Derivation Of Networkmentioning

confidence: 99%

Formalizing psychological interventions through network control theory

Stocker¹,

Koppe²,

Reich³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This number is then normalized by dividing by the mean firing rate of the population. Maximum entropy modeling Fitting of maximum entropy models was performed using the maxent_toolbox (Maoz and Schneidman, 2017). The central principle of a maximum entropy model is the generation of a pattern probability distribution in which the activity of individual neurons and the coactivity of pairs of neurons match those of empirical probability distribution, but for which there is no further structure.…”

Section: Micementioning

confidence: 99%

Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology

Chockanathan

Padmanabhan

2022

Preprint

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Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by a range of behavioral alterations including memory loss as well as cognitive and psychiatric symptoms. While there is growing evidence that cellular and molecular pathologies, such as the accumulation of amyloid beta plaques may contribute to AD, it remains unclear how this histopathology can give rise to such disparate behavioral deficits. One hypothesis for these diverse behavioral presentations is that amyloid beta plaques; accumulation has differential effects on neuronal circuits across brain regions, depending on the diverse neurophysiological properties and connections within and between the neurons in these different areas. To test this, we recorded from large neuronal populations in the dorsal and ventral CA1 regions of the hippocampus, areas that are known to be structurally and functionally diverse, in both APP/PS1 animals, a mouse model of Amyloid pathology, and age-matched C57BL/6 controls. Although we found similar levels of Amyloid pathology in the two subregions, populations of neurons in dorsal and ventral CA1 in APP/PS1 mice showed distinct signatures of disrupted neuronal activity as animals navigated a virtual reality environment. In dorsal CA1, pairwise correlations and entropy, a measure of the diversity of activity patterns, were decreased in the APP/PS1 mice. However, in ventral CA1, the opposite findings were observed; pair-wise correlations and entropy was increased in APP/PS1 mice relative to C57BL/6 controls. When we attempted to connect the microscopic features of population activity (the correlations) with the macroscopic features of the population code (the entropy) using a pairwise Ising model, we found that the models' performance decreased in predicting dorsal CA1 activity but increased in predicting vCA1 activity in APP/PS1 mice, as compared to the C57BL/6 animals. Taken together, these findings suggest that amyloid beta pathology exerts distinct effects across different hippocampal regions. Our results suggest that the diverse behavioral deficits associated with AD and the cellular pathology that arises from amyloid accumulation may be mechanistically linked by studying the dynamics of neural activity within these diverse hippocampal circuits.

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Dynamics of social representation in the mouse prefrontal cortex

et al. 2019

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The prefrontal cortex (PFC) plays an important role in regulating social functions in mammals, and impairments in this region have been linked with social dysfunction in psychiatric disorders. Yet little is known of how the PFC encodes social information and of how social representations may be altered in such disorders. Here, we show that neurons in the medial PFC (mPFC) of freely behaving mice preferentially respond to socially-relevant sensory cues. Population activity patterns in the mPFC differed considerably between social and nonsocial stimuli and underwent experience-dependent refinement. In Cntnap2 knockout mice, a genetic model of autism, both the categorization of sensory stimuli and the refinement of social representations were impaired.Noise levels in spontaneous population activity were higher in Cntnap2 mice, and correlated strongly with the degree to which social representations were disrupted. Our findings elucidate the encoding of social sensory cues in the mPFC, and provide an important link between altered prefrontal dynamics and autism-associated social dysfunction.

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Maxent_Toolbox: Maximum Entropy Toolbox For Matlab, Version 1.0.2

Cited by 6 publications

References 0 publications

Formalizing psychological interventions through network control theory

Formalizing psychological interventions through network control theory

Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology

Dynamics of social representation in the mouse prefrontal cortex

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