Model of biological pattern recognition with spatially chaotic dynamics

Yao, Yong; Freeman, Walter

doi:10.1016/0893-6080(90)90086-z

Cited by 312 publications

(119 citation statements)

References 18 publications

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“…Other parameters required to model the full set are distributed feedback delays. We have been shown empirically that the solutions of the model suffice to simulate versatile EEG patterns by proper evaluation of all the parameters (Freeman, 1987(Freeman, , 1992Yao and Freeman, 1990).…”

Section: Architecture Of a Model Of An Olfactory Systemmentioning

confidence: 99%

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Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors

1998

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TitleOptimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors. AbstractWe present a general connectionist model for an olfactory system. The dynamical behavior of each node (neural ensemble) of the model is governed by a second-order ordinary differential equation (ODE) followed by an asymmetric sigmoidal function, relating the aggregate activity of neurons to system parameters and stimuli from an outside environment. A digital implementation of the general connectionist model simulates the characteristics of a mammalian olfactory system having modifiable synaptic connections and spatio-temporal interactions among neural ensembles. Each of four distributed delay terms is represented by a second-order ODE. A parameter optimization algorithm is an integral component of the model. The parameter optimization discussed in this paper results in aperiodic oscillations having a near 1/f-type power spectrum with a peak in the gamma range, simulating the electro-encephalographic (EEG) potentials from the neural olfactory system. Random optimization is used for a rough search in a global parameter domain and the parameter self-adaptation rule serves for fine tuning within a local domain after the global search. By design the model is started from an unstable zero point by an external impulse input. It requires 650-850 ms to pass through an initializing transient before settling into a strange attractor. The attractor persists for at least 1500 ms, which is 7.5-20 times longer than the duration of the maximal stationary states observed in the EEGs and it is stable under perturbation by simulated sensory inputs giving response amplitudes less than 2 times the basal aperiodic activity. However, the optimization to 1/f-type activity reveals an inherent limit on digital simulation of chaotic states, owing to attractor crowding such that the size of basins decreases with increasing size of the model, until it approaches the size of digitizing step in computation, here a 64-bit word (ϳ10 ¹16). Outputs that are not optimized to approach 1/f-type power spectra transit earlier to limit cycle activity, usually well before 2000 ms. The duration of stationarity is increased by randomizing the terminal bit of the 64-bit words representing the state variables. The 1/f-type solutions are also exquisitely sensitive to parameter truncation; parameter values must be saved in their full binary form for re-starting. The implications in terms of numerical instability, chaos, attractor crowding and the shadowing theorem are discussed. ᭧

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Section: Architecture Of a Model Of An Olfactory Systemmentioning

confidence: 99%

“…Using the same procedure to model the other delayed feedback loops, the ''infinite dimensional'' differential-delay equations (Yao and Freeman, 1990) convert into finite dimensional ODEs. The dynamical model expressed in Section 2.1 and Appendix A is thus constructed.…”

Section: Odes For the Periglomerular Cell Ensemblementioning

confidence: 99%

Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors

1998

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“…recordings. 2 When an input is presented, the KIII jumps into a limit cycle attractor that is specific to the input stimulus. Once the stimulus is removed, the KIII returns to its basal state.…”

Section: The Electronic Nose Technologymentioning

confidence: 99%

“…[2][3][4][5] With a few exceptions, the KIII model has gone largely unnoticed in the machine olfaction literature. Otto has applied the KIII to process data from FTIR and chemical sensors [6][7][8] using habituation and hebbian learning.…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement and background suppression of chemosensor array patterns with the KIII model

Gutiérrez-Gálvez

Gutierrez‐Osuna

2006

Int. J. Intell. Syst.

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Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.

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“…Grossberg [3] has proposed that cortical connections to the bulb may selectively filter the bulb input and cause resonance between the two regions. Yao and Freeman [4] have implicated these feedback connections with chaotic dynamics in the bulb.…”

Section: Introductionmentioning

confidence: 99%

Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction

Raman

Gutierrez‐Osuna

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

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We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or antiHebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array.

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Model of biological pattern recognition with spatially chaotic dynamics

Cited by 312 publications

References 18 publications

Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors

Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors

Contrast enhancement and background suppression of chemosensor array patterns with the KIII model

Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction

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