Hippocampal and spatial metrics
Hippocampal and spatial metrics depends on specific files and metadata to be processed the pipeline.
Table of contents
- Theta metrics
- Spatial metrics
- Deep-superficial metrics
A theta-band filtered time series is generated from the lfp file. Continues theta power and phase is then calculated from the generated time series. For each unit the average theta firing profile is calculated together with the theta phase peak/trough and the strength of the theta entrainment. Learn more about theta oscillation metrics. The tracking file is used for filtering by a minimum running speed.
|theta filtered channel|
|behavioral tracking file|
|Theta channel tag (required)|
All spatial metrics are generated from an existing 1D firing rate map. Learn more about spatial metrics and the firing rate map Matlab struct.
|1D firing rate map|
Deep-superficial metrics are calculated from ripple timestamps and the average ripple is extracted from a channel from the lfp file. A reveral point for the polarity of the sharp wave is derived from a time interval before the average ripple, aligned to their peaks. Deep-superficial distance is estimmated from the reversal point by assigning a numeric value determined from the channel offset to the reversal point.
Learn more about deep-superficial metrics, and see the tutorial here.
|LFP file (generated in the processing module)|
|Ripples events (generated in the processing module)|
|‘sessionName.deepSuperficialfromRipple.channelinfo.mat’||Ripples events (generated in the processing module)|
|Ripple channel tag (required)|
|Ripple channel tag (required; linear,staggered,poly2, edge,poly3,poly5)|
|Vertical spacing between sites (required, [µm])|
|Bad electrode groups (e.g. broken shanks)|
|Cortical spike groups|