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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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Learning to detect odors.A, Two odors are randomly presented to the network for 40 s. This histogram represents the distribution of tuning ratios after this learning period. The tuning ratio of a postsynaptic neuron is the proportion of spikes triggered by the first odor. B, Responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). C, Voltage traces for a postsynaptic neuron tuned to odor B, when odor A (left) and B (right) are presented.
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pcbi-1002561-g011: Learning to detect odors.A, Two odors are randomly presented to the network for 40 s. This histogram represents the distribution of tuning ratios after this learning period. The tuning ratio of a postsynaptic neuron is the proportion of spikes triggered by the first odor. B, Responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). C, Voltage traces for a postsynaptic neuron tuned to odor B, when odor A (left) and B (right) are presented.

Mentions: Finally, the specific wiring I have described can be learned by synaptic plasticity mechanisms, as explained for the duration model (Fig. 4). In Fig. 11, the two odors A and B were randomly presented to the olfactory model, with random synapses between receptors and postsynaptic neurons (50 synapses per postsynaptic neuron). The presented odor is updated every 200 ms, for a total duration of 40 s. The synaptic weights evolve according to the same homeostatic and synaptic plasticity mechanisms as for the duration model (Fig. 4). At the end of the stimulation, a tuning ratio is calculated for each neuron, as the proportion of spikes in response to odor A, over the second half of the stimulation. That is, a tuning ratio of 0 means that the neuron only responds to odor A, while a tuning ratio of 1 means that it responds only to odor B. Fig. 11A shows the distribution of tuning ratios of the postsynaptic neurons. All neurons but one have tuning ratios clustered near 0 or 1, that is, they are tuned to a single odor. The neurons are then ordered by tuning ratio, and they are presented with odor A with an increasing concentration, then with odor B (Fig. 11B). The concentration varies between 0.1 and 10 (bottom), where 1 is the concentration during the learning phase. It appears that odor selectivity is preserved at all tested concentrations. Fig. 11C shows the voltage traces of a neuron tuned to odor B, when odor A (left) and B (right) are presented (spikes are added for readability). The membrane potential has standard deviation 0.17 (odor A) and 0.18 (odor B, calculated without the spikes), and mean 0.08 (A) and 0.07 (B). Thus, the membrane potential distributions are similar for the preferred and non-preferred odors: the increased firing is due to transient synchrony events rather than changes in input statistics.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Learning to detect odors.A, Two odors are randomly presented to the network for 40 s. This histogram represents the distribution of tuning ratios after this learning period. The tuning ratio of a postsynaptic neuron is the proportion of spikes triggered by the first odor. B, Responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). C, Voltage traces for a postsynaptic neuron tuned to odor B, when odor A (left) and B (right) are presented.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g011: Learning to detect odors.A, Two odors are randomly presented to the network for 40 s. This histogram represents the distribution of tuning ratios after this learning period. The tuning ratio of a postsynaptic neuron is the proportion of spikes triggered by the first odor. B, Responses of postsynaptic neurons, ordered by tuning ratio, to odor A (blue) and odor B (red), with an increasing concentration (0.1 to 10, where 1 is odor concentration in the learning phase). C, Voltage traces for a postsynaptic neuron tuned to odor B, when odor A (left) and B (right) are presented.
Mentions: Finally, the specific wiring I have described can be learned by synaptic plasticity mechanisms, as explained for the duration model (Fig. 4). In Fig. 11, the two odors A and B were randomly presented to the olfactory model, with random synapses between receptors and postsynaptic neurons (50 synapses per postsynaptic neuron). The presented odor is updated every 200 ms, for a total duration of 40 s. The synaptic weights evolve according to the same homeostatic and synaptic plasticity mechanisms as for the duration model (Fig. 4). At the end of the stimulation, a tuning ratio is calculated for each neuron, as the proportion of spikes in response to odor A, over the second half of the stimulation. That is, a tuning ratio of 0 means that the neuron only responds to odor A, while a tuning ratio of 1 means that it responds only to odor B. Fig. 11A shows the distribution of tuning ratios of the postsynaptic neurons. All neurons but one have tuning ratios clustered near 0 or 1, that is, they are tuned to a single odor. The neurons are then ordered by tuning ratio, and they are presented with odor A with an increasing concentration, then with odor B (Fig. 11B). The concentration varies between 0.1 and 10 (bottom), where 1 is the concentration during the learning phase. It appears that odor selectivity is preserved at all tested concentrations. Fig. 11C shows the voltage traces of a neuron tuned to odor B, when odor A (left) and B (right) are presented (spikes are added for readability). The membrane potential has standard deviation 0.17 (odor A) and 0.18 (odor B, calculated without the spikes), and mean 0.08 (A) and 0.07 (B). Thus, the membrane potential distributions are similar for the preferred and non-preferred odors: the increased firing is due to transient synchrony events rather than changes in input statistics.

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