In the present work we investigate the dependence of local field potential (LFP) on spiking activity and how this relationship can be influenced by experimental conditions (such as a motor task or behavioral state of the animal) which likely involve different neural populations and different correlations between spiking activity and LFP. Motivated by the success of a similar approach in simpler contexts and in anesthetized animals (Rasch, Logothetis et al. 2009), to accomplish this we use a simple linear approach, in which the LFP is just expressed as a convolution of the spike sequence (or the multi unit activity, MUA) with a kernel, the latter being determined by a criterion of optimal reconstruction. We found that phase relationships between spikes and LFP at frequencies below 40 Hz are determinant to understand linear dependence. This is consistent with other studies (Rasch, Gretton et al. 2008) according to which phase and power of LFP in lower frequency band (<10 Hz) are significant predictors of spiking activity, together with LFP power in high ([40 90] Hz) band. We also found that, further disentangling signals that hypothetically result from different neural populations which are involved in a given behavioral task, we can improve the kernel estimation for the assumed linear dependence of LFP on spiking activity. For example, dealing with separate classes of putative excitatory and inhibitory neurons, studying separately the LFP phase at which spikes are emitted, can increase the fraction of LFP estimated from spiking activity.

Role of behavioral state in linear estimation of local field potentials / D'Andrea, Valeria. - (2013 Dec 16).

Role of behavioral state in linear estimation of local field potentials

D'ANDREA, Valeria
2013

Abstract

In the present work we investigate the dependence of local field potential (LFP) on spiking activity and how this relationship can be influenced by experimental conditions (such as a motor task or behavioral state of the animal) which likely involve different neural populations and different correlations between spiking activity and LFP. Motivated by the success of a similar approach in simpler contexts and in anesthetized animals (Rasch, Logothetis et al. 2009), to accomplish this we use a simple linear approach, in which the LFP is just expressed as a convolution of the spike sequence (or the multi unit activity, MUA) with a kernel, the latter being determined by a criterion of optimal reconstruction. We found that phase relationships between spikes and LFP at frequencies below 40 Hz are determinant to understand linear dependence. This is consistent with other studies (Rasch, Gretton et al. 2008) according to which phase and power of LFP in lower frequency band (<10 Hz) are significant predictors of spiking activity, together with LFP power in high ([40 90] Hz) band. We also found that, further disentangling signals that hypothetically result from different neural populations which are involved in a given behavioral task, we can improve the kernel estimation for the assumed linear dependence of LFP on spiking activity. For example, dealing with separate classes of putative excitatory and inhibitory neurons, studying separately the LFP phase at which spikes are emitted, can increase the fraction of LFP estimated from spiking activity.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1659782
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