A neuromorphic approach to auditory pattern recognition in cricket phonotaxis

Rost, Thomas; Ramachandran, Harshawardhan; Nawrot, Martin Paul; Chicca, Elisabetta

doi:10.1109/ecctd.2013.6662247

Cited by 11 publications

(9 citation statements)

References 23 publications

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“…The 2DIFTWA was later used to explore models of auditory perception in crickets (Rost et al, 2013) and to study latency code processing in weakly electric fish (Engelmann et al, 2016).…”

Section: The 2difwta Chip: a 2d Array Of Integrate-and-fire Neurons Fmentioning

confidence: 99%

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

et al. 2018

Self Cite

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Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.

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“…The 2DIFTWA was later used to explore models of auditory perception in crickets (Rost et al, 2013) and to study latency code processing in weakly electric fish (Engelmann et al, 2016).…”

Section: The 2difwta Chip: a 2d Array Of Integrate-and-fire Neurons Fmentioning

confidence: 99%

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

et al. 2018

Self Cite

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“…Secondly, wireless communication and high energy consumption prevent application in a truly autonomous system. On a longer perspective, matured neuromorphic hardware technology [20], [21], [22], [23], [24] will offer a compact and energy-efficient approach for autonomous robot control by artificial minibrains. …”

Section: Discussionmentioning

confidence: 99%

Conditioned behavior in a robot controlled by a spiking neural network

Helgadottir

Haenicke

Landgraf

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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Insects show a rich repertoire of goal-directed and adaptive behaviors that are still beyond the capabilities of today's artificial systems. Fast progress in our comprehension of the underlying neural computations make the insect a favorable model system for neurally inspired computing paradigms in autonomous robots. Here, we present a robotic platform designed for implementing and testing spiking neural network control architectures. We demonstrate a neuromorphic realtime approach to sensory processing, reward-based associative plasticity and behavioral control. This is inspired by the biological mechanisms underlying rapid associative learning and the formation of distributed memories in the insect.

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“…One cellular mechanism that could serve this task is short-term synaptic plasticity. Fascilitation and depression at synapses are governed by processes with typical time constants in the right order of magnitude and they have repeatedly been suggested to be involved in decision making processes (Mongillo et al, 2008 ; Martínez-García et al, 2011 ) including a suggested algorithm for auditory pattern recognition in the cricket's central brain (Rost et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system

Meckenhäuser

Krämer

Farkhooi

et al. 2014

Front. Syst. Neurosci.

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Acoustic communication plays a key role for mate attraction in grasshoppers. Males use songs to advertise themselves to females. Females evaluate the song pattern, a repetitive structure of sound syllables separated by short pauses, to recognize a conspecific male and as proxy to its fitness. In their natural habitat females often receive songs with degraded temporal structure. Perturbations may, for example, result from the overlap with other songs. We studied the response behavior of females to songs that show different signal degradations. A perturbation of an otherwise attractive song at later positions in the syllable diminished the behavioral response, whereas the same perturbation at the onset of a syllable did not affect song attractiveness. We applied naïve Bayes classifiers to the spike trains of identified neurons in the auditory pathway to explore how sensory evidence about the acoustic stimulus and its attractiveness is represented in the neuronal responses. We find that populations of three or more neurons were sufficient to reliably decode the acoustic stimulus and to predict its behavioral relevance from the single-trial integrated firing rate. A simple model of decision making simulates the female response behavior. It computes for each syllable the likelihood for the presence of an attractive song pattern as evidenced by the population firing rate. Integration across syllables allows the likelihood to reach a decision threshold and to elicit the behavioral response. The close match between model performance and animal behavior shows that a spike rate code is sufficient to enable song pattern recognition.

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A neuromorphic approach to auditory pattern recognition in cricket phonotaxis

Cited by 11 publications

References 23 publications

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Conditioned behavior in a robot controlled by a spiking neural network

Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system

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