pwc-archive/papers-with-abstracts huggingface dataset:
Nerve theorems for fixed points of neural networks
Authors: Daniela Egas Santander, Stefania Ebli, Alice Patania, Nicole Sanderson, Felicia Burtscher, Katherine Morrison, Carina Curto
Date: 2021-02-23 00:00:00

Nonlinear network dynamics are notoriously difficult to understand. Here we study a class of recurrent neural networks called combinatorial threshold-linear networks (CTLNs) whose dynamics are determined by the structure of a directed graph. They are a special case of TLNs, a popular framework for modeling neural activity in computational neuroscience. In prior work, CTLNs were found to be surprisingly tractable mathematically. For small networks, the fixed points of the network dynamics can often be completely determined via a series of graph rules that can be applied directly to the underlying graph. For larger networks, it remains a challenge to understand how the global structure of the network interacts with local properties. In this work, we propose a method of covering graphs of CTLNs with a set of smaller directional graphs that reflect the local flow of activity. While directional graphs may or may not have a feedforward architecture, their fixed point structure is indicative of feedforward dynamics. The combinatorial structure of the graph cover is captured by the nerve of the cover. The nerve is a smaller, simpler graph that is more amenable to graphical analysis. We present three nerve theorems that provide strong constraints on the fixed points of the underlying network from the structure of the nerve. We then illustrate the power of these theorems with some examples. Remarkably, we find that the nerve not only constrains the fixed points of CTLNs, but also gives insight into the transient and asymptotic dynamics. This is because the flow of activity in the network tends to follow the edges of the nerve.
Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling
Authors: Bhavya Vasudeva, Runfeng Tian, Dee H. Wu, Shirley A. James, Hazem H. Refai, Fei He, Yuan Yang
Date: 2021-02-20 00:00:00

Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. $f$, $2f/3$) and across three frequencies (e.g. $f_1$, $f_2$, $f_1+f_2$), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. $f_1$, $f_2$, $f_3$, $f_4$, $f_1+f_2+f_3-f_4$), or coupling that involves non-integer multiples of the frequencies (e.g. $f_1$, $f_2$, $2f_1/3+f_2/3$). To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators. The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled R\"ossler oscillators. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems.
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Authors: Dominik Klepl, Fei He, Min Wu, Matteo De Marco, Daniel J. Blackburn, Ptolemaios Sarrigiannis
Date: 2021-02-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.
The neural dynamics underlying prioritisation of task-relevant information
Authors: Tijl Grootswagers, Amanda K. Robinson, Sophia M. Shatek, Thomas A. Carlson
Date: 2021-02-02 00:00:00

The human brain prioritises relevant sensory information to perform different tasks. Enhancement of task-relevant information requires flexible allocation of attentional resources, but it is still a mystery how this is operationalised in the brain. We investigated how attentional mechanisms operate in situations where multiple stimuli are presented in the same location and at the same time. In two experiments, participants performed a challenging two-back task on different types of visual stimuli that were presented simultaneously and superimposed over each other. Using electroencephalography and multivariate decoding, we analysed the effect of attention on the neural responses to each individual stimulus. Whole brain neural responses contained considerable information about both the attended and unattended stimuli, even though they were presented simultaneously and represented in overlapping receptive fields. As expected, attention increased the decodability of stimulus-related information contained in the neural responses, but this effect was evident earlier for stimuli that were presented at smaller sizes. Our results show that early neural responses to stimuli in fast-changing displays contain remarkable information about the sensory environment but are also modulated by attention in a manner dependent on perceptual characteristics of the relevant stimuli. Stimuli, code, and data for this study can be found at https://osf.io/7zhwp/.
Self-Organized Criticality in the Brain
Authors: Dietmar Plenz, Tiago L. Ribeiro, Stephanie R. Miller, Patrick A. Kells, Ali Vakili, Elliott L. Capek
Date: 2021-02-18 00:00:00

Self-organized criticality (SOC) refers to the ability of complex systems to evolve towards a 2nd-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance. For the last two decades, considerable experimental evidence accumulated that the mammalian cortex with its diversity in cell types and connections might exhibit SOC. Here we review experimental findings of isolated, layered cortex preparations to self-organize towards four dynamical motifs identified in the cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches that exhibit robust power law scaling in size with a slope of -3/2 and a critical branching parameter of 1. This dynamical coordination, tracked in the local field potential (nLFP) and pyramidal neuron activity using 2-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, displays separation of time scales, and reveals unique size vs. quiet time dependencies. A threshold operation identifies coherence potentials; avalanches that in addition maintain the precise time course of propagated synchrony. Avalanches emerge under conditions of external driving. Control parameters are established by the balance of excitation and inhibition (E/I) and the neuromodulator dopamine. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack cortical layers and exhibit dynamics similar to a 1st-order phase transition. The precise interactions between up-states, nested oscillations, avalanches, and coherence potentials in superficial cortical layers provide compelling evidence for SOC in the brain.
Analysis of EEG data using complex geometric structurization
Authors: Eddy Kwessi, Lloyd Edwards
Date: 2021-02-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Electroencephalogram (EEG) is a common tool used to understand brain activities. The data are typically obtained by placing electrodes at the surface of the scalp and recording the oscillations of currents passing through the electrodes. These oscillations can sometimes lead to various interpretations, depending on the subject's health condition, the experiment carried out, the sensitivity of the tools used, human manipulations etc. The data obtained over time can be considered a time series. There is evidence in the literature that epilepsy EEG data may be chaotic. Either way, the embedding theory in dynamical systems suggests that time series from a complex system could be used to reconstruct its phase space under proper conditions. In this paper, we propose an analysis of epilepsy electroencephalogram time series data based on a novel approach dubbed complex geometric structurization. Complex geometric structurization stems from the construction of strange attractors using embedding theory from dynamical systems. The complex geometric structures are themselves obtained using a geometry tool, namely the $\alpha$-shapes from shape analysis. Initial analyses show a proof of concept in that these complex structures capture the expected changes brain in lobes under consideration. Further, a deeper analysis suggests that these complex structures can be used as biomarkers for seizure changes.
Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number
Authors: William B Levy, Victoria G. Calvert
Date: 2021-02-11 00:00:00
Tasks: Computational Efficiency

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained, computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between ATP-consuming processes, especially computation \textit{per se} vs the communication processes including action potentials and transmitter release. Thus to apply this mathematical function requires an energy audit with a partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 watts of glucose available to the brain \cite{sokoloff1960metabolism,sawada2013synapse}, the fraction partitioned to cortical computation is only 0.1 watts of ATP. On the other hand at 3.5 watts, long-distance communication costs are 35-fold greater. Other novel quantifications include (i) a finding that the biological vs ideal values of neural computational efficiency differ by a factor of $10^8$ and (ii) two predictions of $N$, the number of synaptic transmissions needed to fire a neuron (2500 vs 2000).
A General Framework for Revealing Human Mind with auto-encoding GANs
Authors: Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo
Date: 2021-02-10 00:00:00
Tasks: Brain Decoding

Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image, leading to a better understanding of cognitive function. The traditional approach treats brain decoding as a classification problem, reading the mind through statistical analysis of brain activity. However, human thought is rich and varied, that it is often influenced by more of a combination of object features than a specific type of category. For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment. To find the correspondence from brain signal features to image features, we embedded them into two latent spaces with modality-specific encoders and then aligned the two spaces by minimising the distance between paired latent representations. The proposed framework was trained by simultaneous electroencephalogram and functional MRI data, which were recorded when the subjects were viewing or imagining a set of image stimuli. In this paper, we focused on implementing the fMRI experiment. Our experimental results demonstrated the feasibility of translating brain activity to an image. The reconstructed image matches image stimuli approximate in both shape and colour. Our framework provides a promising direction for building a direct visualisation to reveal human mind.
Twelve- and fourteen-year-old school children differentially benefit from sensorimotor- and multisensory-enriched vocabulary training
Authors: Brian Mathias, Christian Andrae, Anika Schwager, Manuela Macedonia, Katharina von Kriegstein
Date: 2021-02-09 00:00:00

Both children and adults have been shown to benefit from the integration of multisensory and sensorimotor enrichment into pedagogy. For example, integrating pictures or gestures into foreign language (L2) vocabulary learning can improve learning outcomes relative to unisensory learning. However, whereas adults seem to benefit to a greater extent from sensorimotor enrichment such as the performance of gestures in contrast to multisensory enrichment with pictures, this is not the case in elementary school children. Here, we compared multisensory- and sensorimotor-enriched learning in an intermediate age group that falls between the age groups tested in previous studies (elementary school children and young adults), in an attempt to determine the developmental time point at which children's responses to enrichment mature from a child-like pattern into an adult-like pattern. Twelve-year-old and fourteen-year-old German children were trained over 5 consecutive days on auditorily-presented, concrete and abstract, Spanish vocabulary. The vocabulary was learned under picture-enriched, gesture-enriched, and non-enriched (auditory-only) conditions. The children performed vocabulary recall and translation tests at 3 days, 2 months, and 6 months post-learning. Both picture and gesture enrichment interventions were found to benefit children's L2 learning relative to non-enriched learning up to 6 months post-training. Interestingly, gesture-enriched learning was even more beneficial than picture-enriched learning for the fourteen-year-olds, while the twelve-year-olds benefitted equivalently from learning enriched with pictures and gestures. These findings provide evidence for opting to integrate gestures rather than pictures into L2 pedagogy starting at fourteen years of age.
Entanglement in Cognition violating Bell Inequalities Beyond Cirel'son's Bound
Authors: Diederik Aerts, Jonito Aerts Arguëlles, Lester Beltran, Suzette Geriente, Sandro Sozzo
Date: 2021-02-07 00:00:00
Tasks: Image Retrieval, Retrieval

We present the results of two tests where a sample of human participants were asked to make judgements about the conceptual combinations {\it The Animal Acts} and {\it The Animal eats the Food}. Both tests significantly violate the Clauser-Horne-Shimony-Holt version of Bell inequalities (`CHSH inequality'), thus exhibiting manifestly non-classical behaviour due to the meaning connection between the individual concepts that are combined. We then apply a quantum-theoretic framework which we developed for any Bell-type situation and represent empirical data in complex Hilbert space. We show that the observed violations of the CHSH inequality can be explained as a consequence of a strong form of `quantum entanglement' between the component conceptual entities in which both the state and measurements are entangled. We finally observe that a quantum model in Hilbert space can be elaborated in these Bell-type situations even when the CHSH violation exceeds the known `Cirel'son bound', in contrast to a widespread belief. These findings confirm and strengthen the results we recently obtained in a variety of cognitive tests and document and image retrieval operations on the same conceptual combinations.
Adaptive Internal Models: Explaining the Oculomotor System and the Cerebellum
Authors: Mireille E. Broucke
Date: 2019-01-04 00:00:00

We propose a new model of the oculomotor system, particularly the vestibulo-ocular reflex, gaze fixation, and smooth pursuit. Our key insight is to exploit recent developments on adaptive internal models. The outcome is a simple model that includes the interactions between the brainstem and the cerebellum and that recovers behaviors from more than 15 oculomotor experiments. In addition, we put forward a thesis that the cerebellum embodies internal models of all persistent, exogenous reference and disturbance signals acting on the body and observable through the error signals it receives.
Neural circuit function redundancy in brain disorders
Authors: Beatriz E. P. Mizusaki, Cian O'Donnell
Date: 2021-02-05 00:00:00

Redundancy is a ubiquitous property of the nervous system. This means that vastly different configurations of cellular and synaptic components can enable the same neural circuit functions. However, until recently very little brain disorder research considered the implications of this characteristic when designing experiments or interpreting data. Here, we first summarise the evidence for redundancy in healthy brains, explaining redundancy and three of its sub-concepts: sloppiness, dependencies, and multiple solutions. We then lay out key implications for brain disorder research, covering recent examples of redundancy effects in experimental studies on psychiatric disorders. Finally, we give predictions for future experiments based on these concepts.
Entropic Causal Inference for Neurological Applications
Authors: Jeremie Fish, Alexander DeWitt, Abd AlRahman R. AlMomani, Paul J. Laurienti, Erik Bollt
Date: 2020-10-08 00:00:00
Tasks: Causal Inference, Functional Connectivity

The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. Key to this study is understanding structure of both the structural and functional connectivity between anatomical regions. In this paper we follow previous work in developing a simple dynamical model of the brain by simulating its various regions as Kuramoto oscillators whose coupling structure is described by a complex network. However in our simulations rather than generating synthetic networks, we simulate our synthetic model but coupled by a real network of the anatomical brain regions which has been reconstructed from diffusion tensor imaging (DTI) data. By using an information theoretic approach that defines direct information flow in terms of causation entropy (CSE), we show that we can more accurately recover the true structural network than either of the popular correlation or LASSO regression techniques. We demonstrate the effectiveness of our method when applied to data simulated on the realistic DTI network, as well as on randomly generated small-world and Erd\"os-R\'enyi (ER) networks.
Matched transient and steady-state approximation of first-passage-time distributions of coloured noise driven leaky neurons
Authors: Akke Mats Houben
Date: 2021-02-03 00:00:00

The first-passage-time distribution of a leaky integrate-and-fire neuron driven by a characteristically coloured noise is approximated by matching a transient and a steady-state solution of the membrane voltage distribution. These approximations follow from a simple manipulation, made possible by the specific `eigen' colouring of the noise, which allows to express the membrane potential as a Gaussian diffusion process on top of a deterministic exponential movement. Following, the presented method is extended to the case of an arbitrarily coloured noise driving by factoring out the `eigen' noise and replacing the residue with an equivalent Gaussian process. It is shown that the obtained expressions agree well with numerical simulations for different values of the neuron parameters and noise colouring.
The Geometry of Information Coding in Correlated Neural Populations
Authors: Rava Azeredo da Silveira, Fred Rieke
Date: 2021-02-01 00:00:00

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout we emphasize a geometrical picture of how noise correlations impact the neural code.
Input-output behaviour of a model neuron with alternating drift
Authors: A. Buonocore, A. Di Crescenzo, E. Di Nardo
Date: 2021-01-31 00:00:00

The input-output behaviour of the Wiener neuronal model subject to alternating input is studied under the assumption that the effect of such an input is to make the drift itself of an alternating type. Firing densities and related statistics are obtained via simulations of the sample-paths of the process in the following three cases: the drift changes occur during random periods characterized by (i) exponential distribution, (ii) Erlang distribution with a preassigned shape parameter, and (iii) deterministic distribution. The obtained results are compared with those holding for the Wiener neuronal model subject to sinusoidal input