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Introducing a Comprehensive Framework to Measure Spike-LFP Coupling

Zarei, Mohammad | 2019

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 52410 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Jahed, Mehran; Daliri, Mohammad Reza
  7. Abstract:
  8. LFP characteristics have been shown to be informative of sensory stimuli and influenced by attention. We show the gamma power in area MT is selective to motion direction showing a Gaussian tuning curve. Here we show for the first time, some evidence based on a single monkey’s data that spatial attention enhances the tuning curve based on gamma power.The synchronization of activity across neurons has been a focus in many recent studies of information processing in networks of cortical cells and areas. One aspect of such inter-neuronal synchrony is the locking of spiking activity to local field potentials (LFPs). Such an interaction has been observed between the timing of individual action potentials (‘spikes’) of single neurons and the phase of low frequency (<15 Hz) oscillations of LFPs. However, the potential role of this phase-locking in neural encoding is unknown. To address this question, two behaving male macaque monkeys were trained to maintain their gaze on a central fixation point on a computer screen while two coherently moving random dot patterns (RDP) were simultaneously presented at eccentric locations, moving linearly in the same direction. One of the two RDPs was presented inside the receptive field of the recorded neuron and moved either in the neuron’s preferred or anti-preferred direction LFPs and spikes were recorded from visual cortical area MT. To investigate if phase-locking depends on the sensory properties of the visual stimulus, we measured the interconnection (locking) between spikes and the phase of low frequency LFP oscillations as a function of the stimulus’ motion direction. We found that the phase-locking follows a tuning curve based on the presented stimulus’ direction. This function is inverted compared to the tuning of the spike rate, i.e., the least spike-LFP coupling occurs for the preferred direction (based on the spike rate), while the strongest spike-LFP coupling is induced by the anti-preferred direction. This implies that those spikes added to a neuron’s spike train in response to the preferred (rather than the anti-preferred) stimulus are inserted during LFP phases with a low spike rate, reducing the overall phase-locking. We tested this by comparing the neural discrimination calculated based on the spike-rate at the preferred vs. anti-preferred LFP phase. We found that the neural discrimination in the preferred LFP phase is significantly larger than the anti-preferred LFP phase. This suggests that neural information encoded in the spike rate varies with the LFP phase. Our data suggest that 1) the neural system harnesses spike-LFP coupling in the primate visual cortex to encode visual information and 2) the information coded by single neurons fluctuates relative to the surrounding LFP phase. Measuring the coupling of single neuron’s spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates
  9. Keywords:
  10. Spike Train ; Local Field Potential ; Receptive Field ; Phase Locking Value ; Visual Attention ; Pairwise Phase Consistency (PPC)

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