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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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Synchrony receptive fields in the auditory and visual modalities.A, Binaural hearing with realistic sound diffraction. The sound S arrives at the two ears as a binaural signal (FR*S, FL*S), where FR and FL are location-dependent filters, and is subsequently processed by two monaural neurons with receptive fields NA and NB. The synchrony receptive field is the set of source locations such that NA * FR = NB * FL. B, Pitch. Two monaural neurons with different preferred frequencies fire in synchrony for a pure tone or resolved partial with frequency 1/f0, at the intersection of the two amplitude spectra (provided that the phase difference is compensated by appropriate delays). C, Binocular disparity. Two retinal ganglion cells fire in synchrony when there is an object at the convergence point of their fixation lines. D, Edges and textures. Two visual neurons with circular receptive fields fire in synchrony to images that are invariant to translations of the vector linking the two receptive field centers: edges with the same orientation and spatially periodic textures with the period given by that vector.
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pcbi-1002561-g012: Synchrony receptive fields in the auditory and visual modalities.A, Binaural hearing with realistic sound diffraction. The sound S arrives at the two ears as a binaural signal (FR*S, FL*S), where FR and FL are location-dependent filters, and is subsequently processed by two monaural neurons with receptive fields NA and NB. The synchrony receptive field is the set of source locations such that NA * FR = NB * FL. B, Pitch. Two monaural neurons with different preferred frequencies fire in synchrony for a pure tone or resolved partial with frequency 1/f0, at the intersection of the two amplitude spectra (provided that the phase difference is compensated by appropriate delays). C, Binocular disparity. Two retinal ganglion cells fire in synchrony when there is an object at the convergence point of their fixation lines. D, Edges and textures. Two visual neurons with circular receptive fields fire in synchrony to images that are invariant to translations of the vector linking the two receptive field centers: edges with the same orientation and spatially periodic textures with the period given by that vector.

Mentions: Finally, I will show how the concepts I have exposed apply to a few auditory and visual examples (Fig. 12). In Fig. 7A, I illustrated the notion of structured stimulus in a simplified description of binaural hearing, where the sound arrives at the two ears with an interaural delay that depends on the source direction. In reality, bina ural cues are more complex because the sound is diffracted by the head and pinnae, and even the body (Fig. 12A). The correct physical description is that the two monaural signals are two linearly filtered versions of the original signal S: SL = FL*S, SR = FR*S (* is the convolution). These location-specific filters are called head-related impulse responses and are more complex than pure delays (in particular, ITD is frequency-dependent [67]). I consider two monaural neurons A and B on opposite sides with different receptive fields NA and NB. These neural filters represent basilar membrane filtering around a characteristic frequency (CF), and include an outgoing axonal delay. Thus, they may differ both in CF and in axonal delay. In the framework I have described, these two neurons have synchronous responses when NA*FL*S = NB*FR*S, that is, their SRF includes all acoustical filter pairs (FL, FR) such that NA*FL = NB*FR, meaning that the combination of neural and acoustical filtering match on both sides. Therefore this is a spatial field, and synchrony signals source location independently of source signal. A spiking neural model based on these properties can accurately estimate the location of previously unheard sounds in a realistic virtual acoustic environment [68]. This corresponds to the idea that the tuning properties of binaural neurons may come not only from mismatches in axonal delay but also in the preferred frequency of their monaural inputs [69]–[71]. A prediction from this theory is that the preferred ITD of a binaural neuron can depend on sound frequency, because ITDs depend on frequency when diffraction is taken into account [67]. This property has indeed been observed in binaural neurons of many species [72], [73]. More specifically, the theory predicts that the frequency-dependence of preferred ITDs should match the corresponding quantities in the acoustical filters, which can be measured.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Synchrony receptive fields in the auditory and visual modalities.A, Binaural hearing with realistic sound diffraction. The sound S arrives at the two ears as a binaural signal (FR*S, FL*S), where FR and FL are location-dependent filters, and is subsequently processed by two monaural neurons with receptive fields NA and NB. The synchrony receptive field is the set of source locations such that NA * FR = NB * FL. B, Pitch. Two monaural neurons with different preferred frequencies fire in synchrony for a pure tone or resolved partial with frequency 1/f0, at the intersection of the two amplitude spectra (provided that the phase difference is compensated by appropriate delays). C, Binocular disparity. Two retinal ganglion cells fire in synchrony when there is an object at the convergence point of their fixation lines. D, Edges and textures. Two visual neurons with circular receptive fields fire in synchrony to images that are invariant to translations of the vector linking the two receptive field centers: edges with the same orientation and spatially periodic textures with the period given by that vector.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g012: Synchrony receptive fields in the auditory and visual modalities.A, Binaural hearing with realistic sound diffraction. The sound S arrives at the two ears as a binaural signal (FR*S, FL*S), where FR and FL are location-dependent filters, and is subsequently processed by two monaural neurons with receptive fields NA and NB. The synchrony receptive field is the set of source locations such that NA * FR = NB * FL. B, Pitch. Two monaural neurons with different preferred frequencies fire in synchrony for a pure tone or resolved partial with frequency 1/f0, at the intersection of the two amplitude spectra (provided that the phase difference is compensated by appropriate delays). C, Binocular disparity. Two retinal ganglion cells fire in synchrony when there is an object at the convergence point of their fixation lines. D, Edges and textures. Two visual neurons with circular receptive fields fire in synchrony to images that are invariant to translations of the vector linking the two receptive field centers: edges with the same orientation and spatially periodic textures with the period given by that vector.
Mentions: Finally, I will show how the concepts I have exposed apply to a few auditory and visual examples (Fig. 12). In Fig. 7A, I illustrated the notion of structured stimulus in a simplified description of binaural hearing, where the sound arrives at the two ears with an interaural delay that depends on the source direction. In reality, bina ural cues are more complex because the sound is diffracted by the head and pinnae, and even the body (Fig. 12A). The correct physical description is that the two monaural signals are two linearly filtered versions of the original signal S: SL = FL*S, SR = FR*S (* is the convolution). These location-specific filters are called head-related impulse responses and are more complex than pure delays (in particular, ITD is frequency-dependent [67]). I consider two monaural neurons A and B on opposite sides with different receptive fields NA and NB. These neural filters represent basilar membrane filtering around a characteristic frequency (CF), and include an outgoing axonal delay. Thus, they may differ both in CF and in axonal delay. In the framework I have described, these two neurons have synchronous responses when NA*FL*S = NB*FR*S, that is, their SRF includes all acoustical filter pairs (FL, FR) such that NA*FL = NB*FR, meaning that the combination of neural and acoustical filtering match on both sides. Therefore this is a spatial field, and synchrony signals source location independently of source signal. A spiking neural model based on these properties can accurately estimate the location of previously unheard sounds in a realistic virtual acoustic environment [68]. This corresponds to the idea that the tuning properties of binaural neurons may come not only from mismatches in axonal delay but also in the preferred frequency of their monaural inputs [69]–[71]. A prediction from this theory is that the preferred ITD of a binaural neuron can depend on sound frequency, because ITDs depend on frequency when diffraction is taken into account [67]. This property has indeed been observed in binaural neurons of many species [72], [73]. More specifically, the theory predicts that the frequency-dependence of preferred ITDs should match the corresponding quantities in the acoustical filters, which can be measured.

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