The Experts below are selected from a list of 8517 Experts worldwide ranked by ideXlab platform
Anders Lansner - One of the best experts on this subject based on the ideXlab platform.
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spike based bayesian Hebbian Learning of temporal sequences
PLOS Computational Biology, 2016Co-Authors: Philip Tully, Henrik Linden, Matthias H Hennig, Anders LansnerAbstract:Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) Learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the Learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative Learning in unison.
Muhammad Umair - One of the best experts on this subject based on the ideXlab platform.
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on rationality of decision models incorporating emotion related valuing and Hebbian Learning
International Conference on Neural Information Processing, 2011Co-Authors: Jan Treur, Muhammad UmairAbstract:In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Four different variations of Hebbian Learning are considered for different types of connections in the decision model. To assess the extent of rationality, a measure is introduced reflecting the environment’s behaviour. Simulation results and the extents of rationality of the different models over time are presented and analysed.
Mathias Quoy - One of the best experts on this subject based on the ideXlab platform.
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a mathematical analysis of the effects of Hebbian Learning rules on the dynamics and structure of discrete time random recurrent neural networks
Neural Computation, 2008Co-Authors: Benoit Siri, Hugues Berry, Bruno Cessac, Bruno Delord, Mathias QuoyAbstract:We present a mathematical analysis of the effects of Hebbian Learning in random recurrent neural networks, with a generic Hebbian Learning rule, including passive forgetting and different timescales, for neuronal activity and Learning dynamics. Previous numerical work has reported that Hebbian Learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
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a mathematical analysis of the effects of Hebbian Learning rules on the dynamics and structure of discrete time random recurrent neural networks
arXiv: Chaotic Dynamics, 2007Co-Authors: Benoit Siri, Hugues Berry, Bruno Cessac, Bruno Delord, Mathias QuoyAbstract:We present a mathematical analysis of the effects of Hebbian Learning in random recurrent neural networks, with a generic Hebbian Learning rule including passive forgetting and different time scales for neuronal activity and Learning dynamics. Previous numerical works have reported that Hebbian Learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on the neural network evolution. Furthermore, we show that the sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
Benoit Siri - One of the best experts on this subject based on the ideXlab platform.
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a mathematical analysis of the effects of Hebbian Learning rules on the dynamics and structure of discrete time random recurrent neural networks
Neural Computation, 2008Co-Authors: Benoit Siri, Hugues Berry, Bruno Cessac, Bruno Delord, Mathias QuoyAbstract:We present a mathematical analysis of the effects of Hebbian Learning in random recurrent neural networks, with a generic Hebbian Learning rule, including passive forgetting and different timescales, for neuronal activity and Learning dynamics. Previous numerical work has reported that Hebbian Learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
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a mathematical analysis of the effects of Hebbian Learning rules on the dynamics and structure of discrete time random recurrent neural networks
arXiv: Chaotic Dynamics, 2007Co-Authors: Benoit Siri, Hugues Berry, Bruno Cessac, Bruno Delord, Mathias QuoyAbstract:We present a mathematical analysis of the effects of Hebbian Learning in random recurrent neural networks, with a generic Hebbian Learning rule including passive forgetting and different time scales for neuronal activity and Learning dynamics. Previous numerical works have reported that Hebbian Learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on the neural network evolution. Furthermore, we show that the sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
Henrik Linden - One of the best experts on this subject based on the ideXlab platform.
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spike based bayesian Hebbian Learning of temporal sequences
PLOS Computational Biology, 2016Co-Authors: Philip Tully, Henrik Linden, Matthias H Hennig, Anders LansnerAbstract:Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) Learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the Learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative Learning in unison.