Group Title: BMC Neuroscience
Title: Evaluating feedforward spiking neuron networks using a novel decoding strategy
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 Material Information
Title: Evaluating feedforward spiking neuron networks using a novel decoding strategy
Physical Description: Book
Language: English
Creator: VanderKraats, Nathan
Banerjee, Arunava
Publisher: BMC Neuroscience
Publication Date: 2008
General Note: Start page P113
General Note: M3: 10.1186/1471-2202-9-S1-P113
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Bibliographic ID: UF00099980
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: Open Access:
Resource Identifier: issn - 1471-2202


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BMC Meuroscience

BioMed Central

Poster presentation

Evaluating feedforward spiking neuron networks using a novel
decoding strategy
Nathan D VanderKraats* and Arunava Banerjee

Address: Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
Email: Nathan D VanderKraats*
* Corresponding author

from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008
Portland, OR, USA. 19-24 July 2008

Published: II July 2008
8MC Neuroscience 2008, 9(Suppl I):P 113 doi: 10.1 186/1471-2202-9-SI-PI 13

This abstract is available from: I/PI 13
2008 VanderKraats and Banerjee; licensee BioMed Central Ltd.

Investigating how information is represented within a
population of model neurons is a primary focus of com-
putational neuroscience research. In feed-forward sys-
tems, a fundamentally related question is how this
representation changes as it advances through the net-
work. In this letter, we explore the capabilities of several
kinds of feed-forward network architectures at transmit-
ting complexly coded information using a large, heteroge-
neous populations of model neurons. For a suitably
elaborate input, we employ a realistic model of the audi-
tory periphery, the Meddis Inner-Hair Cell Model [1]. To
interpret the spike train responses for sizeable neuronal
populations, we introduce a novel method for decoding
based on a discrete version of the reconstruction method
[2]. By combining an interspike interval (ISI) representa-
tion with support vector machine (SVM) classifiers, we

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so I
so --- --- ----- l -- l ---- l- ---

1000 1500 2000 2500 3000 3500 4000 4500 5000
Frequency Slit Point (Hz)
Figure I
Accuracy for various frequency split points.

successful decode information from layers of 200 spiral
ganglion cells of 20 different types. Furthermore, this
method makes no assumptions about the spike train's

We judge the performance of several candidate networks
using a two-tiered system. For our discrete task, we ask
whether each stimulus, a pure tone blip, is higher or lower
than some predetermined split point frequency. To obtain
a baseline, we measure the classification accuracy for this
task on our simulated auditory nerve. Next, we use this
spike signal as an input to our candidate architectures and
record the output spike trains. We can now evaluate our
architectures' performances by decoding the output with
respect to the initial sound stimulus. A graph of our results
for various frequencies is shown in Figure 1.

I. Sumner CJ, Lopez-Poveda EA, O'Mard LP, Meddis R: A revised
model of the inner-hair cell and auditory nerve complex. J
Acoust Soc Am 2002, I I 1:2178-2188.
2. Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W: Spikes:
Exploring the neural code Cambridge: MIT Press; 1997.

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