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Compound short- and long-term memory for memory augmented neural networks
, Article Engineering Applications of Artificial Intelligence ; Volume 116 , 2022 ; 09521976 (ISSN) ; Ghaemmaghami, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Adding memory to artificial intelligence systems in an effective way has been addressed by researchers for many years. Recurrent neural networks and long short-term memories (LSTMs), among other neural network systems, have some inherent memory capabilities. Recently, in memory augmented neural networks, such as neural Turing machine (NTM) and its variants, a separate memory module is implemented, which can be accessed via read and write heads. Despite its capabilities in simple algorithmic tasks, such as copying and repeat copying, neural Turing machines fail when doing complex tasks with long-term dependencies due to their limited memory capacity. In this paper, we propose a new memory...
Salience memories formed by value, novelty and aversiveness jointly shape object responses in the prefrontal cortex and basal ganglia
, Article Nature Communications ; Volume 13, Issue 1 , 2022 ; 20411723 (ISSN) ; Hikosaka, O ; Sharif University of Technology
Nature Research
2022
Abstract
Ecological fitness depends on maintaining object histories to guide future interactions. Recent evidence shows that value memory changes passive visual responses to objects in ventrolateral prefrontal cortex (vlPFC) and substantia nigra reticulata (SNr). However, it is not known whether this effect is limited to reward history and if not how cross-domain representations are organized within the same or different neural populations in this corticobasal circuitry. To address this issue, visual responses of the same neurons across appetitive, aversive and novelty domains were recorded in vlPFC and SNr. Results showed that changes in visual responses across domains happened in the same rather...
Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times
, Article Brain Structure and Function ; Volume 227, Issue 5 , 2022 , Pages 1641-1654 ; 18632653 (ISSN) ; Jahed, M ; Dezfouli, M. P ; Daliri, M. R ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2022
Abstract
Neural synchronization has been engaged in several brain mechanisms. Previous studies have shown that the interaction between the time of spiking activity and phase of local field potentials (LFPs) plays a key role in many cognitive functions. However, the potential role of this spike–LFP phase coupling (SPC) in neural coding is not fully understood. Here, we sought to investigate the role of this SPC for encoding the sensory properties of visual stimuli. To this end, we measured SPC strength in the preferred and anti-preferred motion directions of stimulus presented inside the receptive field of middle temporal (MT) neurons. We found a selective response in terms of SPC strength for...
Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions
, Article PLoS Computational Biology ; Volume 18, Issue 7 , 2022 ; 1553734X (ISSN) ; Fakharian, M. A ; Ghazizadeh, A ; Sharif University of Technology
Public Library of Science
2022
Abstract
Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as...
Critical behavior at the onset of synchronization in a neuronal model
, Article Physica A: Statistical Mechanics and its Applications ; Volume 587 , 2022 ; 03784371 (ISSN) ; Moghimi Araghi, S ; Sharif University of Technology
Elsevier B.V
2022
Abstract
It has been observed experimentally that the neural tissues generate highly variable and scale-free distributed outbursts of activity both in vivo and in vitro. Understanding whether these heterogeneous patterns of activity come from operation of the brain at the edge of a phase transition is an interesting possibility. Therefore, constructing a simple model that exhibits such behavior is of great interest. Additionally, the presence of both critical behavior and oscillatory patterns in brain dynamics is a very interesting phenomenon: Oscillatory patterns define a temporal scale, while criticality imposes scale-free characteristics. In this paper, we consider a model for a neuronal...
A computational modelling study of excitation of neuronal cells with triboelectric nanogenerators
, Article Scientific Reports ; Volume 12, Issue 1 , 2022 ; 20452322 (ISSN) ; Mohammadpour, R ; Asadian, E ; Rafii-Tabar, H ; Sasanpour, P ; Sharif University of Technology
Nature Research
2022
Abstract
Neurological disorders and nerve injuries, such as spinal cord injury, stroke, and multiple sclerosis can result in the loss of muscle function. Electrical stimulation of the neuronal cells is the currently available clinical treatment in this regard. As an effective energy harvester, the triboelectric nanogenerators (TENG) can be used for self-powered neural/muscle stimulations because the output of the TENG provides stimulation pulses for nerves. In the present study, using a computational modelling approach, the effect of surface micropatterns on the electric field distribution, induced voltage and capacitance of the TENG structures have been investigated. By incorporating the effect of...
Evaluation of differentiation quality of several differentiation inducers of bone marrow-derived mesenchymal stem cells to nerve cells by as-sessing expression of beta-tubulin 3 marker: A systematic review
, Article Current Stem Cell Research and Therapy ; Volume 16, Issue 8 , 2021 , Pages 994-1004 ; 1574888X (ISSN) ; Zahedi, F ; Hashemi, Z. S ; Khalili, S ; Sharif University of Technology
Bentham Science Publishers
2021
Abstract
Neurological diseases have different etiological causes. Contemporary, developing an ef-fective treatment for these diseases is an ongoing challenge. Cell therapy is recognized as one of the promising solutions for the treatment of these diseases. Amongst various types of stem cells, bone marrow-derived mesenchymal stem cells (BM-MSC) are known to be the most widely used stem cells. These cells are endowed with appealing properties such as the ability to differentiate into other cell types, including the muscle, liver, glial, and nerve cells. In this review study, we have systematically evaluated the ability of a variety of chemical compounds used in the last ten years to differentiate...
Common coding of expected value and value uncertainty memories in the prefrontal cortex and basal ganglia output
, Article Science Advances ; Volume 7, Issue 20 , 2021 ; 23752548 (ISSN) ; Hikosaka, O ; Sharif University of Technology
American Association for the Advancement of Science
2021
Abstract
Recent evidence implicates both basal ganglia and ventrolateral prefrontal cortex (vlPFC) in encoding value memories. However, comparative roles of cortical and basal nodes in value memory are not well understood. Here, single-unit recordings in vlPFC and substantia nigra reticulata (SNr), within macaque monkeys, revealed a larger value signal in SNr that was nevertheless correlated with and had a comparable onset to the vlPFC value signal. The value signal was maintained for many objects (>90) many weeks after reward learning and was resistant to extinction in both regions and to repetition suppression in vlPFC. Both regions showed comparable granularity in encoding expected value and value...
Identification of the appropriate architecture of multilayer feed-forward neural network for estimation of NPPs parameters using the GA in combination with the LM and the BR learning algorithms
, Article Annals of Nuclear Energy ; Volume 156 , 2021 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using...
Estimating buildup factor of alloys based on combination of Monte Carlo method and multilayer feed-forward neural network
, Article Annals of Nuclear Energy ; Volume 152 , 2021 ; 03064549 (ISSN) ; Mohtashami, S ; Sahraeian, M ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society...
Task-specific modulation of PFC activity for matching-rule governed decision-making
, Article Brain Structure and Function ; Volume 226, Issue 2 , 2021 , Pages 443-455 ; 18632653 (ISSN) ; Zarei, M ; Constantinidis, C ; Daliri, M. R ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021
Abstract
Storing information from incoming stimuli in working memory (WM) is essential for decision-making. The prefrontal cortex (PFC) plays a key role to support this process. Previous studies have characterized different neuronal populations in the PFC for working memory judgements based on whether an originally presented stimulus matches a subsequently presented one (matching-rule decision-making). However, much remains to be understood about this mechanism at the population level of PFC neurons. Here, we hypothesized differences in processing of feature vs. spatial WM within the PFC during a matching-rule decision-making task. To test this hypothesis, the modulation of neural activity within the...
ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices
, Article IEEE Transactions on Biomedical Circuits and Systems ; Volume 13, Issue 6 , 2021 , Pages 1483-1493 ; 19324545 (ISSN) ; Hashemi, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification...
Extending concepts of mapping of human brain to artificial intelligence and neural networks
, Article Scientia Iranica ; Volume 28, Issue 3 D , 2021 , Pages 1529-1534 ; 10263098 (ISSN) ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
This paper introduces the concept of mapping of Artificially Intelligent (AI) computational systems. The concept of homunculus from human neurophysiology is extended to AI systems. It is assumed that an AI system behaves similarly to a mini-column or ganglion in the natural animal brain that comprises a layer of afferent (input) neurons, a number of interconnecting processing cells, and a layer of efferent (output) neurons or organs. The objective of the present study was to identify the correlation between the stimulus to each afferent neuron and the corresponding response from each efferent organ when the intelligent system is subjected to certain stimuli. To clarify the general concept, a...
A thermodynamically consistent electro-chemo-mechanical theory for modeling axonal swelling
, Article Journal of the Mechanics and Physics of Solids ; Volume 145 , 2020 ; Naghdabadi, R ; Sohrabpour, S ; Li, Y ; Hu, Y ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In the present study, for the first time, a thermodynamically consistent large deformation theory is developed to model the multi physics problem of axonal swelling which is the hallmark of most of the brain diseases. To this end, the relevant axonal compartments are first explained and the corresponding model parts are introduced. Next, the problem is formulated as an open thermodynamic system and the corresponding constitutive and evolution equations are extracted utilizing the balance laws. Here, a multiplicative decomposition of the deformation gradient is used to capture the active behavior of the axonal actin cortex. Specific free energy functions are given for the model parts to...
Prediction of BLEVE mechanical energy by implementation of artificial neural network
, Article Journal of Loss Prevention in the Process Industries ; Volume 63 , January , 2020 ; Casal, J ; Planas, E ; Hemmatian, B ; Rashtchian, D ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input...
ILS-based reservoir computing for handwritten digits recognition
, Article 8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020, 2 September 2020 through 4 September 2020 ; 2020 , Pages 7-12 ; Baghri Shouraki, S ; Faraji, M. M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
ILS-based reservoir is a bio-inspired computational model consisting of spiking neurons which has been designed to process spatiotemporal patterns appropriately. In ILS-based reservoir, the neurons are located in an ionic environment and the connections are provided by ionic density. By using ionic diffusion as a processing operation, this model is able to consider the effects of both the preceding and current stimuli properly. Since character recognition is an important task in various applications, this paper focuses on the classification of handwritten digits using ILS-based reservoir. For this purpose, a neuromorphic handwritten digit dataset called N-MNIST dataset is used as a...
Estimating buildup factor of alloys based on combination of Monte Carlo method and multilayer feed-forward neural network
, Article Annals of Nuclear Energy ; 2020 ; Mohtashami, S ; Sahraeian, M ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society...
Emergence of bursting in two coupled neurons of different types of excitability
, Article Chaos, Solitons and Fractals ; Volume 132 , 2020 ; Yasaman, S ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In this manuscript, a spiking neuron of type I excitability and a silent neuron of type II excitability are coupled through a gap junction with unequal coupling strengths, and none of the coupled neurons can burst intrinsically. By applying the theory of dynamical systems (e.g. bifurcation theory), we investigate how the coupling strength affects the dynamics of the neurons, when one of the coupling strengths is fixed and the other varies. We report four different regimes of oscillations as the coupling strength increases. (1) Spike–Spike Phase–Locking, where both neurons are in tonic spiking mode but with different frequencies; (2) Spike–Burst mode, where the type II neuron bursts while the...
A new nonlinear sparse component analysis for a biologically plausible model of neurons
, Article Neural Computation ; Volume 31, Issue 9 , 2019 , Pages 1853-1873 ; 08997667 (ISSN) ; Jahed, M ; Ghazizadeh, A ; Sharif University of Technology
MIT Press Journals
2019
Abstract
It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and...
An efficient uniform-segmented neuron model for large-scale neuromorphic circuit design: Simulation and FPGA synthesis results
, Article IEEE Transactions on Circuits and Systems I: Regular Papers ; Volume 66, Issue 6 , 2019 , Pages 2336-2349 ; 15498328 (ISSN) ; Abolfathi, H ; Ahmadi, A ; Ahmadi, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Large-scale simulation of spiking neural networks on hardware with a remarkable resemblance to their mathematical models is a key objective of the neuromorphic discipline. This issue is, however, considerably resource-intensive due to the presence of nonlinear terms in neuron models. This paper proposes a novel uniform piecewise linear segmentation approach for nonlinear function evaluations. Employing the proposed approach, we present a uniform-segmented adaptive exponential neuron model capable of accurately producing various responses exhibited by the original model and suitable for efficient large-scale implementation. In contrast to previous nonuniform-segmented neuron models, the...