Title
On population encoding and decoding of auditory information for bat echolocation On population encoding and decoding of auditory information for bat echolocation
Author
Faculty/Department
Faculty of Applied Economics
Publication type
article
Publication
Berlin ,
Subject
Biology
Source (journal)
Biological cybernetics. - Berlin
Volume/pages
102(2010) :4 , p. 311-326
ISSN
0340-1200
ISI
000276344100003
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
In this article, we study the neural encoding of acoustic information for FM-bats (such as Eptesicus fuscus) in simulation. In echolocation research, the frequencytime sound representation as expressed by the spectrogram is often considered as input. The rationale behind this is that a similar representation is present in the cochlea, i.e. the receptor potential of the inner hair cells (IHC) along the length of the cochlea, and hence similar acoustic information is relayed to the brain. In this article, we study to what extent the latter assumption is true. The receptor potential is converted into neural activity of the synapting auditory nerve cells (ANC), and information might be lost in this conversion process. Especially for FM-bats, this information transmission is not trivial: in contrast to other mammals, they detect short transient signals, and consequently neural activity can only be integrated over very limited time intervals. To quantify the amount of information transmitted we design a neural network-based algorithm to reconstruct the IHC receptor potentials from the spiking activity of the synapting auditory neurons. Both the receptor potential and the resulting neural activity are simulated using Meddis peripheral model. Comparing the reconstruction to the IHC receptor potential, we quantify the information transmission of the bat hearing system and investigate how this depends on the intensity of the incoming signal, the distribution of auditory neurons, and previous masking stimulation (adaptation). In addition, we show how this approach allows to inspect which spectral features survive neural encoding and hence can be relevant for echolocation.
E-info
https://repository.uantwerpen.be/docman/iruaauth/e53fd8/d85f9d88614.pdf
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