The linearity of emergent spectro-temporal receptive fields in a model of auditory cortex

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.

This source preferred by Emili Balaguer-Ballester

This data was imported from PubMed:

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.

Journal: Biosystems

Volume: 94

Issue: 1-2

Pages: 60-67

eISSN: 1872-8324

DOI: 10.1016/j.biosystems.2008.05.011

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.

This data was imported from DBLP:

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.J.

Journal: Biosystems

Volume: 94

Pages: 60-67

This data was imported from Scopus:

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.

Journal: BioSystems

Volume: 94

Issue: 1-2

Pages: 60-67

ISSN: 0303-2647

DOI: 10.1016/j.biosystems.2008.05.011

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (< 1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system. © 2008 Elsevier Ireland Ltd. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.

Journal: BIOSYSTEMS

Volume: 94

Issue: 1-2

Pages: 60-67

eISSN: 1872-8324

ISSN: 0303-2647

DOI: 10.1016/j.biosystems.2008.05.011

This data was imported from Europe PubMed Central:

Authors: Coath, M., Balaguer-Ballester, E., Denham, S.L. and Denham, M.

Journal: Bio Systems

Volume: 94

Issue: 1-2

Pages: 60-67

eISSN: 1872-8324

ISSN: 0303-2647

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.

The data on this page was last updated at 05:12 on February 21, 2020.