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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 mechanism with sensory stimuli.A, Schematic representation of stimulus encoding by a neuron: the stimulus S is filtered through the receptive field N, and the resulting signal N(S) is nonlinearly transformed into spike trains. The synchrony receptive field of two different neurons A and B is the set of stimuli such that the two filtered signals match: NA(S) = NB(S). B, Schematic representation of a standard receptive field (N(S)>θ) and a synchrony receptive field in a two-dimensional world. C, Fluctuating input and independent noise. Right: input autocorrelation (time constant 5 ms). D, Responses of a noisy integrate-and-fire model in repeated trials. Right: shuffled auto-correlogram (SAC) for different signal-to-noise ratios (SNR). E, Precision and reliability of spike timing as a function of SNR.
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pcbi-1002561-g005: Synchrony mechanism with sensory stimuli.A, Schematic representation of stimulus encoding by a neuron: the stimulus S is filtered through the receptive field N, and the resulting signal N(S) is nonlinearly transformed into spike trains. The synchrony receptive field of two different neurons A and B is the set of stimuli such that the two filtered signals match: NA(S) = NB(S). B, Schematic representation of a standard receptive field (N(S)>θ) and a synchrony receptive field in a two-dimensional world. C, Fluctuating input and independent noise. Right: input autocorrelation (time constant 5 ms). D, Responses of a noisy integrate-and-fire model in repeated trials. Right: shuffled auto-correlogram (SAC) for different signal-to-noise ratios (SNR). E, Precision and reliability of spike timing as a function of SNR.

Mentions: I introduced the concepts of synchrony receptive fields and synchrony partition with an elementary example, duration selectivity, where stimuli are one-dimensional. Real world stimuli, on the other hand, vary along many dimensions, which makes computation much more difficult [50]. To understand synchrony patterns in this more general setting, I describe neuron responses in the following simplified way (Fig. 5A, top): a stimulus S is transformed through a linear or non-linear filter N, which represents the (standard) receptive field of the neuron, then the filtered stimulus N(S) is mapped to a spike train through a non-linear spiking transformation (for example, N(S) is the input to a spiking neuron model). Note that although this description appears to be feedforward, the computation of the filter N may or may not rely on a feedforward circuit. Assuming that two neurons A and B fire in synchrony when they receive the same dynamic input NA(S) and NB(S), the SRF of A and B is the set of stimuli S such that NA(S) = NB(S). In mathematical terms, this is a manifold of stimulus space; if the neural filters are linear, it is a linear subspace of stimuli. For example, in two dimensions, the SRF is a line (Fig. 5B, left). In contrast, a neuron fires when the filtered stimulus exceeds some threshold, N(S)>θ, that is, in two dimensions, when the stimulus is on one side of a line (Fig. 5B, right). In higher dimension, a neuron fires when the stimulus is on one side of a hyperplane, while two neurons fire in synchrony when the stimulus is close to a hyperplane (assuming linear filtering). This makes computation with synchrony qualitatively different from rate-based computation, with interesting computational properties, for example SRFs are unchanged by linear scaling of the stimulus (i.e., intensity change).


Computing with neural synchrony.

Brette R - PLoS Comput. Biol. (2012)

Synchrony mechanism with sensory stimuli.A, Schematic representation of stimulus encoding by a neuron: the stimulus S is filtered through the receptive field N, and the resulting signal N(S) is nonlinearly transformed into spike trains. The synchrony receptive field of two different neurons A and B is the set of stimuli such that the two filtered signals match: NA(S) = NB(S). B, Schematic representation of a standard receptive field (N(S)>θ) and a synchrony receptive field in a two-dimensional world. C, Fluctuating input and independent noise. Right: input autocorrelation (time constant 5 ms). D, Responses of a noisy integrate-and-fire model in repeated trials. Right: shuffled auto-correlogram (SAC) for different signal-to-noise ratios (SNR). E, Precision and reliability of spike timing as a function of SNR.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375225&req=5

pcbi-1002561-g005: Synchrony mechanism with sensory stimuli.A, Schematic representation of stimulus encoding by a neuron: the stimulus S is filtered through the receptive field N, and the resulting signal N(S) is nonlinearly transformed into spike trains. The synchrony receptive field of two different neurons A and B is the set of stimuli such that the two filtered signals match: NA(S) = NB(S). B, Schematic representation of a standard receptive field (N(S)>θ) and a synchrony receptive field in a two-dimensional world. C, Fluctuating input and independent noise. Right: input autocorrelation (time constant 5 ms). D, Responses of a noisy integrate-and-fire model in repeated trials. Right: shuffled auto-correlogram (SAC) for different signal-to-noise ratios (SNR). E, Precision and reliability of spike timing as a function of SNR.
Mentions: I introduced the concepts of synchrony receptive fields and synchrony partition with an elementary example, duration selectivity, where stimuli are one-dimensional. Real world stimuli, on the other hand, vary along many dimensions, which makes computation much more difficult [50]. To understand synchrony patterns in this more general setting, I describe neuron responses in the following simplified way (Fig. 5A, top): a stimulus S is transformed through a linear or non-linear filter N, which represents the (standard) receptive field of the neuron, then the filtered stimulus N(S) is mapped to a spike train through a non-linear spiking transformation (for example, N(S) is the input to a spiking neuron model). Note that although this description appears to be feedforward, the computation of the filter N may or may not rely on a feedforward circuit. Assuming that two neurons A and B fire in synchrony when they receive the same dynamic input NA(S) and NB(S), the SRF of A and B is the set of stimuli S such that NA(S) = NB(S). In mathematical terms, this is a manifold of stimulus space; if the neural filters are linear, it is a linear subspace of stimuli. For example, in two dimensions, the SRF is a line (Fig. 5B, left). In contrast, a neuron fires when the filtered stimulus exceeds some threshold, N(S)>θ, that is, in two dimensions, when the stimulus is on one side of a line (Fig. 5B, right). In higher dimension, a neuron fires when the stimulus is on one side of a hyperplane, while two neurons fire in synchrony when the stimulus is close to a hyperplane (assuming linear filtering). This makes computation with synchrony qualitatively different from rate-based computation, with interesting computational properties, for example SRFs are unchanged by linear scaling of the stimulus (i.e., intensity change).

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
Related in: MedlinePlus