Gordon Pipa

Osnabrück University | UOS · Institute of Cognitive Science

Ising models are routinely used to quantify the second order, functional structure of neural populations. With some recent exceptions, they generally do not include the influence of time varying stimulus drive. Yet if the dynamics of network function are to be understood, time varying stimuli must be taken into account. Inclusion of stimulus drive...

Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The cha...

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial a...

The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentati...

Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferenti...

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilit...

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This "out-of-the-loop" problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilit...

The function of spike synchrony is debatable: some researchers view it as a mechanism for binding perceptual features, others – as a byproduct of brain activity. We argue for an alternative computational role: synchrony can estimate the prior probability of incoming stimuli. In V1, this can be achieved by comparing input with previously acquired vi...

This paper describes an application of a machine learning based method and compares it to classical methods for input modelling of software processing latencies for a discrete-event simulation model of a hardware-in-the-loop test bench. We apply and compare the following four different methods for input modelling: stochastic distribution functions,...

The function of spike synchrony has long been debated in the neuroscience community. Some researchers view synchrony as a byproduct of brain activity, while others argue that it serves as a mechanism for binding perceptual features. We, however, argue for an alternative view on the computational role of synchrony: it can serve as a mechanism for es...

Most artificial neural networks used for object recognition are trained in a fully supervised setup. This is not only resource consuming as it requires large data sets of labeled examples but also quite different from how humans learn. We use a setup in which an artificial agent first learns in a simulated world through self-supervised, curiosity-d...

Electroencephalography (EEG) is a crucial tool in cognitive neuroscience, enabling the study of neurophysiological function by measuring the brain’s electrical activity. Its applications include perception, learning, memory, language, decision making and neural network mapping. Recently, interest has surged in extending EEG measurements to domestic...

In this paper, we present the development and characteristics of a novel mobile-EEG device coined as the "DreamMachine." This innovative system serves as a cost-effective alternative to traditional laboratory-based EEG systems as well as currently available mobile-EEG devices. The DreamMachine offers an impressive array of features, positioning it...

Propofol belongs to a class of molecules that are known to block learning and memory in mammals, including rodents and humans. Interestingly, learning and memory are not tied to the presence of a nervous system. There are several lines of evidence indicating that single-celled organisms also have the capacity for learning and memory which may be co...

Numerical weather prediction (NWP) models are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditio...

This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for post-processing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare...

The brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brain's own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical fu...

Grammar acquisition is of significant importance for mastering human language. As the language signal is sequential in its nature, it poses the challenging task to extract its structure during online processing. This modeling study shows how spike-timing dependent plasticity (STDP) successfully enables sequence learning of artificial grammars that...

This study uses transformers architecture of Artificial neural networks to generate artificial business text for a given topic or theme. The implication of the study is to augment the business report writing, and general business writings process with help of generative pretrained transformers (generative pretrained transformer (GPT)) networks. Mai...

Numerical Weather Prediction models (NWP) are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditio...

Autonomous vehicles represent a significant development in our society, and their acceptance will largely depend on trust. This study investigates strategies to increase trust and acceptance by making the cars’ decisions. For this purpose, we created a virtual reality (VR) experiment with a self-explaining autonomous car, providing participants wit...

This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment ind...

Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datase...

This study uses transformers architecture of Artificial neural networks to generate artificial business text for a given topic or theme. The implication of the study is to augment the business report writing, and general business writings process with help of Generative pretrained transformers (GPT) networks. Main focus of study is to provide pract...

While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired...

Missing terms in dynamical systems are a challenging problem for modeling. Recent developments in the combination of machine learning and dynamical system theory open possibilities for a solution. We show how physics-informed differential equations and machine learning—combined in the Universal Differential Equation (UDE) framework by Rackauckas et...

Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets. This is particularly challenging if there are systematic differences between them, as is often the case. Accounting for these differences requires a larger number of parameters and thus increases the risk of overfitting. We investigate h...

Autonomous vehicles as cognitive agents will be an important use case of artificial intelligence in modern societies. Investigating how to increase acceptance and trust, we created a self-explaining car, informing passengers before actions in virtual reality. This study investigates the attitude towards self-driving cars with data from 7850 partici...

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate e...

Most artificial neural networks used for object detection and recognition are trained in a fully supervised setup. This is not only very resource consuming as it requires large data sets of labeled examples but also very different from how humans learn. We introduce a setup in which an artificial agent first learns in a simulated world through self...

Dreams take us to a different reality, a hallucinatory world that feels as real as any waking experience. These often-bizarre episodes are emblematic of human sleep but have yet to be adequately explained. Retrospective dream reports are subject to distortion and forgetting, presenting a fundamental challenge for neuroscientific studies of dreaming...

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate ef...

How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment? Here we investigate embodiment with a closed loop between action and perception as one key component in this process. We take a close look at the representations learned by a deep reinforcement learning agent t...

The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with...

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of...

With the introduction of autonomous vehicles, drivers will be able to engage in non-related tasks while being driven. But in critical situations the car needs the support of the human driver. How do distracted drivers get back into the control-loop quickly when the car requests a take-over? To investigate effective take-over actions, we developed a...

This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial electroencephalographic (iEEG) signals. The resulting time and frequency components capture the...

Virtual environments will deeply alter the way we conduct scientific studies on human behavior. Possible applications range from spatial navigation over addressing moral dilemmas in a more natural manner to therapeutic applications for affective disorders. The decisive factor for this broad range of applications is that virtual reality (VR) is able...

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infect...

Structural covariance analysis is a promising and increasingly used structural Magnetic Resonance Imaging (MRI) analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. However, to our knowledge, little has been investigated in terms of the comparability of results between different data set...

Self-driving cars have the potential to greatly improve public safety. However, their introduction onto public roads must overcome both ethical and technical challenges. To further understand the ethical issues of introducing self-driving cars, we conducted two moral judgement studies investigating potential differences in the moral norms applied t...

Experts review the latest research on the neocortex and consider potential directions for future research. Over the past decade, technological advances have dramatically increased information on the structural and functional organization of the brain, especially the cerebral cortex. This explosion of data has radically expanded our ability to chara...

Experts review the latest research on the neocortex and consider potential directions for future research. Over the past decade, technological advances have dramatically increased information on the structural and functional organization of the brain, especially the cerebral cortex. This explosion of data has radically expanded our ability to chara...

The question of how self-driving cars should behave in dilemma situations has recently attracted a lot of attention in science, media and society. A growing number of publications amass insight into the factors underlying the choices we make in such situations, often using forced-choice paradigms closely linked to the trolley dilemma. The methodolo...

This document describes the implementation of a copyright classification process for user-contributed Portable Document Format (PDF) documents. The implementation employs two ways to classify documents as copyright-protected or non-copyright-protected: first, using structural features extracted from the document metadata, content and underlying doc...

Bistable perception describes the phenomenon of perception alternating between stable states when a subject is presented two incompatible stimuli. Besides intensive research in the last century many open questions remain. As a phenomenon occurring across different perceptual domains, understanding bistable perception can help to reveal properties o...

Self-driving cars have the potential to greatly improve public safety. However, their introduction onto public roads must overcome both ethical and technical challenges. To further understand the ethical issues of introducing self-driving cars, we conducted two moral judgement studies investigating potential differences in the moral norms applied t...

A bstract Over the last two decades, advances in neurobiology have established the essential role of active processes in neural dendrites for almost every aspect of cognition, but how these processes contribute to neural computation remains an open question. We show how two kinds of events within the dendrite, synaptic spikes and localized dendriti...

Automated driving technology advances quickly, and self-driving vehicles will soon no longer need human supervision. The ethical questions that the technology brings with it, however, are diverse and not always easily solvable. In particular, the question of morally right behavior in dilemma situations presents an unsolved issue to date, a solution...

This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial electroencephalographic (iEEG) signals. The resulting time and frequency components capture the...

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infect...

Ethical thought experiments such as the trolley dilemma have been investigated extensively in the past, showing that humans act in a utilitarian way, trying to cause as little overall damage as possible. These trolley dilemmas have gained renewed attention over the past years; especially due to the necessity of implementing moral decisions in auton...

The question of how self-driving cars should behave in dilemma situations has recently attracted a lot of attention in science, media and society. A growing number of publications amass insight into the factors underlying the choices we make in such situations, often using forced-choice paradigms closely linked to the trolley dilemma. The methodolo...

A Lucid dream (LD) is a dream in which the dreaming person knows that he or she is dreaming. Being neglected by scientific researchers and viewed as esoteric or paranormal for many decades, nowadays LD is an acknowledged research field, which also has practical clinical implications. However, the public's perception of LD has not yet been studied....

https://www.frontiersin.org/articles/10.3389/fnbeh.2018.00128/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Behavioral_Neuroscience&id=365687

The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with...

Variance in spatial abilities are thought to be determined by in utero levels of testosterone and oestrogen, measurable in adults by the length ratio of the 2nd and 4th digit (2D:4D). We confirmed the relationship between 2D:4D and spatial performance using rats in two different tasks (paired-associate task and watermaze) and replicated this in hum...

Autonomous vehicles, though having enormous potential, face a number of challenges. As a computer system interacting with society on a large scale and human beings in particular, they will encounter situations, which require moral assessment. What will count as right behavior in such situations depends on which factors are considered to be both mor...

A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input signals can be characterized by co...

During dreaming, we experience a wake-like hallucinatory reality, however with restricted reflective abilities: in the face of a bizarre dream environment, we do not realize that we are actually dreaming. In contrast, during the rare phenomenon of lucid dreaming, the dreamer gains insight into the current state of mind while staying asleep. This me...

We introduce a feature extraction scheme from a biologically inspired model using receptive fields (RFs) to point-light human motion patterns to form an action descriptor. The Echo State Network (ESN) which also has a biological plausibility is chosen for classification. We demonstrate the efficiency and robustness of applying the proposed feature...

The major diagnostic sleep laboratory tool for assessing excessive daytime sleepiness (EDS), the multiple sleep latency test (MSLT), is increasingly criticized for poor precision in the differentiation of idiopathic hypersomnia (IH) and narcolepsy (Trotti et al., 2013; Johns, 2000). Recent evidence suggests that actigraphy can supplement the diagno...