NeuronsJohn HopfieldComputationConvolutional neural network1995Conference on Neural NetworksRecurrentBackpropagationAssociative memoriesDynamics1996IEEE TransBiologicalArchitecturesCognitiveAdaptiveCircuitsSynchronization1999NIPSNonlinearComputational propertiesTransactions1991DynamicalTheorySystemsNeuronModelModelsBehaviourSensoryParameterSpin glassesExhibitPrinciplesCurrentsInspirationDecadesComputingCircuitCellularDeepBrain
Neurons9
- Hopfield J: Neurons with graded response have collective computational properties like those of two-state neurons. (biomedcentral.com)
- Poon, C.-S. & Zhou, K. Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. (nature.com)
- Neural Networks The brain is built and works by multiple networks of neurons, synapses and axons in constant flux due to weighted inputs and experience. (naturalgenesis.net)
- A neural network is a neural circuit of biological neurons , sometimes also called a biological neural network , or a network of artificial neurons or nodes in the case of an artificial neural network . (cloudfront.net)
- A biological neural network is composed of a group of chemically connected or functionally associated neurons. (cloudfront.net)
- A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. (cloudfront.net)
- Neural network theory has served to identify better how the neurons in the brain function and provide the basis for efforts to create artificial intelligence. (cloudfront.net)
- The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. (elifesciences.org)
- Dynamics of a recurrent network of spiking neurons before and following learning. (billhowell.ca)
John Hopfield1
- [10] [9] His learning RNN was popularised by John Hopfield in 1982. (cloudfront.net)
Computation6
- In 2020, we are celebrating the 10-year anniversary of our publication [MLP1] in Neural Computation (2010) on deep multilayer perceptrons trained by plain gradient descent on GPU. (idsia.ch)
- Neural Computation, 5(1):140-153. (billhowell.ca)
- Neural Computation, 10(2):251-276. (billhowell.ca)
- Network: Computation in Neural Systems, 8(4):373-404. (billhowell.ca)
- Neural Computation, 8(3):643-674. (billhowell.ca)
- Neural Computation, 4:559-572. (billhowell.ca)
Convolutional neural network1
- ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars. (nature.com)
19952
- Hopfield and Herz 1995). (scholarpedia.org)
- Obtuvo el título de M.S. en Ingenieria de las Comunicaciones en la ETSI Telecomunicaciones de la Universidad Politécnica de Cataluña en 1990 y el Ph.D. en Telecomunicaciones en la Universitat Politècnica de València en 1995. (upv.es)
Conference on Neural Networks1
- In IEEE 1st International Conference on Neural Networks, San Diego, volume 2, pages 609-618. (billhowell.ca)
Recurrent4
- Artificial neural network (ANN) methods in general fall within this category, and par- ticularly interesting in the context of optimization are recurrent network methods based on deterministic annealing. (lu.se)
- Goetz, P., Walters, D.: The dynamics of recurrent behaviour networks. (crossref.org)
- Wilhelm Lenz (1920) and Ernst Ising (1925) created and analyzed the Ising model [8] which is essentially a non-learning artificial recurrent neural network (RNN) consisting of neuron-like threshold elements. (cloudfront.net)
- New results on recurrent network training: unifying the algorithms and accelerating convergence. (billhowell.ca)
Backpropagation3
- The first system is an adaptive traffic signal light controller based upon the Hopfield neural network model, while the second system is a backpropagation model trained to predict urban traffic congestion. (aaai.org)
- This optimization procedure moves backwards through the network in an iterative manner to minimize the difference between desired and actual outputs (backpropagation). (jneurosci.org)
- Dynamic node creation in backpropagation neural networks. (billhowell.ca)
Associative memories1
- 20. D. L. Lee and Thomas C. Chuang, "Design of General Bidirectional Associative Memories with Improved Recall Capability," International Journal of Neural Systems, Vol. 14, No. 5, pp. 325-328, October 2004. (vnu.edu.tw)
Dynamics3
- M. Löwe and F. Vermet, The Capacity of q -state Potts neural networks with parallel retrieval dynamics. (esaim-ps.org)
- Universality Similar self-organized complex adaptive network dynamics are found throughout nature from cosmos to civilization. (naturalgenesis.net)
- but the network properties that underlie such dynamics are poorly understood. (elifesciences.org)
19961
- Neural Networks 7 (1996) 803-815. (esaim-ps.org)
IEEE Trans1
- 10. D. L. Lee and Thomas C. Chuang, "Designing Asymmetric Hopfield-type Associative Memory with Higher Order Hamming Stability", IEEE Trans. (vnu.edu.tw)
Biological4
- Chung, S. and Abbott, L.F. (2021) Neural Population Geometry: An Approach for Understanding Biological and Artificial Neural Networks. (columbia.edu)
- Artificial intelligence, cognitive modelling, and neural networks are information processing paradigms inspired by how biological neural systems process data. (cloudfront.net)
- Artificial intelligence and cognitive modelling try to simulate some properties of biological neural networks. (cloudfront.net)
- On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems. (cloudfront.net)
Architectures2
- Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. (nature.com)
- González, P.P., Negrete, J.: REDSIEX: A cooperative network of expert systems with blackboard architectures. (crossref.org)
Cognitive3
- ABSTRACT When connectionist networks are used to design high-level cognitive models, the comparison with symbolic AI becomes unavoidable, as well as fundamental representational issues. (ucsd.edu)
- INTRODUCTION Neural networks have not only offered new techniques for practical applications (such as pattern recognition or optimization problems), but they have also opened new avenues for cognitive modeling (Rumelhart & McClelland 86). (ucsd.edu)
- THE QUESTION OF REPRESENTATIONS Now if one is to take seriously neural networks as cognitive models, the question of representations becomes inescapable. (ucsd.edu)
Adaptive6
- Artificial intelligence and neural network are promising technologies that provide intelligent, adaptive performance in a variety of application domains. (aaai.org)
- Tai, Chia-Shing, Jheng-Huang Hong, De-Yang Hong, and Li-Chen Fu, "A Real-time Demand-side Management System Considering User Preference with Adaptive Deep Q Learning in Home Area Network," Sustainable Energy, Grids and Networks, DOI: 10.1016/j.segan.2021.100572, (SCI) 2021. (ntu.edu.tw)
- Wu, Jim-Wei, Kuang-Yao Chang, and Li-Chen Fu, "Adaptive Under-sampling Deep Neural Network for Rapid and Reliable Image Recovery in Confocal Laser Scanning Microscope Measurements," IEEE Transactions on Instrumentation & Measurement, DOI : 10.1109/TIM.2021.3134324, (SCI) 2021. (ntu.edu.tw)
- These artificial networks may be used for predictive modeling , adaptive control and applications where they can be trained via a dataset. (cloudfront.net)
- In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition , image analysis and adaptive control , in order to construct software agents (in computer and video games ) or autonomous robots . (cloudfront.net)
- Adaptive dropout for training deep neural networks. (billhowell.ca)
Circuits3
- Abbott, L.F. and Svoboda, K., editors (2020) Brain-wide Interactions Between Neural Circuits. (columbia.edu)
- As is well known, conventional NNs can be implemented by circuits: the Hopfield NNs models [ 1 ] can be implemented in a very large-scale electronic circuit where the self feedback connection weights are implemented by resistors, in which resistors are applied to emulate neural synapses. (springeropen.com)
- Based on different initial conditions, Pecora and Carroll [ 13 ] have studied synchronization analysis for two identical chaotic systems in 1990, which is the first idea of theoretical research on synchronization and the approach was improved in electronic circuits. (springeropen.com)
Synchronization10
- Crook SM, Ermentrout GB, Bower JM: Spike frequency adaptation affects the synchronization properties of networks of cortical oscillations. (biomedcentral.com)
- Crook SM, Ermentrout GB, Vanier MC, Bower JM: The role of axonal delay in the synchronization of networks of coupled cortical oscillators. (biomedcentral.com)
- By designing a simple feedback controller, the fixed-time synchronization of neural networks with discrete delay is investigated based on the fixed-time stability theorem established in this paper. (hindawi.com)
- In recent years, considerable results have been reported about the stability and synchronization of neural networks [ 6 - 12 ]. (hindawi.com)
- Recently, the finite-time synchronization of neural networks has been intensively studied [ 14 - 22 ]. (hindawi.com)
- In [ 14 ], the finite-time synchronization of neural networks with mixed delays and perturbations was investigated. (hindawi.com)
- In [ 19 ], the finite-time synchronization of Cohen-Grossberg neural networks was studied via delayed feedback control. (hindawi.com)
- As typical nonlinear systems, neural networks usually present some strange dynamic behaviors, so the fixed-time synchronization of neural networks can also be applied in many fields, such as secure communication and image encryption. (hindawi.com)
- Based on the fixed-time stability theorems in [ 23 , 24 , 29 ], there have been some publications about the fixed-time synchronization of neural networks [ 29 - 34 ]. (hindawi.com)
- This paper addresses the anti-synchronization control problem of memristor-based distributed parameter neural networks (MDPNNs) with time-varying delays. (springeropen.com)
19991
- Neural Networks 12 (1999) 1377-1389. (esaim-ps.org)
NIPS2
- In Advances in Neural Information Processing Systems (NIPS), pages 3084-3092. (billhowell.ca)
- In Advances in neural information processing systems 12 (NIPS), pages 968-974. (billhowell.ca)
Nonlinear2
- Since the memristor has been successfully utilized as a notional tool to analyze signals and nonlinear semiconductor devices, now it is becoming an interesting problem to investigate the memristor-based neural networks (MNNs) because of its practical applications of the NNs [ 9 - 12 ]. (springeropen.com)
- Its late version is expressed by Rosetta-like translations of the sciences of nonlinear, self-organizing, networked complexity. (naturalgenesis.net)
Computational properties1
- G. Dreyfus, I. Guyon and L. Personnaz, Collective computational properties of neural networks: New learning mechanisms. (esaim-ps.org)
Transactions1
- IEEE Transactions on Neural Networks, 11(3):697-709. (billhowell.ca)
19911
- Network 2 (1991) 237-243. (esaim-ps.org)
Dynamical1
- By means of numerical simulations, the dynamical behaviour of a Neural Network composed of two Hopfield Subnetworks interconnected unidirectionally and updated synchronically (at zero temperature T =0) is studied. (journaldephysique.org)
Theory2
- To appear in The Handbook of Brain Theory and Neural Networks, (2nd edition), M.A. Arbib (ed. (lu.se)
- The general scientific community at the time was skeptical of Bain's [4] theory because it required what appeared to be an inordinate number of neural connections within the brain. (cloudfront.net)
Systems4
- Artificial neural systems. (deepdyve.com)
- Hopfield J: Neural networks and physical systems with emergent collective computational abilities. (biomedcentral.com)
- This paper describes two separate neural network systems that have been developed for integration into a ATMS blackboard architecture [Gilmore et al. (aaai.org)
- Such networks resemble statistical models of magnetic systems ("spin glasses"), with an atomic spin state (up or down) seen as analogous to the "firing" state of a neuron (on or off). (lu.se)
Neuron2
- Data mining is also used as a tool for the construction of computer graphics as solutions to the TSP and also for the activation of an output neuron for a three‐layer feed‐forward network that is trained using a Boolean function. (deepdyve.com)
- a , The network consists of many simple computing nodes, each simulating a neuron, and organized in a series of layers. (jneurosci.org)
Model8
- On the other hand, a very different functional structure has recently been proposed based on a detailed biologically realistic network model of the olfactory cortex [ 11 ]. (biomedcentral.com)
- O'Connor S, Angelo K, Jacob TJC: Burst firing versus synchrony in a gap junction connected olfactory bulb mitral cell network model. (biomedcentral.com)
- A. Bovier, Sharp upper bounds for perfect retrieval in the Hopfield model. (esaim-ps.org)
- A. Bovier and V. Gayrard, Hopfield models as a generalized mean field model , preprint. (esaim-ps.org)
- M. Löwe and F. Vermet, The storage capacity of the Hopfield model and moderate deviations. (esaim-ps.org)
- Unlike the von Neumann model, neural network computing does not separate memory and processing. (cloudfront.net)
- His model, by focusing on the flow of electrical currents, did not require individual neural connections for each memory or action. (cloudfront.net)
- [11] McCulloch and Pitts [12] (1943) also created a computational model for neural networks based on mathematics and algorithms. (cloudfront.net)
Models3
- M. Löwe, On the storage capacity of Hopfield models with weakly correlated patterns. (esaim-ps.org)
- Artificial neural networks are computational network models inspired by signal processing in the brain. (nature.com)
- These early models paved the way for neural network research to split into two distinct approaches. (cloudfront.net)
Behaviour1
- Brooks, R.A.: A robot that walks: Emergent behaviour from a carefully evolved network. (crossref.org)
Sensory1
- Then discusses the applications of data mining for the construction of graphical mappings of the sensory space as a two‐dimensional neural network grid as well as the traveling salesman problem (TSP) and simulated annealing. (deepdyve.com)
Parameter1
- We find that player strength scales as a power law in neural network parameter count when not bottlenecked by available compute, and as a power of compute when training optimally sized agents. (uni-frankfurt.de)
Spin glasses2
- In Mathematics of spin glasses and neural networks , A. Bovier and P. Picco (Eds. (esaim-ps.org)
- Mathematical aspects of spin glasses and neural networks , in A. Bovier and P. Picco (Eds. (esaim-ps.org)
Exhibit1
- The process of learning involves optimizing connection weights between nodes in successive layers to make the neural network exhibit a desired behavior ( Fig. 1 b ). (jneurosci.org)
Principles1
- In this viewpoint, we advocate that deep learning can be further enhanced by incorporating and tightly integrating five fundamental principles of neural circuit design and function: optimizing the system to environmental need and making it robust to environmental noise, customizing learning to context, modularizing the system, learning without supervision, and learning using reinforcement strategies. (jneurosci.org)
Currents1
- Khajeh, R., Fumarola, F. and Abbott, L.F. (2022) Sparce Balance: Excitatory-Inhibitory Networks with Small Bias Currents and Broadly Distributed Synaptic Weights. (columbia.edu)
Inspiration2
- Although a complete characterization of the neural basis of learning remains ongoing, scientists for nearly a century have used the brain as inspiration to design artificial neural networks capable of learning, a case in point being deep learning. (jneurosci.org)
- This similarity has been the source of much inspiration for neural network studies. (lu.se)
Decades1
- Misra, J. & Saha, I. Artificial neural networks in hardware: a survey of two decades of progress. (nature.com)
Computing4
- a descendent of classical artificial neural networks ( Rosenblatt, 1958 ), comprises many simple computing nodes organized in a series of layers ( Fig. 1 ). (jneurosci.org)
- However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. (nature.com)
- Convolutional networks for fast, energy efficient neuromorphic computing. (nature.com)
- Hybrid computing using a neural network with dynamic external memory. (nature.com)
Circuit2
- In addition, complex switching phenomena and the switching rule depending on the state of the network are shown by memristor-based circuit networks. (springeropen.com)
- A neural circuit mechanism of similarity-based pattern match decision making. (cshl.edu)
Cellular1
- Scale-Free Networks Elemental nodes interlink from cellular metabolisms to ecosystems and the Internet. (naturalgenesis.net)
Deep4
- A schematic of a deep learning neural network for classifying images. (jneurosci.org)
- Surprisingly, our simple but unusually deep supervised artificial neural network (NN) outperformed all previous methods on the (back then famous) machine learning benchmark MNIST. (idsia.ch)
- Mastering the game of go with deep neural networks and tree search. (nature.com)
- The recent observation of neural power-law scaling relations has made a significant impact in the field of deep learning. (uni-frankfurt.de)
Brain1
- Second, although these key brain cells form appropriate connections to act as a clock, it is still not clear whether and how the network uses these connections during singing. (elifesciences.org)