Abstract / Description of output
Recent studies have pointed out that the neural code may use multiplexing to encode unique information at different temporal scales of spike trains [1]. However, how to mathematically separate out the different components of a neural code and to identify the unique contribution of each time scale to sensory coding and behaviour has remained an open challenge [2].
Here we investigated this problem by developing a novel approach to spike train analysis based on a popular image compression method, the Laplacian Pyramid Decomposition (LPD) [3]. This technique allows us to express the neuronal response in a basis that characterises different temporal scales (see figure).
Using the LPD as a framework, we have analytically calculated the information contained in each temporal scale as follows: first, we have separated the information about the stimuli into two components: the information that each scale provides and the information that is redundant [4] across different temporal scales. In a second step, we have performed a short time-scale series expansion [5] of these two components in order to quantify the amount of information that one scale contains about another. We then showed that the first order approximation of this contamination is non-symmetric: it can be non-zero only from coarse to fine scales. Furthermore, when the stimuli do not elicit any fine pattern in the neural response, the first order components of the information contained in all scales are equal. Taking these results into account, and assuming a firing rate regime in which the first order component of the information dominates, we propose a method to attribute redundant information to a specific scale and hence obtain a well-interpretable separation of scale-specific information.
Our method aims at separating information uniquely contained in each scale and thereby provides a promising analysis method to study in detail the advantages and limitations of a multiplexed neural code.
Here we investigated this problem by developing a novel approach to spike train analysis based on a popular image compression method, the Laplacian Pyramid Decomposition (LPD) [3]. This technique allows us to express the neuronal response in a basis that characterises different temporal scales (see figure).
Using the LPD as a framework, we have analytically calculated the information contained in each temporal scale as follows: first, we have separated the information about the stimuli into two components: the information that each scale provides and the information that is redundant [4] across different temporal scales. In a second step, we have performed a short time-scale series expansion [5] of these two components in order to quantify the amount of information that one scale contains about another. We then showed that the first order approximation of this contamination is non-symmetric: it can be non-zero only from coarse to fine scales. Furthermore, when the stimuli do not elicit any fine pattern in the neural response, the first order components of the information contained in all scales are equal. Taking these results into account, and assuming a firing rate regime in which the first order component of the information dominates, we propose a method to attribute redundant information to a specific scale and hence obtain a well-interpretable separation of scale-specific information.
Our method aims at separating information uniquely contained in each scale and thereby provides a promising analysis method to study in detail the advantages and limitations of a multiplexed neural code.
| Original language | English |
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| Number of pages | 1 |
| DOIs | |
| Publication status | Published - 15 Sept 2015 |
| Event | Bernstein Conference 2015 - Duration: 25 Sept 2015 → 28 Sept 2015 |
Conference
| Conference | Bernstein Conference 2015 |
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| Period | 25/09/15 → 28/09/15 |