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Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Bottom Line: The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity.Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors.This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Sorbonne Paris Cité, Paris, France. romain.brette@ens.fr

ABSTRACT
Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

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Structure and synchrony.A, Binaural hearing (simplified). The sound arrives at the two ears after a propagation delay dL and dR. Monaural neurons A and B project to a binaural neuron with axonal conduction delays δL and δR. Synchrony (seen on the postsynaptic side) occurs when dR−dL = δL−δR, corresponding to a specific interaural time difference. B, Pitch. Two monaural neurons responding to a sound project to a postsynaptic neuron with axonal delays δA and δB. From the postsynaptic point of view, synchrony occurs for a periodic sound with period 1/f0 matching the delay difference: 1/f0 = δB−δA. C, Olfaction. Left, Odor concentration fluctuates rapidly because of turbulences, and odorant molecules bind to different types of receptors. Each receptor has an odor-specific affinity, so that its coverage by the odor is the product of concentration and affinity. Right, Olfactory neurons A and B have the same receptor type but different global sensitivities, neuron C has a different receptor type. Colored curves schematically represent the sensitivity to different odors, defined as the product of odor affinity and global sensitivity. Synchrony occurs at intersection points, for specific odors. D, More generally, a structured stimulus is described as the image of a lower-dimensional stimulus X through some transformation T. Synchrony occurs in two different neurons when their receptive fields match when combined with the transformation T.
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pcbi-1002561-g007: Structure and synchrony.A, Binaural hearing (simplified). The sound arrives at the two ears after a propagation delay dL and dR. Monaural neurons A and B project to a binaural neuron with axonal conduction delays δL and δR. Synchrony (seen on the postsynaptic side) occurs when dR−dL = δL−δR, corresponding to a specific interaural time difference. B, Pitch. Two monaural neurons responding to a sound project to a postsynaptic neuron with axonal delays δA and δB. From the postsynaptic point of view, synchrony occurs for a periodic sound with period 1/f0 matching the delay difference: 1/f0 = δB−δA. C, Olfaction. Left, Odor concentration fluctuates rapidly because of turbulences, and odorant molecules bind to different types of receptors. Each receptor has an odor-specific affinity, so that its coverage by the odor is the product of concentration and affinity. Right, Olfactory neurons A and B have the same receptor type but different global sensitivities, neuron C has a different receptor type. Colored curves schematically represent the sensitivity to different odors, defined as the product of odor affinity and global sensitivity. Synchrony occurs at intersection points, for specific odors. D, More generally, a structured stimulus is described as the image of a lower-dimensional stimulus X through some transformation T. Synchrony occurs in two different neurons when their receptive fields match when combined with the transformation T.

Mentions: In this framework, a random stimulus cannot produce tightly synchronous responses in neurons with different receptive fields. Therefore, synchrony must reflect some non-randomness or “structure” in the stimulus. Fig. 7 illustrates the relationship between synchrony and structure with a few sensory examples.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Structure and synchrony.A, Binaural hearing (simplified). The sound arrives at the two ears after a propagation delay dL and dR. Monaural neurons A and B project to a binaural neuron with axonal conduction delays δL and δR. Synchrony (seen on the postsynaptic side) occurs when dR−dL = δL−δR, corresponding to a specific interaural time difference. B, Pitch. Two monaural neurons responding to a sound project to a postsynaptic neuron with axonal delays δA and δB. From the postsynaptic point of view, synchrony occurs for a periodic sound with period 1/f0 matching the delay difference: 1/f0 = δB−δA. C, Olfaction. Left, Odor concentration fluctuates rapidly because of turbulences, and odorant molecules bind to different types of receptors. Each receptor has an odor-specific affinity, so that its coverage by the odor is the product of concentration and affinity. Right, Olfactory neurons A and B have the same receptor type but different global sensitivities, neuron C has a different receptor type. Colored curves schematically represent the sensitivity to different odors, defined as the product of odor affinity and global sensitivity. Synchrony occurs at intersection points, for specific odors. D, More generally, a structured stimulus is described as the image of a lower-dimensional stimulus X through some transformation T. Synchrony occurs in two different neurons when their receptive fields match when combined with the transformation T.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g007: Structure and synchrony.A, Binaural hearing (simplified). The sound arrives at the two ears after a propagation delay dL and dR. Monaural neurons A and B project to a binaural neuron with axonal conduction delays δL and δR. Synchrony (seen on the postsynaptic side) occurs when dR−dL = δL−δR, corresponding to a specific interaural time difference. B, Pitch. Two monaural neurons responding to a sound project to a postsynaptic neuron with axonal delays δA and δB. From the postsynaptic point of view, synchrony occurs for a periodic sound with period 1/f0 matching the delay difference: 1/f0 = δB−δA. C, Olfaction. Left, Odor concentration fluctuates rapidly because of turbulences, and odorant molecules bind to different types of receptors. Each receptor has an odor-specific affinity, so that its coverage by the odor is the product of concentration and affinity. Right, Olfactory neurons A and B have the same receptor type but different global sensitivities, neuron C has a different receptor type. Colored curves schematically represent the sensitivity to different odors, defined as the product of odor affinity and global sensitivity. Synchrony occurs at intersection points, for specific odors. D, More generally, a structured stimulus is described as the image of a lower-dimensional stimulus X through some transformation T. Synchrony occurs in two different neurons when their receptive fields match when combined with the transformation T.
Mentions: In this framework, a random stimulus cannot produce tightly synchronous responses in neurons with different receptive fields. Therefore, synchrony must reflect some non-randomness or “structure” in the stimulus. Fig. 7 illustrates the relationship between synchrony and structure with a few sensory examples.

Bottom Line: The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity.Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors.This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Sorbonne Paris Cité, Paris, France. romain.brette@ens.fr

ABSTRACT
Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

Show MeSH
Related in: MedlinePlus