time dynamic model Search Results


non-fragile finite-time filter design for time-delayed markovian jump systems via tcs fuzzy model approach  (Nonlinear Dynamics)
90
Nonlinear Dynamics non-fragile finite-time filter design for time-delayed markovian jump systems via tcs fuzzy model approach
Non Fragile Finite Time Filter Design For Time Delayed Markovian Jump Systems Via Tcs Fuzzy Model Approach, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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linear time-invariant model  (Nonlinear Dynamics)
90
Nonlinear Dynamics linear time-invariant model
Linear Time Invariant Model, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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linear time-invariant model - by Bioz Stars, 2026-04
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panel and time series models  (Nonlinear Dynamics)
90
Nonlinear Dynamics panel and time series models
Panel And Time Series Models, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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hierarchical autoencoders in time (hat)  (Cortical Dynamics)
90
Cortical Dynamics hierarchical autoencoders in time (hat)
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Hierarchical Autoencoders In Time (Hat), supplied by Cortical Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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modelling of warm dense hydrogen via explicit real time electron dynamics: dynamic structure factors  (Dornheim Medical Images)
90
Dornheim Medical Images modelling of warm dense hydrogen via explicit real time electron dynamics: dynamic structure factors
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Modelling Of Warm Dense Hydrogen Via Explicit Real Time Electron Dynamics: Dynamic Structure Factors, supplied by Dornheim Medical Images, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/modelling of warm dense hydrogen via explicit real time electron dynamics: dynamic structure factors/product/Dornheim Medical Images
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modelling of warm dense hydrogen via explicit real time electron dynamics: dynamic structure factors - by Bioz Stars, 2026-04
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time-space fractional soliton neuron model (tsfsnm)  (Nonlinear Dynamics)
90
Nonlinear Dynamics time-space fractional soliton neuron model (tsfsnm)
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Time Space Fractional Soliton Neuron Model (Tsfsnm), supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/time-space fractional soliton neuron model (tsfsnm)/product/Nonlinear Dynamics
Average 90 stars, based on 1 article reviews
time-space fractional soliton neuron model (tsfsnm) - by Bioz Stars, 2026-04
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forecasting model of the regulatory dynamics of flowering-time genes  (SATAKE)
90
SATAKE forecasting model of the regulatory dynamics of flowering-time genes
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Forecasting Model Of The Regulatory Dynamics Of Flowering Time Genes, supplied by SATAKE, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/forecasting model of the regulatory dynamics of flowering-time genes/product/SATAKE
Average 90 stars, based on 1 article reviews
forecasting model of the regulatory dynamics of flowering-time genes - by Bioz Stars, 2026-04
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continuous-time plant model  (Nonlinear Dynamics)
90
Nonlinear Dynamics continuous-time plant model
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Continuous Time Plant Model, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/continuous-time plant model/product/Nonlinear Dynamics
Average 90 stars, based on 1 article reviews
continuous-time plant model - by Bioz Stars, 2026-04
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garch model  (Nonlinear Dynamics)
90
Nonlinear Dynamics garch model
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Garch Model, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/garch model/product/Nonlinear Dynamics
Average 90 stars, based on 1 article reviews
garch model - by Bioz Stars, 2026-04
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discrete-time model  (Nonlinear Dynamics)
90
Nonlinear Dynamics discrete-time model
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Discrete Time Model, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/discrete-time model/product/Nonlinear Dynamics
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discrete-time model - by Bioz Stars, 2026-04
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time-scale modeling of dynamic networks with applications to power systems  (Verlag GmbH)
90
Verlag GmbH time-scale modeling of dynamic networks with applications to power systems
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Time Scale Modeling Of Dynamic Networks With Applications To Power Systems, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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time-scale modeling of dynamic networks with applications to power systems - by Bioz Stars, 2026-04
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changing dynamics: time-varying autoregressive models using generalized additive modeling  (KU Leuven)
90
KU Leuven changing dynamics: time-varying autoregressive models using generalized additive modeling
(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) <t>HAT</t> model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical <t>autoencoders</t> in time, AT = autoencoder in time, rSI = intact-scramble ISPC.
Changing Dynamics: Time Varying Autoregressive Models Using Generalized Additive Modeling, supplied by KU Leuven, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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changing dynamics: time-varying autoregressive models using generalized additive modeling - by Bioz Stars, 2026-04
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Image Search Results


(A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) HAT model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical autoencoders in time, AT = autoencoder in time, rSI = intact-scramble ISPC.

Journal: Neuron

Article Title: Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

doi: 10.1016/j.neuron.2020.02.013

Figure Lengend Snippet: (A) HLI model schematic: the new state of each unit is a linear weighted sum of its old state and its new input. (B) HAT model schematic: each region maintains a representation of temporal context, which is combined with new input to form a simplified joint representation. (C) An AT unit, in which local context CNTX is updated via hidden representation HID and current input IN, modulated by time constant τ and “surprise” α. α is computed via auto-associative error Δ and a scaling parameter k. (D) In HAT, the input to level i is gated by surprise α from level (i-1). (E) HLI simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (F) HAT simulation of rSIDE:CE predicts longer alignment time at higher stages of processing. (G) Empirical rSIDE:CE results grouped by alignment time, consistent with predictions of both HLI and HAT. (H) HLI simulation of rSICD:CE predicts that regions that construct context slowly will also forget context slowly. (I) HAT simulations predict that the timescale of context separation (rSICD:CE) need not be slower in levels of the model with longer alignment times (rSIDE:CE). (J) Empirical rSICD:CE results grouped by alignment time. HLI = hierarchical linear integrator, HAT = hierarchical autoencoders in time, AT = autoencoder in time, rSI = intact-scramble ISPC.

Article Snippet: Gated integration using hierarchical autoencoders in time (HAT) The mismatch of alignment and separation times in cortical dynamics indicates that the integration rate is variable, consistent with the notion that temporal sequences are grouped into events, and that prior context is more rapidly forgotten at event boundaries ( Reynolds et al., 2007 ; Zacks and Tversky, 2001 ).

Techniques: Construct