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Neural network architecture
Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance
Time_delay_neural_network
Branch of machine learning
machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Computational model used in machine learning
neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological neural networks
Neural network (machine learning)
Neural_network_(machine_learning)
Type of feedforward neural network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep
Convolutional_neural_network
Neural network working on two input vectors
A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on
Siamese_neural_network
Class of artificial neural network
artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where
Recurrent_neural_network
American computer scientist
machine learning, he is known for the Time Delay Neural Network (TDNN), the first Convolutional Neural Network (CNN) trained by gradient descent, using
Alex_Waibel
Type of artificial neural network
input information available to the network. For example, multilayer perceptron (MLPs) and time delay neural network (TDNNs) have limitations on the input
Bidirectional recurrent neural networks
Bidirectional_recurrent_neural_networks
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs
History of artificial neural networks
History_of_artificial_neural_networks
British-Canadian computer scientist (born 1947)
Sejnowski. His other contributions to neural network research include distributed representations, time delay neural network, mixtures of experts, Helmholtz
Geoffrey_Hinton
French-born researcher in machine learning (born 1961)
Roopak Shah, Signature verification using a" siamese" time delay neural network, Advances in Neural Information Processing Systems, 1994. Isabelle Guyon
Isabelle_Guyon
Automatic conversion of spoken language into text
(RNNs), Time Delay Neural Networks (TDNN's), and transformers demonstrated improved performance. Researchers are exploring deep neural networks (DNNs)
Speech_recognition
Machine learning technique
They trained 6 experts, each being a "time-delayed neural network" (essentially a multilayered convolution network over the mel spectrogram). They found
Mixture_of_experts
Classification of Artificial Neural Networks (ANNs)
Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers,
Types of artificial neural networks
Types_of_artificial_neural_networks
Research facility in California, USA
(2011). "Fast pattern matching with time-delay neural networks". International Joint Conference on Neural Networks.{{cite journal}}: CS1 maint: multiple
HRL_Laboratories
Brainwaves, repetitive patterns of neural activity in the central nervous system
Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory
Neural_oscillation
Network or circuit of neurons
another to form large scale brain networks. Neural circuits have inspired the design of artificial neural networks, though there are significant differences
Neural_circuit
Tunisian–Canadian electrical engineer
perceptron model to handle dynamics and proposed a real-valued time delay neural network architecture. In May 2009, Dr. Ghannouchi invented the multi-dimensional
Fadhel_M._Ghannouchi
Parallel computing paradigm
learning, Cellular Neural Networks (CNN) or Cellular Nonlinear Networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference
Cellular_neural_network
Class of dynamic neutral networks
Early stopping Convolutional neural network Machine learning Time delay neural network "Early-Exit Deep Neural Network - A Comprehensive Survey". ACM
Early-exit_network
NEST is a simulation software for spiking neural network models, including large-scale neuronal networks. NEST was initially developed by Markus Diesmann
NEST_(software)
Predicting future value of company stock
networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (RNN) or time delay neural network (TDNN)
Stock_market_prediction
Method by which information is represented in the brain
relationships among networks of neurons in an ensemble. Action potentials, which act as the primary carrier of information in biological neural networks, are generally
Neural_coding
French mathematician and computer scientist (born 1965)
package for simulating artificial neural networks. His master's thesis concerned using Time Delay Neural Networks for speech recognition. He then joined
Léon_Bottou
School for computer science in the United States
on machine learning, he is known for the Time Delay Neural Network, the first Convolutional Neural Network trained by gradient descent, using backpropagation
Carnegie Mellon School of Computer Science
Carnegie_Mellon_School_of_Computer_Science
Cellular automaton model for spiking neural networks
for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a
CoDi
Optimization algorithm for artificial neural networks
used for training a neural network in computing parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation efficiently
Backpropagation
1943 paper proposing artificial neural networks
realized by a neural network iff there exists a time-delay T ≥ 0 {\displaystyle T\geq 0} , a neuron i {\displaystyle i} in the network, and an initial
A Logical Calculus of the Ideas Immanent in Nervous Activity
A_Logical_Calculus_of_the_Ideas_Immanent_in_Nervous_Activity
Theorem in Mathematics
particular, the stability of industrial processes with dead-time and delayed neural networks. Let t 0 {\displaystyle t_{0}} be a real number and τ {\displaystyle
Halanay_inequality
Network that allows computers to share resources and communicate with each other
still require a small amount of time to regenerate the signal. This can cause a propagation delay that affects network performance and may affect proper
Computer_network
Perception of events' position in time
temporal illusions help to expose the underlying neural mechanisms of time perception. Pioneering work on time perception, emphasizing species-specific differences
Time_perception
Subset of artificial intelligence
recognition and information retrieval. Neural network research was abandoned by AI and computer science around the same time. This subfield, termed "connectionism"
Machine_learning
Type of recurrent neural network with random and non-trainable internal structure
Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational
Reservoir_computing
Phase transition in machine learning
relatively shallow models, grokking has been observed in deep neural networks and non-neural models and is the subject of active research. One potential
Grokking_(machine_learning)
Study of speech signals and the processing methods of these signals
the value of the hidden variable x(t) (both at time t).[citation needed] An artificial neural network (ANN) is based on a collection of connected units
Speech_processing
Resistance of an immediate reward in return for a greater reward later
Jonides, John; Berman, Marc G.; et al. (2011). "Behavioral and neural correlates of delay of gratification 40 years later". Proceedings of the National
Delayed_gratification
Model-free reinforcement learning algorithm
instabilities when the value function is approximated with an artificial neural network. In that case, starting with a lower discount factor and increasing
Q-learning
Map used to model a biological neuron
and simplifies the computation of large neural networks. The Rulkov map, with n {\displaystyle n} as discrete time, can be represented by the following dynamical
Rulkov_map
Methods of estimating differential entropy given some observations
analysis, genetic analysis, speech recognition, manifold learning, and time delay estimation it is useful to estimate the differential entropy of a system
Entropy_estimation
Algorithm for supervised learning of binary classifiers
weights with the feature vector. The artificial neuron and artificial neural network were invented in 1943 by Warren McCulloch and Walter Pitts in their
Perceptron
simple neurons often used in Artificial neural networks. Linearity may occur in the basic elements of a neural circuit such as the response of a postsynaptic
Models_of_neural_computation
British computer scientist
Ouazzane, H. Kazemian, Y. Jing and R. Boyd (2009) ‘ Focused Time Delay Neural Network Modelling Towards Typing Stream Prediction' IADIS multiple on
Karim_Ouazzane
Autonomous car program developed by Carnegie Mellon
output layer, with one-step delay in the style of the Jordan network. It was designed to provide rudimentary processing of time The output layer consisted
Navlab
Turkish-French computer scientist (born 1945)
mathematician, known for inventing the Random neural network and his pioneering work in computer system and network performance. His academic career spans several
Erol_Gelenbe
Identification of nonlinear systems
by a model class: Volterra series models, Block-structured models, Neural network models, NARMAX models, and State-space models. There are four steps
Nonlinear system identification
Nonlinear_system_identification
Arbiter on a node in a packet switching communication network
of modern network configurations. For instance, a supervised neural network (NN)-based scheduler has been introduced in cell-free networks to efficiently
Network_scheduler
leveraging the powerful function approximation capabilities of deep neural networks, deep BSDE addresses the computational challenges faced by traditional
Deep backward stochastic differential equation method
Deep_backward_stochastic_differential_equation_method
different algorithms including Vector regression (SVR), time-delay neural network (TDNN) or Bayesian network. Newer methodologies taking into account data relational
Urban traffic modeling and analysis
Urban_traffic_modeling_and_analysis
PMID 33205815. Reese, Martin G (2001-12-01). "Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome"
List of gene prediction software
List_of_gene_prediction_software
Artificial neural network
Confidence Propagation Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem, which regards neural computation and processing
BCPNN
2023 text-generating language model
positions at Musk's company. While OpenAI released both the weights of the neural network and the technical details of GPT-2, and, although not releasing the
GPT-4
Voice conversion software
Conversion". Neural Processing Letters. 56 (3) 166. doi:10.1007/s11063-024-11613-0. Du, Hongqiang (2020). "Optimizing Voice Conversion Network with Cycle
Retrieval-based Voice Conversion
Retrieval-based_Voice_Conversion
American control theorist
predictors to time- and state-dependent delays, to delay-adaptive control for unknown delays, and to sampled-data implementation. PRESCRIBED-TIME CONTROL.
Miroslav_Krstić
Visual representation of the spectrum of frequencies of a signal as it varies with time
an approach to the classification of respiration states based on a neural network model. Acoustic signature Chromagram Fourier analysis for computing
Spectrogram
Mathematical function having a characteristic S-shaped curve or sigmoid curve
section. In some fields, most notably in the context of artificial neural networks, the term "sigmoid function" is used as a synonym for "logistic function"
Sigmoid_function
Telecoms software company
Non-Terrestrial Networks (NTN) for satellite communications, and 6G network development. Pulsone family includes a real-time neural receiver developed
Cohere_Technologies
Intelligence in machines
space search and mathematical optimisation, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics
Artificial_intelligence
Audio track separation technique
applicable to stem-based audio are provided. Neural networks Convolutional neural networks (CNNs) Recurrent neural networks (RNNs) and transformers Source separation
Music_source_separation
Biological neuron model
neural networks; and in the neurosciences to predict the subthreshold voltage and the firing times of cortical neurons during stimulation with a time-dependent
Spike_response_model
Lossy audio compression applied to human speech
pulse-code modulation (ADPCM) G.722 for VoIP Neural speech coding Lyra (Google): V1 uses neural network reconstruction of log-mel spectrogram; V2 is an
Speech_coding
Applications of machine learning to quantum physics
In machine learning, physics-informed neural networks (PINNs), also referred to as theory-trained neural networks (TTNs), are a type of universal function
Machine_learning_in_physics
Behavior observed in neurons
information throughout the neural network. The study of neuron synchrony could provide information on the differences that occur in neural states such as normal
Phase_resetting_in_neurons
Collection of molecular regulators
MR, Clement M, Martinez T, Snell Q (2010). "Time Series Gene Expression Prediction using Neural Networks with Hidden Layers" (PDF). Proceedings of the
Gene_regulatory_network
Techniques to improve network performance
within a specified time (a function of the estimated round-trip delay time), the sender will assume the segment was lost in the network and will retransmit
TCP_congestion_control
Ability of the brain to continuously change
Neuroplasticity, also known as neural plasticity or just plasticity, is the medium of neural networks in the brain to change through growth and reorganization
Neuroplasticity
the time delay between input and output into a voltage applied to the ionic liquid that either drives ions into the SNO or removes them. A network of such
Synaptic_transistor
System whose output depends on moment of observation and input signal application
words, a time delay or time advance of input not only shifts the output signal in time but also changes other parameters and behavior. Time variant systems
Time-variant_system
Digital circuit
"Towards a High Performance Neural Branch Predictor" (PDF). Proceedings International Journal Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN
Branch_predictor
Mexican control theorist
in intelligent control, and in particular in the use of artificial neural networks for applications including the control of electric motors, robot manipulators
Alma_Y._Alanís
Graph where most nodes are reachable in a small number of steps
connectomics and network neuroscience, have found the small-worldness of neural networks to be associated with efficient communication. In neural networks, short
Small-world_network
Network whose links change over time
time resolved physical proximity networks has been used to improve epidemic modeling. Neural networks and brain networks can be represented as time-varying
Temporal_network
Canadian academic
developed an optical neural network (ONN) that isolates specific transmissions and identifies signals in real time with a processing delay of less than 15
Bhavin_J._Shastri
Nature, 381, 610–613. Wang, D.(2005). "The time dimension of scene analysis". IEEE Transactions on Neural Networks, 16(6), 1401–1426. Bregman, A.(1990). Auditory
Computational auditory scene analysis
Computational_auditory_scene_analysis
Acoustic technology to locate the source of a sound in three-dimensional space
techniques for optimal results, such as neural network, maximum likelihood and Multiple signal classification (MUSIC). Real-time methods using an Acoustic Vector
3D_sound_localization
Correlation of brain activity across two or more people over time
Neural synchrony is the correlation of brain activity across two or more people over time. In social and affective neuroscience, neural synchrony specifically
Neural_synchrony
Field of machine learning
for reinforcement learning in neural networks". Proceedings of the IEEE First International Conference on Neural Networks. CiteSeerX 10.1.1.129.8871. {{cite
Reinforcement_learning
Continuous progression from past to future
Blanke, Olaf (18 June 2008). "Self in Time: Imagined Self-Location Influences Neural Activity Related to Mental Time Travel". The Journal of Neuroscience
Time
Distributed Processing (PDP)), Biological neural network, Artificial neural network (a.k.a. Neural network), Computational neuroscience, as well as in
Nervous_system_network_models
Optical illusion
flash-lag effect is that the visual system is predictive, accounting for neural delays by extrapolating the trajectory of a moving stimulus into the future
Flash_lag_illusion
effects are typically only observed after a considerable delay, these studies have focused on the neural changes occurring during the consolidation period.
Varied_practice
Type of cognitive prosthesis
suffer damage from Alzheimer's, stroke, or injury, the disruption of neural networks often stops long-term memories from forming. The system designed by
Hippocampal_prosthesis
Canadian neuroscientist (born 1943)
research focuses on the neural substrates of visual perception and visuomotor control. In 2025, Goodale was named the top life-time scholar of the visual
Melvyn_A._Goodale
List of concepts in artificial intelligence
manner without delay or overshoot and ensuring control stability. convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet)
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Approach to control of non-linear systems
using Machine learning, such as Adaptive control based on Artificial Neural Networks (ANN) and Reinforcement Learning, have been studied. Linear parameter-varying
Gain_scheduling
Type of connection between neurons
gap junction between two neurons. Electrical synapses are often found in neural systems that require the fastest possible response, such as defensive reflexes
Electrical_synapse
Measure of delay in executing operations
tardiness is a measure of a delay in executing certain operations and earliness is a measure of finishing operations before due time. The operations may depend
Tardiness_(scheduling)
Characterizes a recent neural network architecture providing advances in time series deep learning
In deep learning, state space models (SSMs) are a type of neural network used to process long streams of sequential data, such as text, speech, or sensor
State space model (deep learning)
State_space_model_(deep_learning)
Signal processing computational method
Jutten, C. (1986). Space or time adaptive signal processing by neural networks models. Intern. Conf. on Neural Networks for Computing (pp. 206-211).
Independent component analysis
Independent_component_analysis
Coordination of events to operate a system in unison
2015). "Plasticity of brain wave network interactions and evolution across physiologic states". Frontiers in Neural Circuits. 9 62. doi:10.3389/fncir
Synchronization
Concept in decision-making
predictions. For neural network–based agents, the NoisyNet method changes some of its neural network modules by noisy versions. That is, some network parameters
Exploration–exploitation dilemma
Exploration–exploitation_dilemma
Phenomenon of visual perception
produce less of an effect on the target, as the target has had more time to form a full neural representation in the brain. Polat, Sterkin, and Yehezkel went
Visual_masking
American computer engineer
stock trend prediction using time delay, recurrent and probabilistic neural networks." IEEE Transactions on neural networks 9.6 (1998): 1456–1470. (cited
Donald_Wunsch
Branch of neuroscience
cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory; although
Computational_neuroscience
Ability to learn associations between unrelated objects
considered as an emergent feature of the nonlinear dynamics of large neural networks. More recent experimental discovery of the so-called concept or grandmother
Associative memory (psychology)
Associative_memory_(psychology)
Mexican computer scientist
learning approaches including support vector machines and artificial neural networks applied to robot motion planning, computer vision and related problems
Nancy_Arana-Daniel
Theory of brain function
Adaptive resonance theory, a neural network architecture developed by Stephen Grossberg. Computational neuroscience Neural Darwinism Predictive coding
Memory-prediction_framework
Neural tube defect in which the brain protrudes out of the skull
Encephalocele is a neural tube defect characterized by sac-like protrusions of the brain and the membranes that cover it through openings in the skull
Encephalocele
Interdisciplinary research area
between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum
Quantum_machine_learning
Computer hardware and software capable of playing chess
Stockfish, rely on efficiently updatable neural networks, tailored to be run exclusively on CPUs, but Lc0 uses networks reliant on GPU performance. Top engines
Computer_chess
Biological process that adjusts the strength of connections between neurons in the brain
of artificial spiking neural networks. Using this approach the weight of a connection between two neurons is increased if the time at which a presynaptic
Spike-timing-dependent plasticity
Spike-timing-dependent_plasticity
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TIME DELAY-NEURAL-NETWORK
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