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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 field.A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). Neuron B has similar properties but different values for the threshold and K channel parameters (blue line). The synchrony receptive field of neurons A and B is the stimulus with duration 500 ms. C, A postsynaptic neuron receives inputs from A and B. D, It is more likely to fire when the stimulus in the synchrony receptive field of A and B. E, Distribution p(v) of the postsynaptic membrane potential when the neuron is not stimulated (left, “background”) and when it receives an input of size Δv (right, “signal”; e.g. neurons A and B shown in panel C fire together). The standard deviation of the distribution is σ. The neuron fires when v is greater than the spike threshold θ. F, Receiver-operation characteristic (ROC) for three levels of noise, obtained by varying the threshold θ (black curves). The hit rate is the probability that the neuron fires within one integration time constant τ when depolarized by Δv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curves, with sensitivity index d′ = Δv/σ, are shown in red. G, When a neuron receives two synchronous inputs of size w (PSP peak), the peak potential is 2w plus the background noise (left). When the second input arrives after a delay δ, the peak is  plus the background noise (right). H, Distinguishing between synchronous inputs and delayed inputs corresponds to setting a threshold θ between two distributions separated by .
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pcbi-1002561-g001: Synchrony receptive field.A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). Neuron B has similar properties but different values for the threshold and K channel parameters (blue line). The synchrony receptive field of neurons A and B is the stimulus with duration 500 ms. C, A postsynaptic neuron receives inputs from A and B. D, It is more likely to fire when the stimulus in the synchrony receptive field of A and B. E, Distribution p(v) of the postsynaptic membrane potential when the neuron is not stimulated (left, “background”) and when it receives an input of size Δv (right, “signal”; e.g. neurons A and B shown in panel C fire together). The standard deviation of the distribution is σ. The neuron fires when v is greater than the spike threshold θ. F, Receiver-operation characteristic (ROC) for three levels of noise, obtained by varying the threshold θ (black curves). The hit rate is the probability that the neuron fires within one integration time constant τ when depolarized by Δv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curves, with sensitivity index d′ = Δv/σ, are shown in red. G, When a neuron receives two synchronous inputs of size w (PSP peak), the peak potential is 2w plus the background noise (left). When the second input arrives after a delay δ, the peak is plus the background noise (right). H, Distinguishing between synchronous inputs and delayed inputs corresponds to setting a threshold θ between two distributions separated by .

Mentions: For synchrony to be computationally useful, it must be stimulus-dependent. To illustrate this idea, let us consider neurons which spike after being hyperpolarized (“rebound spiking”), because of the presence of voltage-activated conductances (Fig. 1; simple neuron models are used in this and all other figures; see Methods for details). Neurons with rebound spiking have been found for example in the superior paraolivary nucleus of the auditory brainstem, a structure involved in encoding the temporal structure of sounds [33]; and in the pyloric network of lobsters, involved in the generation of rhythmic motor patterns [34]. Fig. 1 shows a minimal neuron model with this property (but it is only meant as an illustration). The model includes a slow outward current, modeling K+ channels, which activates at low voltages (half-activation voltage −70 mV). This current prevents the neuron from spontaneously spiking. When the neuron is inhibited for a few hundred ms (Fig. 1A, top), the K+ channels slowly close (the conductance decreases, Fig. 1A, bottom). When inhibition is released, the negative K+ current is smaller than at rest, which makes the neuron spike. The latency of the rebound spike depends on the value of the K+ conductance when inhibition is released, and therefore on the duration of inhibition: if the neuron is inhibited for a shorter duration, K+ channels are still partially open when inhibition is released and the neuron spikes later. If inhibition is very short, the neuron may not spike. Thus, the timing of the rebound spike is negatively correlated with the duration of inhibition. Fig. 1B shows this relationship for two different model neurons A and B, which have the same rebound spiking property but quantitatively different parameter values (spike threshold and time constant of K+ channels).


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Synchrony receptive field.A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). Neuron B has similar properties but different values for the threshold and K channel parameters (blue line). The synchrony receptive field of neurons A and B is the stimulus with duration 500 ms. C, A postsynaptic neuron receives inputs from A and B. D, It is more likely to fire when the stimulus in the synchrony receptive field of A and B. E, Distribution p(v) of the postsynaptic membrane potential when the neuron is not stimulated (left, “background”) and when it receives an input of size Δv (right, “signal”; e.g. neurons A and B shown in panel C fire together). The standard deviation of the distribution is σ. The neuron fires when v is greater than the spike threshold θ. F, Receiver-operation characteristic (ROC) for three levels of noise, obtained by varying the threshold θ (black curves). The hit rate is the probability that the neuron fires within one integration time constant τ when depolarized by Δv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curves, with sensitivity index d′ = Δv/σ, are shown in red. G, When a neuron receives two synchronous inputs of size w (PSP peak), the peak potential is 2w plus the background noise (left). When the second input arrives after a delay δ, the peak is  plus the background noise (right). H, Distinguishing between synchronous inputs and delayed inputs corresponds to setting a threshold θ between two distributions separated by .
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g001: Synchrony receptive field.A, When neuron A is hyperpolarized by an inhibitory input (top), its low-voltage-activated K channels slowly close (bottom), which makes the neuron fire when inhibition is released (neuron models are used in this and other figures). B, Spike latency is negatively correlated with the duration of inhibition (black line). Neuron B has similar properties but different values for the threshold and K channel parameters (blue line). The synchrony receptive field of neurons A and B is the stimulus with duration 500 ms. C, A postsynaptic neuron receives inputs from A and B. D, It is more likely to fire when the stimulus in the synchrony receptive field of A and B. E, Distribution p(v) of the postsynaptic membrane potential when the neuron is not stimulated (left, “background”) and when it receives an input of size Δv (right, “signal”; e.g. neurons A and B shown in panel C fire together). The standard deviation of the distribution is σ. The neuron fires when v is greater than the spike threshold θ. F, Receiver-operation characteristic (ROC) for three levels of noise, obtained by varying the threshold θ (black curves). The hit rate is the probability that the neuron fires within one integration time constant τ when depolarized by Δv, and the false alarm rate is the firing probability without depolarization. The corresponding theoretical curves, with sensitivity index d′ = Δv/σ, are shown in red. G, When a neuron receives two synchronous inputs of size w (PSP peak), the peak potential is 2w plus the background noise (left). When the second input arrives after a delay δ, the peak is plus the background noise (right). H, Distinguishing between synchronous inputs and delayed inputs corresponds to setting a threshold θ between two distributions separated by .
Mentions: For synchrony to be computationally useful, it must be stimulus-dependent. To illustrate this idea, let us consider neurons which spike after being hyperpolarized (“rebound spiking”), because of the presence of voltage-activated conductances (Fig. 1; simple neuron models are used in this and all other figures; see Methods for details). Neurons with rebound spiking have been found for example in the superior paraolivary nucleus of the auditory brainstem, a structure involved in encoding the temporal structure of sounds [33]; and in the pyloric network of lobsters, involved in the generation of rhythmic motor patterns [34]. Fig. 1 shows a minimal neuron model with this property (but it is only meant as an illustration). The model includes a slow outward current, modeling K+ channels, which activates at low voltages (half-activation voltage −70 mV). This current prevents the neuron from spontaneously spiking. When the neuron is inhibited for a few hundred ms (Fig. 1A, top), the K+ channels slowly close (the conductance decreases, Fig. 1A, bottom). When inhibition is released, the negative K+ current is smaller than at rest, which makes the neuron spike. The latency of the rebound spike depends on the value of the K+ conductance when inhibition is released, and therefore on the duration of inhibition: if the neuron is inhibited for a shorter duration, K+ channels are still partially open when inhibition is released and the neuron spikes later. If inhibition is very short, the neuron may not spike. Thus, the timing of the rebound spike is negatively correlated with the duration of inhibition. Fig. 1B shows this relationship for two different model neurons A and B, which have the same rebound spiking property but quantitatively different parameter values (spike threshold and time constant of K+ channels).

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