Development and application of white-noise modeling techniques for studies of insect visual nervous system

1973 ◽  
Vol 12 (2) ◽  
pp. 74-89 ◽  
Author(s):  
P. Z. Marmarelis ◽  
G. D. McCann
Keyword(s):  
Nervous System ◽  
White Noise ◽  
Noise Modeling ◽  
Modeling Techniques ◽  
Visual Nervous System

Violations of sensorimotor theories of visual experience

2004 ◽  
Vol 27 (6) ◽  
pp. 904-905 ◽  
Author(s):  
Bruce Bridgeman
Keyword(s):  
Nervous System ◽  
Visual Experience ◽  
System Structure ◽  
Space Constancy ◽  
Sensorimotor Theory ◽  
Significant Step ◽  
Sensorimotor Contingencies ◽  
Visual Nervous System

Although the sensorimotor account is a significant step forward, it cannot explain experiences of entoptic phenomena that violate normal sensorimotor contingencies but nonetheless are perceived as visual. Nervous system structure limits how they can be interpreted. Neurophysiology, combined with a sensorimotor theory, can account for space constancy by denying the existence of permanent representations of states that must be corrected or updated.

Noise Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
White Noise ◽  
Autocorrelation Function ◽  
Model Building ◽  
Moving Average ◽  
Information Criteria ◽  
Noise Model ◽  
Adequate Model ◽  
Noise Modeling ◽  
Modeling Strategy

Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

scholarly journals Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array (Adv. Mater. 51/2021)

2021 ◽  
Vol 33 (51) ◽  
pp. 2170405
Author(s):  
Tae‐Ju Lee ◽  
Kwang‐Ro Yun ◽  
Su‐Kyung Kim ◽  
Jong‐Ho Kim ◽  
Junyoung Jin ◽  
...  
Keyword(s):  
Nervous System ◽  
Optoelectronic Device ◽  
Visual Nervous System

The Expression Patterns of Nogo-A and NgR in the Neonatal Rat Visual Nervous System

2009 ◽  
Vol 34 (7) ◽  
pp. 1204-1208 ◽  
Author(s):  
Yin Xiaolei ◽  
Yuan Rongdi ◽  
Ji Shuxing ◽  
Ye Jian
Keyword(s):  
Nervous System ◽  
Expression Patterns ◽  
Neonatal Rat ◽  
Visual Nervous System

Effect of pulse parameters on visual nervous system

2002 ◽  
Vol 14 (1-4) ◽  
pp. 337-340 ◽  
Author(s):  
Hiroyuki Kanda ◽  
Masami Watanabe ◽  
Takashi Fujikado ◽  
Tohru Yagi
Keyword(s):  
Nervous System ◽  
Pulse Parameters ◽  
Visual Nervous System

scholarly journals Neural correlates of auditory recognition memory in the primate dorsal temporal pole

2014 ◽  
Vol 111 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Chi-Wing Ng ◽  
Bethany Plakke ◽  
Amy Poremba
Keyword(s):  
Nervous System ◽  
Recognition Memory ◽  
Object Identification ◽  
Matching To Sample ◽  
Related Activity ◽  
Delayed Matching ◽  
Delayed Matching To Sample ◽  
Auditory Recognition ◽  
Temporal Pole ◽  
Visual Nervous System

Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory ( Olson et al. 2007 ). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003 ). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP ( Desimone 1996 ; Nakamura and Kubota 1996 ). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects.

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