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Memory Stacking in Hierarchical Networks

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Memory Stacking in Hierarchical Networks. / Westö, Johan; May, Patrick; Tiitinen, Hannu.
In: Neural Computation, Vol. 28, No. 2, 02.2016, p. 327-353.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Westö, J, May, P & Tiitinen, H 2016, 'Memory Stacking in Hierarchical Networks', Neural Computation, vol. 28, no. 2, pp. 327-353. https://doi.org/10.1162/NECO_a_00803

APA

Westö, J., May, P., & Tiitinen, H. (2016). Memory Stacking in Hierarchical Networks. Neural Computation, 28(2), 327-353. https://doi.org/10.1162/NECO_a_00803

Vancouver

Westö J, May P, Tiitinen H. Memory Stacking in Hierarchical Networks. Neural Computation. 2016 Feb;28(2):327-353. doi: 10.1162/NECO_a_00803

Author

Westö, Johan ; May, Patrick ; Tiitinen, Hannu. / Memory Stacking in Hierarchical Networks. In: Neural Computation. 2016 ; Vol. 28, No. 2. pp. 327-353.

Bibtex

@article{cde1248fd02a4c7eb2ce264b3e819c4f,
title = "Memory Stacking in Hierarchical Networks",
abstract = "Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.",
author = "Johan West{\"o} and Patrick May and Hannu Tiitinen",
year = "2016",
month = feb,
doi = "10.1162/NECO_a_00803",
language = "English",
volume = "28",
pages = "327--353",
journal = "Neural Computation",
issn = "0899-7667",
publisher = "MIT Press Journals",
number = "2",

}

RIS

TY - JOUR

T1 - Memory Stacking in Hierarchical Networks

AU - Westö, Johan

AU - May, Patrick

AU - Tiitinen, Hannu

PY - 2016/2

Y1 - 2016/2

N2 - Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

AB - Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

U2 - 10.1162/NECO_a_00803

DO - 10.1162/NECO_a_00803

M3 - Journal article

VL - 28

SP - 327

EP - 353

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 2

ER -