Parameter Extraction from Population Codes: A Critical Assessment

1996 ◽  
Vol 8 (3) ◽  
pp. 511-529 ◽  
Author(s):  
Herman P. Snippe
Keyword(s):  
Parameter Extraction ◽  
Minimum Variance ◽  
Neural System ◽  
Center Of Gravity ◽  
Movement Direction ◽  
Neural Systems ◽  
Neural Activation ◽  
Neuronal Ensemble ◽  
Activation Profile ◽  
Perceptual Systems

In perceptual systems, a stimulus parameter can be extracted by determining the center-of-gravity of the response profile of a population of neural sensors. Likewise at the motor end of a neural system, center-of-gravity decoding, also known as vector decoding, generates a movement direction from the neural activation profile. We evaluate these schemes from a statistical perspective, by comparing their statistical variance with the minimum variance possible for an unbiased parameter extraction from the noisy neuronal ensemble activation profile. Center-of-gravity decoding can be statistically optimal. This is the case for regular arrays of sensors with gaussian tuning profiles that have an output described by Poisson statistics, and for arrays of sensors with a sinusoidal tuning profile for the (angular) parameter estimated. However, there are also many cases in which center-of-gravity decoding is highly inefficient. This includes the important case where sensor positions are very irregular. Finally, we study the robustness of center-of-gravity decoding against response nonlinearities at different stages of an information processing hierarchy. We conclude that, in neural systems, instead of representing a parameter explicitly, it is safer to leave the parameter coded implicitly in a neuronal ensemble activation profile.

Implementation of Deep Learning Neural Network for Retinal Images

2020 ◽  
pp. 77-95
Author(s):  
R. Murugan
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Neural System ◽  
Neural Systems ◽  
Retinal Images ◽  
Manual Segmentation ◽  
Convolutional Network ◽  
The World ◽  
Deep Learning Neural Network ◽  
Wide Assortment

The retinal parts segmentation has been recognized as a key component in both ophthalmological and cardiovascular sickness analysis. The parts of retinal pictures, vessels, optic disc, and macula segmentations, will add to the indicative outcome. In any case, the manual segmentation of retinal parts is tedious and dreary work, and it additionally requires proficient aptitudes. This chapter proposes a supervised method to segment blood vessel utilizing deep learning methods. All the more explicitly, the proposed part has connected the completely convolutional network, which is normally used to perform semantic segmentation undertaking with exchange learning. The convolutional neural system has turned out to be an amazing asset for a few computer vision assignments. As of late, restorative picture investigation bunches over the world are rapidly entering this field and applying convolutional neural systems and other deep learning philosophies to a wide assortment of uses, and uncommon outcomes are rising constantly.

Modeling The Tower Of Hanoi Using Neural Network

1993 ◽  
pp. 47-56
Author(s):  
Mohamed Othman ◽  
Mohd. Hassan Selamat ◽  
Zaiton Muda ◽  
Lili Norliya Abdullah
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Neural System ◽  
Neural Systems ◽  
Tower Of Hanoi ◽  
Artificial Neural ◽  
The Given

This paper discusses the modeling of Tower of Hanoi using the concepts of neural network. The basis idea of backpropagation learning algorithm in Artificial Neural Systems is then described. While similar in some ways, Artificial Neural System learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connection in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable qf reproducing the desired function within the given network. Key words: Tower of Hanoi; Backpropagation Algorithm; Knowledge Representation;

scholarly journals Object Recognition for Quadcopter Drone using Convolutional Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 224-227
Keyword(s):  
Neural System ◽  
Neural Systems ◽  
Course Of Action ◽  
Self Sufficiency ◽  
Working Parameters ◽  
The Impact ◽  
Free Dynamic ◽  
Line Observation

Object detection is as of now generally utilized in industry. It is the strategy for location and design of genuine items. Models incorporate intermittent scaffold examinations, debacle the executives, power line observation and traffic examinations. As UAV applications become progressively broad, more significant levels of self-sufficiency and free dynamic procedures are expected to improve the security, proficiency and exactness of the gadgets. This article exhibits in detail the method and parameters important for the preparation of convolutional neural systems (CNN) in the programmed acknowledgment of items. The potential areas of utilization in the vehicle division are additionally featured. The precision and unwavering quality of the CNNs rely upon the arrangement of the system and the determination of working parameters. The impact of article recognition shows that by picking a parameter setting course of action, a CNN can recognize and gather objects with a noteworthy degree of accuracy (97.5%) and computational profitability. Moreover, utilizing a convolutional neural system actualized in the YOLO stage (V3), items can be followed, distinguished and characterized progressively

Line-of-Sight Detection Using Center of Gravity with Pixel Number Variation

2017 ◽  
Vol 5 (3) ◽  
pp. 64-76
Author(s):  
Takahito Niwa ◽  
Ippei Torii ◽  
Naohiro Ishii
Keyword(s):  
Involuntary Movement ◽  
Center Of Gravity ◽  
The Body ◽  
Movement Direction ◽  
Line Of Sight ◽  
Number Variation ◽  
Blink Detection ◽  
Handicapped People ◽  
Institute Of Technology ◽  
Physically Handicapped

This study is developed from the application software, called “Eyetalk” which supports communication for handicapped people studied by Torii laboratory, Aichi Institute of Technology and modified it to be used by the physically handicapped people with involuntary movement (Abnormal movement of the body that occurs independently of the consciousness). Then, the line-of-sight for the detection of the eye movement direction is developed for the communication to determine whether the user is looking left or right, which is based on the computation of the center of gravity with pixel number in the eye image. Further, a newly proposed blink detection method using afterimage is applied to the developed system. This study will be extended to the application that everyone can use easily and quickly to express his/her thoughts and requests.

scholarly journals Dynamic State and Parameter Estimation Applied to Neuromorphic Systems

2012 ◽  
Vol 24 (7) ◽  
pp. 1669-1694 ◽  
Author(s):  
Emre Ozgur Neftci ◽  
Bryan Toth ◽  
Giacomo Indiveri ◽  
Henry D. I. Abarbanel
Keyword(s):  
Parameter Estimation ◽  
Large Scale ◽  
Computational Models ◽  
Internal State ◽  
Network Models ◽  
Experimental System ◽  
Neural System ◽  
Dynamic State ◽  
Neural Systems ◽  
State Variables

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.

scholarly journals Tensors and compositionality in neural systems

2019 ◽  
Vol 375 (1791) ◽  
pp. 20190306 ◽  
Author(s):  
Andrea E. Martin ◽  
Leonidas A. A. Doumas
Keyword(s):  
Expressive Power ◽  
Human Mind ◽  
Neural System ◽  
Process Models ◽  
Neural Systems ◽  
Human Thought ◽  
Multiplicative Operator ◽  
Compositional Structures ◽  
Mind And Brain ◽  
Behavioural Experiments

Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achieve the compositionality exhibited in human thought and language if it were to rely on a multiplicative operator to perform binding, as the tensor product (TP)-based systems that have been widely adopted in cognitive science, neuroscience and artificial intelligence do. We show via simulation and two behavioural experiments that TPs violate variable-value independence, but human behaviour does not. Specifically, TPs fail to capture that in the statements fuzzy cactus and fuzzy penguin , both cactus and penguin are predicated by fuzzy (x) and belong to the set of fuzzy things, rendering these arguments similar to each other. Consistent with that thesis, people judged arguments that shared the same role to be similar, even when those arguments themselves (e.g., cacti and penguins) were judged to be dissimilar when in isolation. By contrast, the similarity of the TPs representing fuzzy (cactus) and fuzzy (penguin) was determined by the similarity of the arguments, which in this case approaches zero. Based on these results, we argue that neural systems that use TPs for binding cannot approximate how the human mind and brain represent compositional information during processing. We describe a contrasting binding mechanism that any physiological or artificial neural system could use to maintain independence between a role and its argument, a prerequisite for compositionality and, thus, for instantiating the expressive power of human thought and language in a neural system. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

scholarly journals Two Takes on the Social Brain: A Comparison of Theory of Mind Tasks

2007 ◽  
Vol 19 (11) ◽  
pp. 1803-1814 ◽  
Author(s):  
Maria Ida Gobbini ◽  
Aaron C. Koralek ◽  
Ronald E. Bryan ◽  
Kimberly J. Montgomery ◽  
James V. Haxby
Keyword(s):  
Theory Of Mind ◽  
Mirror Neuron System ◽  
Mental States ◽  
Superior Temporal Sulcus ◽  
Neural System ◽  
False Belief ◽  
Fusiform Gyrus ◽  
Neural Systems ◽  
General Role ◽  
The Social

We compared two tasks that are widely used in research on mentalizing—false belief stories and animations of rigid geometric shapes that depict social interactions—to investigate whether the neural systems that mediate the representation of others' mental states are consistent across these tasks. Whereas false belief stories activated primarily the anterior paracingulate cortex (APC), the posterior cingulate cortex/precuneus (PCC/PC), and the temporo-parietal junction (TPJ)—components of the distributed neural system for theory of mind (ToM)—the social animations activated an extensive region along nearly the full extent of the superior temporal sulcus, including a locus in the posterior superior temporal sulcus (pSTS), as well as the frontal operculum and inferior parietal lobule (IPL)—components of the distributed neural system for action understanding—and the fusiform gyrus. These results suggest that the representation of covert mental states that may predict behavior and the representation of intentions that are implied by perceived actions involve distinct neural systems. These results show that the TPJ and the pSTS play dissociable roles in mentalizing and are parts of different distributed neural systems. Because the social animations do not depict articulated body movements, these results also highlight that the perception of the kinematics of actions is not necessary to activate the mirror neuron system, suggesting that this system plays a general role in the representation of intentions and goals of actions. Furthermore, these results suggest that the fusiform gyrus plays a general role in the representation of visual stimuli that signify agency, independent of visual form.

scholarly journals Mental Mechanisms for Topics Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Louis Massey
Keyword(s):  
Baseline Level ◽  
Short Term Memory ◽  
Decay Rates ◽  
Neural Systems ◽  
Long Term Memory ◽  
Neural Activation ◽  
Main Hypothesis ◽  
Term Memory

Topics identification (TI) is the process that consists in determining the main themes present in natural language documents. The current TI modeling paradigm aims at acquiring semantic information from statistic properties of large text datasets. We investigate the mental mechanisms responsible for the identification of topics in a single document given existing knowledge. Our main hypothesis is that topics are the result of accumulated neural activation of loosely organized information stored in long-term memory (LTM). We experimentally tested our hypothesis with a computational model that simulates LTM activation. The model assumes activation decay as an unavoidable phenomenon originating from the bioelectric nature of neural systems. Since decay should negatively affect the quality of topics, the model predicts the presence of short-term memory (STM) to keep the focus of attention on a few words, with the expected outcome of restoring quality to a baseline level. Our experiments measured topics quality of over 300 documents with various decay rates and STM capacity. Our results showed that accumulated activation of loosely organized information was an effective mental computational commodity to identify topics. It was furthermore confirmed that rapid decay is detrimental to topics quality but that limited capacity STM restores quality to a baseline level, even exceeding it slightly.

scholarly journals Ann based Shortcoming Area in HVDC Transmission Line

2019 ◽  
Vol 9 (1) ◽  
pp. 2053-2059
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Distribution System ◽  
Back Propagation ◽  
Neural System ◽  
Neural Systems ◽  
Back Propagation Algorithm ◽  
Hvdc Transmission ◽  
Nonlinear Input ◽  
Output Behavior

This paper is to ponder the use of Neural systems for shortcoming discovery and area in HVDC framework. The proposed Neural system learns examples and relationship on transient information. The Back-propagation algorithm has the ability to match map complex and nonlinear input - output behavior. A MATLAB model was created for the 500kv,1000MW,12 pulse-based converter-based distribution system and variousfault events were simulated. The limitations of AC over long transmission lines has led to the use of DC for bulk transmission over long distances. Fault condition like rectifier fault, inverter faults and transmission line faults are discussed in this paper.

scholarly journals Stochastic and vibrational resonance in complex networks of neurons

2021 ◽  
Vol 379 (2198) ◽  
Author(s):  
Ali Calim ◽  
Tugba Palabas ◽  
Muhammet Uzuntarla
Keyword(s):  
Nonlinear Systems ◽  
Complex Networks ◽  
Stochastic Resonance ◽  
Network Architecture ◽  
External Perturbation ◽  
Neural System ◽  
Neural Systems ◽  
Vibrational Resonance ◽  
Resonance Phenomena ◽  
Information Signal

The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 2)’.

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