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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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Generality of coincidence detection.A, Activation of postsynaptic assemblies as a function of duration (as in Fig. 2C) for three noise levels: σv = 0.07, 0.14, 0.28 (bottom to top curve). B, Same as A with synaptic conductances and σv = 0.14 (as in Fig. 2C; grey: individual neurons; black: average). C, Same as B using neurons with rebound spiking (identical to the presynaptic neurons).
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pcbi-1002561-g003: Generality of coincidence detection.A, Activation of postsynaptic assemblies as a function of duration (as in Fig. 2C) for three noise levels: σv = 0.07, 0.14, 0.28 (bottom to top curve). B, Same as A with synaptic conductances and σv = 0.14 (as in Fig. 2C; grey: individual neurons; black: average). C, Same as B using neurons with rebound spiking (identical to the presynaptic neurons).

Mentions: Decoding synchrony patterns requires that neurons are sensitive to coincidences (in the sense that they fire more when their inputs are coincident), but it does not rely on specific neural properties, as is shown in Fig. 3. Varying the amount of internal noise quantitatively changes the neuron sensitivity to coincidences (the sensitivity index d′ in the signal detection theory perspective) but it does not change the qualitative properties (Fig. 3A). In Fig. 3B, inputs to the neurons were modeled as excitatory synaptic conductances (exponentially decaying with time constant τe = 2 ms). The main difference is that the size of PSPs now depends on the driving force (synaptic reversal potential minus membrane potential). However, as argued in [19], for an excitatory synapse, the driving force is restricted to a rather small range below spike threshold (50–80 mV), so that it has little impact on PSP size and on coincidence detection properties. In Fig. 3C, the coincidence detector neurons are modeled in the same way as the presynaptic neurons, with rebound spiking (with time constants τ = 10 ms and τKLT = 400 ms, see the Methods for details). That is, neurons of the same type encode the stimuli and decode the synchrony patterns. The results are qualitatively unchanged.


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Generality of coincidence detection.A, Activation of postsynaptic assemblies as a function of duration (as in Fig. 2C) for three noise levels: σv = 0.07, 0.14, 0.28 (bottom to top curve). B, Same as A with synaptic conductances and σv = 0.14 (as in Fig. 2C; grey: individual neurons; black: average). C, Same as B using neurons with rebound spiking (identical to the presynaptic neurons).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g003: Generality of coincidence detection.A, Activation of postsynaptic assemblies as a function of duration (as in Fig. 2C) for three noise levels: σv = 0.07, 0.14, 0.28 (bottom to top curve). B, Same as A with synaptic conductances and σv = 0.14 (as in Fig. 2C; grey: individual neurons; black: average). C, Same as B using neurons with rebound spiking (identical to the presynaptic neurons).
Mentions: Decoding synchrony patterns requires that neurons are sensitive to coincidences (in the sense that they fire more when their inputs are coincident), but it does not rely on specific neural properties, as is shown in Fig. 3. Varying the amount of internal noise quantitatively changes the neuron sensitivity to coincidences (the sensitivity index d′ in the signal detection theory perspective) but it does not change the qualitative properties (Fig. 3A). In Fig. 3B, inputs to the neurons were modeled as excitatory synaptic conductances (exponentially decaying with time constant τe = 2 ms). The main difference is that the size of PSPs now depends on the driving force (synaptic reversal potential minus membrane potential). However, as argued in [19], for an excitatory synapse, the driving force is restricted to a rather small range below spike threshold (50–80 mV), so that it has little impact on PSP size and on coincidence detection properties. In Fig. 3C, the coincidence detector neurons are modeled in the same way as the presynaptic neurons, with rebound spiking (with time constants τ = 10 ms and τKLT = 400 ms, see the Methods for details). That is, neurons of the same type encode the stimuli and decode the synchrony patterns. The results are qualitatively unchanged.

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