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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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Decoding synchrony patterns in a heterogeneous population.Each column corresponds to one stimulus duration. A, Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the “synchrony partition”. Circled neurons belong to the synchronous group of neuron A. B, Each synchronous group projects to a postsynaptic neuron. Each duration is associated with an assembly of postsynaptic neurons. C, Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average).
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pcbi-1002561-g002: Decoding synchrony patterns in a heterogeneous population.Each column corresponds to one stimulus duration. A, Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the “synchrony partition”. Circled neurons belong to the synchronous group of neuron A. B, Each synchronous group projects to a postsynaptic neuron. Each duration is associated with an assembly of postsynaptic neurons. C, Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average).

Mentions: We can now apply these principles to decode synchrony patterns at the population level. Consider a population of neurons with rebound spiking properties but heterogeneous parameters. For example, in Fig. 2A, the membrane time constant varies randomly across neurons between 10 and 50 ms, and the K+ channel time constant varies between 300 and 500 ms (see Text S1 for a justification of this choice of parameter values). For a given stimulus, for example an inhibitory pulse with duration 300 ms, we can look at the synchrony pattern in the neural population. In Fig. 1E (top left), neurons represented with the same color produce a rebound spike at the same time (with a 2 ms precision), that is, the stimulus is in the SRF of neurons with the same color. Thus, the neuron population can be divided in groups of synchronous neurons (possibly containing just one neuron). I call this partition of the neural population the synchrony partition (mathematically, it is the neural partition defined by synchrony, which is an equivalence relation). This definition mirrors the definition of the SRF: the SRF describes the set of stimuli for which a given group of neurons are synchronous, the synchrony partition describes the groups of neurons that are synchronous for a given stimulus. Fig. 2A shows the synchrony partition in a population of 25 heterogeneous neurons for three stimuli: inhibitory pulses of 300 ms, 400 ms and 500 ms. Each stimulus produces a different synchrony partition: for example, the three neurons colored in green for the 300 ms stimulus are not synchronous for the 400 ms stimulus.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Decoding synchrony patterns in a heterogeneous population.Each column corresponds to one stimulus duration. A, Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the “synchrony partition”. Circled neurons belong to the synchronous group of neuron A. B, Each synchronous group projects to a postsynaptic neuron. Each duration is associated with an assembly of postsynaptic neurons. C, Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g002: Decoding synchrony patterns in a heterogeneous population.Each column corresponds to one stimulus duration. A, Color represents the latency of the spike produced by each neuron responding to the stimulus (white if the neuron did not spike). Thus, neurons with the same color are synchronous for that specific stimulus (duration). The population can be divided in groups of synchronous neurons (i.e., with the same color), forming the “synchrony partition”. Circled neurons belong to the synchronous group of neuron A. B, Each synchronous group projects to a postsynaptic neuron. Each duration is associated with an assembly of postsynaptic neurons. C, Activation of the postsynaptic assembly as a function of duration (grey: individual neurons; black: average).
Mentions: We can now apply these principles to decode synchrony patterns at the population level. Consider a population of neurons with rebound spiking properties but heterogeneous parameters. For example, in Fig. 2A, the membrane time constant varies randomly across neurons between 10 and 50 ms, and the K+ channel time constant varies between 300 and 500 ms (see Text S1 for a justification of this choice of parameter values). For a given stimulus, for example an inhibitory pulse with duration 300 ms, we can look at the synchrony pattern in the neural population. In Fig. 1E (top left), neurons represented with the same color produce a rebound spike at the same time (with a 2 ms precision), that is, the stimulus is in the SRF of neurons with the same color. Thus, the neuron population can be divided in groups of synchronous neurons (possibly containing just one neuron). I call this partition of the neural population the synchrony partition (mathematically, it is the neural partition defined by synchrony, which is an equivalence relation). This definition mirrors the definition of the SRF: the SRF describes the set of stimuli for which a given group of neurons are synchronous, the synchrony partition describes the groups of neurons that are synchronous for a given stimulus. Fig. 2A shows the synchrony partition in a population of 25 heterogeneous neurons for three stimuli: inhibitory pulses of 300 ms, 400 ms and 500 ms. Each stimulus produces a different synchrony partition: for example, the three neurons colored in green for the 300 ms stimulus are not synchronous for the 400 ms stimulus.

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