scholarly journals Neural network models for DMT-induced visual hallucinations

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Michael M Schartner ◽  
Christopher Timmermann
Keyword(s):  
Conscious Experience ◽  
Psychometric Testing ◽  
Network Models ◽  
Serotonergic System ◽  
Verbal Reports ◽  
Visual Effects ◽  
Neural Network Models ◽  
Psychedelic Drugs ◽  
Endogenous Information

Abstract The regulatory role of the serotonergic system on conscious perception can be investigated perturbatorily with psychedelic drugs such as N,N-Dimethyltryptamine. There is increasing evidence that the serotonergic system gates prior (endogenous) and sensory (exogenous) information in the construction of a conscious experience. Using two generative deep neural networks as examples, we discuss how such models have the potential to be, firstly, an important medium to illustrate phenomenological visual effects of psychedelics—besides paintings, verbal reports and psychometric testing—and, secondly, their utility to conceptualize biological mechanisms of gating the influence of exogenous and endogenous information on visual perception.

scholarly journals Role of neural network models for developing speech systems

Sadhana ◽  
2011 ◽  
Vol 36 (5) ◽  
pp. 783-836 ◽  
Author(s):  
K SREENIVASA RAO
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models

scholarly journals Psychedelics and virtual reality: parallels and applications

2020 ◽  
Vol 10 ◽  
pp. 204512532094835
Author(s):  
Jacob S. Aday ◽  
Christopher C. Davoli ◽  
Emily K. Bloesch
Keyword(s):  
Virtual Reality ◽  
Conscious Experience ◽  
Critical Role ◽  
Scientific Interest ◽  
Future Studies ◽  
Therapeutic Uses ◽  
Mental Experience ◽  
Psychedelic Drugs ◽  
Traditional Therapies

Psychedelic drugs and virtual reality (VR) each have the capacity to disrupt the rigidity and limitations of typical conscious experience. This article delineates the parallels among psychedelic and VR states as well as their potential synergistic applications in clinical and recreational settings. Findings indicate that, individually, psychedelics and VR are used in analogous ways to alter sensory experience and evoke awe. They are also both used in tandem with traditional therapies to treat a variety of mood disorders; their shared capacity to transiently alter perspective and disrupt rigid patterns of mental experience may underly their analogous and transdiagnostic therapeutic uses. In terms of their combined applications, a number of recreational users currently utilize psychedelics and VR together to enhance their experience. We propose that VR may be a useful tool for preparing hallucinogen-naïve participants in clinical trials for the sensory distortions experienced in psychedelic states. Given the critical role of “setting” in psychedelic treatment outcomes, we also detail how VR could be used to optimize the environment in psychedelic sessions. Finally, we provide considerations for future studies and detail how advancements in psychedelic and VR research can inform one another. Collectively, this article outlines a number of connections between psychedelics and VR, and, more broadly, is representative of growing scientific interest into the interactions among technology, psychopharmacology, and mental health.

scholarly journals Organizing memories for generalization in complementary learning systems

2021 ◽  
Author(s):  
Weinan Sun ◽  
Madhu Advani ◽  
Nelson Spruston ◽  
Andrew Saxe ◽  
James E Fitzgerald
Keyword(s):  
Network Models ◽  
Memory Systems ◽  
Time Varying ◽  
Neural Network Models ◽  
Brain Areas ◽  
Systems Consolidation ◽  
The Past ◽  
Future Behavior ◽  
Uncertain Future

Our ability to remember the past is essential for guiding our future behavior. Psychological and neurobiological features of declarative memories are known to transform over time in a process known as systems consolidation. While many theories have sought to explain the time-varying role of hippocampal and neocortical brain areas, the computational principles that govern these transformations remain unclear. Here we propose a theory of systems consolidation in which hippocampal-cortical interactions serve to optimize generalizations that guide future adaptive behavior. We use mathematical analysis of neural network models to characterize fundamental performance tradeoffs in systems consolidation, revealing that memory components should be organized according to their predictability. The theory shows that multiple interacting memory systems can outperform just one, normatively unifying diverse experimental observations and making novel experimental predictions. Our results suggest that the psychological taxonomy and neurobiological organization of declarative memories reflect a system optimized for behaving well in an uncertain future.

scholarly journals Role of NMDAR plasticity in a computational model of synaptic memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ekaterina D. Gribkova ◽  
Rhanor Gillette
Keyword(s):  
Computational Model ◽  
Single Neuron ◽  
Network Models ◽  
Time Difference ◽  
Neural Network Models ◽  
Silent Synapses ◽  
Aspartate Receptor ◽  
Artificial Neural Network Models ◽  
Precise Time

AbstractA largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N‐methyl‐d‐aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs’ glutamate and voltage gate conductances induce modifications of the synapse’s NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

The Role of Computer Simulation in Neurolinguistics

1993 ◽  
Vol 16 (2) ◽  
pp. 153-169 ◽  
Author(s):  
Hans-Jürgen Eikmeyer ◽  
Ulrich Schade
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Cognitive Science ◽  
Language Production ◽  
Network Models ◽  
Neural Network Models ◽  
Cognitive Capacities ◽  
The Brain ◽  
Structure And Functions

As a result of present-day technological standards, the technique of computer simulation is constantly gaining influence in cognitive science. Neurolinguistics is a special branch of this field in which cognitive capacities connected with language are related to the structure and functions of the brain. It is argued that computer simulation is a useful technique for evaluating neurolinguistic models. This is demonstrated with respect to neural network models of the process of language production.

scholarly journals Spike frequency adaptation supports network computations on temporally dispersed information

2020 ◽  
Author(s):  
Darjan Salaj ◽  
Anand Subramoney ◽  
Ceca Kraišniković ◽  
Guillaume Bellec ◽  
Robert Legenstein ◽  
...  
Keyword(s):  
Network Models ◽  
Spike Frequency ◽  
Spiking Neurons ◽  
Integration Time ◽  
Spiking Neural Network ◽  
Spike Frequency Adaptation ◽  
Substantial Fraction ◽  
Neural Network Models ◽  
Frequency Adaptation

AbstractFor solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well known property of a substantial fraction of neurons in the neocortex — especially in higher areas of the human neocortex — moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

scholarly journals Spike frequency adaptation supports network computations on temporally dispersed information

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Darjan Salaj ◽  
Anand Subramoney ◽  
Ceca Kraisnikovic ◽  
Guillaume Bellec ◽  
Robert Legenstein ◽  
...  
Keyword(s):  
Network Models ◽  
Spike Frequency ◽  
Spiking Neurons ◽  
Integration Time ◽  
Spiking Neural Network ◽  
Spike Frequency Adaptation ◽  
Substantial Fraction ◽  
Neural Network Models ◽  
Frequency Adaptation

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex – especially in higher areas of the human neocortex – moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

scholarly journals Neural Network Models for Empirical Finance

2020 ◽  
Vol 13 (11) ◽  
pp. 265
Author(s):  
Hector F. Calvo-Pardo ◽  
Tullio Mancini ◽  
Jose Olmo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Ensemble Methods ◽  
Network Models ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Out Of Sample

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.

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