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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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Recognition of odor mixtures and robustness.A, Average firing rate of the postsynaptic assembly tuned to an equal mixture of odors A and B, as a function of the proportion of A in the presented mixture. Each curve corresponds to a different concentration (1, 10, 100). B, Binding: tuning curve of the postsynaptic assembly (same as in A for concentration 10) for mixtures presented in a single turbulent plume (solid) or in two independent plumes for the two odors (dashed). C, Same as in Fig. 6, but the membrane time constant of receptors is heterogeneous (between 15 and 25 ms). With the same synaptic projections as in Fig. 6 (initial wiring), the postsynaptic rate is reduced, but not odor specificity. The firing rate increases when the synaptic projections are adapted to this heterogeneity, i.e., presynaptic neurons have similar membrane time constants (new wiring).
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pcbi-1002561-g010: Recognition of odor mixtures and robustness.A, Average firing rate of the postsynaptic assembly tuned to an equal mixture of odors A and B, as a function of the proportion of A in the presented mixture. Each curve corresponds to a different concentration (1, 10, 100). B, Binding: tuning curve of the postsynaptic assembly (same as in A for concentration 10) for mixtures presented in a single turbulent plume (solid) or in two independent plumes for the two odors (dashed). C, Same as in Fig. 6, but the membrane time constant of receptors is heterogeneous (between 15 and 25 ms). With the same synaptic projections as in Fig. 6 (initial wiring), the postsynaptic rate is reduced, but not odor specificity. The firing rate increases when the synaptic projections are adapted to this heterogeneity, i.e., presynaptic neurons have similar membrane time constants (new wiring).

Mentions: In Fig. 10A, I consider a mixture of two odors A and B and a postsynaptic assembly tuned to the equal mixture (50% A, 50% B). The average firing rate varies with the concentrations of both odors in the mixture and in contrast with Fig. 9B, the presented mixture is always highly correlated with the target mixture. Fig. 10A shows that the assembly responds best when there is an equal proportion of A and B in the mixture, at all concentrations (varying by a factor 100). Although selectivity is broader at the highest concentration, the assembly still responds more to the target mixture at the lowest concentration than to either odor A or B at the highest concentration (×100). Odors A and B are bound into a single mixture because their fluctuations are coherent. If the same odors are simultaneously presented but as a mixture of two independent plumes with their own fluctuations (two different turbulent flows representing two different odor sources), then the network does not bind them together and the assembly does not respond (Fig. 10B). Thus the model implements the idea of binding by synchrony, where precise spike timing acts as a “signature” of an object [24]. More precisely, since neural responses follow the temporal structure of the stimulus, precise coincidences can only detected between neurons that respond to the same stimulus. This is a weak version of binding by synchrony, in the sense that the temporal “signature” is intrinsic to the stimulus rather than created as a result of object formation.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Recognition of odor mixtures and robustness.A, Average firing rate of the postsynaptic assembly tuned to an equal mixture of odors A and B, as a function of the proportion of A in the presented mixture. Each curve corresponds to a different concentration (1, 10, 100). B, Binding: tuning curve of the postsynaptic assembly (same as in A for concentration 10) for mixtures presented in a single turbulent plume (solid) or in two independent plumes for the two odors (dashed). C, Same as in Fig. 6, but the membrane time constant of receptors is heterogeneous (between 15 and 25 ms). With the same synaptic projections as in Fig. 6 (initial wiring), the postsynaptic rate is reduced, but not odor specificity. The firing rate increases when the synaptic projections are adapted to this heterogeneity, i.e., presynaptic neurons have similar membrane time constants (new wiring).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g010: Recognition of odor mixtures and robustness.A, Average firing rate of the postsynaptic assembly tuned to an equal mixture of odors A and B, as a function of the proportion of A in the presented mixture. Each curve corresponds to a different concentration (1, 10, 100). B, Binding: tuning curve of the postsynaptic assembly (same as in A for concentration 10) for mixtures presented in a single turbulent plume (solid) or in two independent plumes for the two odors (dashed). C, Same as in Fig. 6, but the membrane time constant of receptors is heterogeneous (between 15 and 25 ms). With the same synaptic projections as in Fig. 6 (initial wiring), the postsynaptic rate is reduced, but not odor specificity. The firing rate increases when the synaptic projections are adapted to this heterogeneity, i.e., presynaptic neurons have similar membrane time constants (new wiring).
Mentions: In Fig. 10A, I consider a mixture of two odors A and B and a postsynaptic assembly tuned to the equal mixture (50% A, 50% B). The average firing rate varies with the concentrations of both odors in the mixture and in contrast with Fig. 9B, the presented mixture is always highly correlated with the target mixture. Fig. 10A shows that the assembly responds best when there is an equal proportion of A and B in the mixture, at all concentrations (varying by a factor 100). Although selectivity is broader at the highest concentration, the assembly still responds more to the target mixture at the lowest concentration than to either odor A or B at the highest concentration (×100). Odors A and B are bound into a single mixture because their fluctuations are coherent. If the same odors are simultaneously presented but as a mixture of two independent plumes with their own fluctuations (two different turbulent flows representing two different odor sources), then the network does not bind them together and the assembly does not respond (Fig. 10B). Thus the model implements the idea of binding by synchrony, where precise spike timing acts as a “signature” of an object [24]. More precisely, since neural responses follow the temporal structure of the stimulus, precise coincidences can only detected between neurons that respond to the same stimulus. This is a weak version of binding by synchrony, in the sense that the temporal “signature” is intrinsic to the stimulus rather than created as a result of object formation.

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