A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation
A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation
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DOI：
10.1016/j.neucom.2009.10.030
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发表时间：
2010-12-01
期刊：
影响因子：
6
通讯作者：
Wermter, Stefan
中科院分区：
文献类型：
--
作者：
Liu, Jindong;Perez-Gonzalez, David;Wermter, Stefan
本文提出了一种基于哺乳动物皮层下听觉通路的脉冲神经网络（spiking neural network，SNN）来实现双耳声源定位。该网络的设计灵感来自于对双耳处理在内侧上级橄榄核（MSO）、外侧上级橄榄核（LSO）和下丘（IC）的组织的神经生理学研究。为了在宽频率范围内实现声源的尖锐方位角定位，构造了三组人工神经元来表示MSO、LSO和IC中对耳间时间差（ITD）、耳间水平差（ILD）和方位角（theta）。每组中的神经元按音调位置排列，以考虑听觉通路的频率组织。为了反映生物组织，仅使用MSO提取的ITD信息来定位低频（< 1 kHz）声音对于1和4 kHz之间的声音频率，模型还使用LSO提取的ILD信息。该信息在IC模型中组合，其中我们假设来自MSO和LSO的输入强度与P的条件概率成比例。（θ垂直条ITD）或P（θ垂直条ILD）实验结果表明，ILD信息的加入显着增加了声音定位性能在频率高于1 kHz我们的模型可以用来测试不同的范式在哺乳动物大脑中的声音定位，并展示了一个潜在的实用机器人声音定位的应用（C）2010 Elsevier B V版权所有
This paper proposes a spiking neural network (SNN) of the mammalian subcortical auditory pathway to achieve binaural sound source localisation The network is inspired by neurophysiological studies on the organisation of binaural processing in the medial superior olive (MSO) lateral superior olive (LSO) and the inferior colliculus (IC) to achieve a sharp azimuthal localisation of a sound source over a wide frequency range Three groups of artificial neurons are constructed to represent the neurons in the MSO LSO and IC that are sensitive to interaural time difference (ITD) interaural level difference (ILD) and azimuth angle (theta) respectively The neurons in each group are tonotopically arranged to take into account the frequency organisation of the auditory pathway To reflect the biological organisation only ITD information extracted by the MSO is used for localisation of low frequency ( < 1 kHz) sounds for sound frequencies between 1 and 4 kHz the model also uses ILD information extracted by the LSO This information is combined in the IC model where we assume that the strengths of the inputs from the MSO and LSO are proportional to the conditional probability of P(theta vertical bar ITD) or P(theta vertical bar ILD) calculated based on the Bayes theorem The experimental results show that the addition of ILD information significantly increases sound localisation performance at frequencies above 1 kHz Our model can be used to test different paradigms for sound localisation in the mammalian brain and demonstrates a potential practical application of sound localisation for robots (C) 2010 Elsevier B V All rights reserved