scholarly journals Incorporating neuronal fatigue in deep neural networks captures dynamics of adaptation in neurophysiology and perception

2019 ◽  
Author(s):  
Kasper Vinken ◽  
Xavier Boix ◽  
Gabriel Kreiman
Keyword(s):  
Visual Processing ◽  
Computational Models ◽  
Recognition Performance ◽  
Novelty Detection ◽  
Fundamental Property ◽  
Temporal Context ◽  
Repetition Suppression ◽  
Proposed Model ◽  
Biophysical Processes ◽  
Fatigue Mechanism

AbstractAdaptation is a fundamental property of the visual system that molds how an object is processed and perceived in its temporal context. It is unknown whether adaptation requires a circuit level implementation or whether it emerges from neuronally intrinsic biophysical processes. Here we combined neurophysiological recordings, psychophysics, and deep convolutional neural network computational models to test the hypothesis that a neuronally intrinsic, biophysically plausible, fatigue mechanism is sufficient to account for the hallmark properties of adaptation. The proposed model captured neural signatures of adaptation including repetition suppression and novelty detection. At the behavioral level, the proposed model was consistent with perceptual aftereffects. Furthermore, adapting to prevailing but irrelevant inputs improves object recognition and the adaptation computations can be trained in a network trained to maximize recognition performance. These results show that an intrinsic fatigue mechanism can account for key neurophysiological and perceptual properties and enhance visual processing by incorporating temporal context.

scholarly journals Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception

2020 ◽  
Vol 6 (42) ◽  
pp. eabd4205 ◽  
Author(s):  
K. Vinken ◽  
X. Boix ◽  
G. Kreiman
Keyword(s):  
Neural Network ◽  
Visual Processing ◽  
Tuning Curve ◽  
Recognition Performance ◽  
Novelty Detection ◽  
Fundamental Property ◽  
Human Perception ◽  
Subjective Experiences ◽  
Intrinsic Mechanism ◽  
Detection Enhancement

Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.

Repetition Suppression of Induced Gamma Activity Predicts Enhanced Orienting toward a Novel Stimulus in 6-month-old Infants

2008 ◽  
Vol 20 (12) ◽  
pp. 2137-2152 ◽  
Author(s):  
Kelly A. Snyder ◽  
Andreas Keil
Keyword(s):  
Visual Processing ◽  
Novelty Detection ◽  
Neural Mechanisms ◽  
Human Infant ◽  
Gamma Activity ◽  
Repeated Stimulus ◽  
Repetition Suppression ◽  
The Face ◽  
Over 40 Years

Habituation refers to a decline in orienting or responding to a repeated stimulus, and can be inferred to reflect learning about the properties of the repeated stimulus when followed by increased orienting to a novel stimulus (i.e., novelty detection). Habituation and novelty detection paradigms have been used for over 40 years to study perceptual and mnemonic processes in the human infant, yet important questions remain about the nature of these processes in infants. The aim of the present study was to examine the neural mechanisms underlying habituation and novelty detection in infants. Specifically, we investigated changes in induced alpha, beta, and gamma activity in 6-month-old infants during repeated presentations of either a face or an object, and examined whether these changes predicted behavioral responses to novelty at test. We found that induced gamma activity over occipital scalp regions decreased with stimulus repetition in the face condition but not in the toy condition, and that greater decreases in the gamma band were associated with enhanced orienting to a novel face at test. The pattern and topography of these findings are consistent with observations of repetition suppression in the occipital–temporal visual processing pathway, and suggest that encoding in infant habituation paradigms may reflect a form of perceptual learning. Implications for the role of repetition suppression in infant habituation and novelty detection are discussed with respect to a biased competition model of visual attention.

scholarly journals The Neurophysiology of Backward Visual Masking: Information Analysis

1999 ◽  
Vol 11 (3) ◽  
pp. 300-311 ◽  
Author(s):  
Edmund T. Rolls ◽  
Martin J. Tovée ◽  
Stefano Panzeri
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Computational Models ◽  
Direct Evidence ◽  
Visual Masking ◽  
Neuronal Response ◽  
Short Soas ◽  
Stimulus Onset ◽  
Backward Masking ◽  
Neuronal Responses

Backward masking can potentially provide evidence of the time needed for visual processing, a fundamental constraint that must be incorporated into computational models of vision. Although backward masking has been extensively used psychophysically, there is little direct evidence for the effects of visual masking on neuronal responses. To investigate the effects of a backward masking paradigm on the responses of neurons in the temporal visual cortex, we have shown that the response of the neurons is interrupted by the mask. Under conditions when humans can just identify the stimulus, with stimulus onset asynchronies (SOA) of 20 msec, neurons in macaques respond to their best stimulus for approximately 30 msec. We now quantify the information that is available from the responses of single neurons under backward masking conditions when two to six faces were shown. We show that the information available is greatly decreased as the mask is brought closer to the stimulus. The decrease is more marked than the decrease in firing rate because it is the selective part of the firing that is especially attenuated by the mask, not the spontaneous firing, and also because the neuronal response is more variable at short SOAs. However, even at the shortest SOA of 20 msec, the information available is on average 0.1 bits. This compares to 0.3 bits with only the 16-msec target stimulus shown and a typical value for such neurons of 0.4 to 0.5 bits with a 500-msec stimulus. The results thus show that considerable information is available from neuronal responses even under backward masking conditions that allow the neurons to have their main response in 30 msec. This provides evidence for how rapid the processing of visual information is in a cortical area and provides a fundamental constraint for understanding how cortical information processing operates.

scholarly journals Actin-inspired feedback couples speed and persistence in a Cellular Potts Model of cell migration

2018 ◽  
Author(s):  
Inge M. N. Wortel ◽  
Ioana Niculescu ◽  
P. Martijn Kolijn ◽  
Nir Gov ◽  
Rob J. de Boer ◽  
...  
Keyword(s):  
Cell Migration ◽  
Cell Motility ◽  
Potts Model ◽  
Cell Shape ◽  
Computational Models ◽  
Fundamental Property ◽  
Cellular Potts Model ◽  
Universal Coupling ◽  
Migrating Cells

ABSTRACTCell migration is astoundingly diverse. Molecular signatures, cell-cell and cell-matrix interactions, and environmental structures each play their part in shaping cell motion, yielding numerous different cell morphologies and migration modes. Nevertheless, in recent years, a simple unifying law was found to describe cell migration across many different cell types and contexts: faster cells turn less frequently. Given this universal coupling between speed and persistence (UCSP), from a modelling perspective it is important to know whether computational models of cell migration capture this speed-persistence link. Here, we present an in-depth characterisation of an existing Cellular Potts Model (CPM). We first show that this model robustly reproduces the UCSP without having been designed for this task. Instead, we show that this fundamental law of migration emerges spontaneously through a crosstalk of intracellular mechanisms, cell shape, and environmental constraints, resembling the dynamic nature of cell migration in vivo. Our model also reveals how cell shape dynamics can further constrain cell motility by limiting both the speed and persistence a cell can reach, and how a rigid environment such as the skin can restrict cell motility even further. Our results further validate the CPM as a model of cell migration, and shed new light on the speed-persistence coupling that has emerged as a fundamental property of migrating cells.SIGNIFICANCEThe universal coupling between speed and persistence (UCSP) is the first general quantitative law describing motility patterns across the versatile spectrum of migrating cells. Here, we show – for the first time – that this migration law emerges spontaneously in an existing, highly popular computational model of cell migration. Studying the UCSP in entirely different model frameworks, not explicitly built with this law in mind, can help uncover how intracellular dynamics, cell shape, and environment interact to produce the diverse motility patterns observed in migrating cells.

iRNAD: a computational tool for identifying D modification sites in RNA sequence

2019 ◽  
Vol 35 (23) ◽  
pp. 4922-4929 ◽  
Author(s):  
Zhao-Chun Xu ◽  
Peng-Mian Feng ◽  
Hui Yang ◽  
Wang-Ren Qiu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Support Vector ◽  
Final Model ◽  
Rna Sequence ◽  
Functional Roles ◽  
Proposed Model ◽  
User Friendly

Abstract Motivation Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. Results We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. Availability and implementation A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

Analytical Solution of Hydrostatic Pocket Tilting

2016 ◽  
Vol 821 ◽  
pp. 113-119 ◽  
Author(s):  
Eduard Stach ◽  
Jiří Falta ◽  
Matěj Sulitka
Keyword(s):  
Computational Models ◽  
Load Capacity ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Detailed Model ◽  
Limit Values ◽  
Inertia Effects ◽  
Proposed Model ◽  
Machine Tool Structure

Tilting (parallelism error) of guiding surfaces may cause reduction of load capacity of hydrostatic (HS) guideways and bearings in machine tools (MT). Using coupled finite element (FE) computational models of MT structures, it is nowadays possible to determine the extent of guiding surfaces deformation caused by thermal effects, gravitational force, cutting forces and inertia effects. Assessment of maximum allowable tilt has so far been based merely on experience. The paper presents a detailed model developed for description of the effect of HS bearing tilt on the load capacity characteristics of HS guideways. The model allows an evaluation of the tilt influence on the change of the characteristics as well as determination of the limit values of allowable tilt in interaction with compliant machine tool structure. The proposed model is based on the model of flow over the land of the HS pocket under extended Navier-Stokes equation. The model is verified using an experimental test rig.

Object-Selective Cortex Exhibits Performance-Independent Repetition Suppression

2006 ◽  
Vol 95 (2) ◽  
pp. 995-1007 ◽  
Author(s):  
Rory Sayres ◽  
Kalanit Grill-Spector
Keyword(s):  
Response Time ◽  
Response Times ◽  
Recognition Performance ◽  
Region Of Interest ◽  
Stimulus Presentation ◽  
Repetition Suppression ◽  
Fmri Study ◽  
Bold Responses ◽  
Cortical Regions ◽  
Time Required

Object-selective cortical regions exhibit a decreased response when an object stimulus is repeated [repetition suppression (RS)]. RS is often associated with priming: reduced response times and increased accuracy for repeated stimuli. It is unknown whether RS reflects stimulus-specific repetition, the associated changes in response time, or the combination of the two. To address this question, we performed a rapid event-related functional MRI (fMRI) study in which we measured BOLD signal in object-selective cortex, as well as object recognition performance, while we manipulated stimulus repetition. Our design allowed us to examine separately the roles of response time and repetition in explaining RS. We found that repetition played a robust role in explaining RS: repeated trials produced weaker BOLD responses than nonrepeated trials, even when comparing trials with matched response times. In contrast, response time played a weak role in explaining RS when repetition was controlled for: it explained BOLD responses only for one region of interest (ROI) and one experimental condition. Thus repetition suppression seems to be mostly driven by repetition rather than performance changes. We further examined whether RS reflects processes occurring at the same time as recognition or after recognition by manipulating stimulus presentation duration. In one experiment, durations were longer than required for recognition (2 s), whereas in a second experiment, durations were close to the minimum time required for recognition (85–101 ms). We found significant RS for brief presentations (albeit with a reduced magnitude), which again persisted when controlling for performance. This suggests a substantial amount of RS occurs during recognition.

scholarly journals DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 898 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Kil To Chong
Keyword(s):  
Computational Models ◽  
Short Term Memory ◽  
Binary Sequence ◽  
The Neural Network ◽  
Proposed Model ◽  
Time Parameters ◽  
Long Short Term Memory ◽  
High Level ◽  
User Friendly ◽  
Fold Cross Validation

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

scholarly journals Computational models of cortical visual processing.

1996 ◽  
Vol 93 (2) ◽  
pp. 623-627 ◽  
Author(s):  
D. J. Heeger ◽  
E. P. Simoncelli ◽  
J. A. Movshon
Keyword(s):  
Visual Processing ◽  
Computational Models ◽  
Cortical Visual Processing
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