--- jupytext: formats: md:myst text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.15.0 kernelspec: display_name: Python 3 (ipykernel) language: python name: ctlut --- # Accessing Dynamic Foraging data The Dynamic foraging task is being used to study and model decision making, and is being combined with other modalities of physiology data including electrophysiology and fiber photometry to explore how neurons, networks, and neuromodulators are involved in the task. At this time, we are sharing ~400 sessions of **behavior only** sessions of the Dynamic Foraging task, including sessions from different stages of training. The mice in these session are likely being trained for different ultimate experiments (e.g. fiber photometry or ephys), and so there is a range of different procedural preparations (e.g. different genotypes and viral injections). But the behavior is all the same task. ## Looking at the metadata TODO ## Loading a session These data can be loaded using pynwb ```{code-cell} ipython3 from hdmf_zarr import NWBZarrIO import pandas as pd import matplotlib.pyplot as plt %matplotlib inline ``` ```{code-cell} ipython3 session_path = '/data/behavior_761433_2025-03-27_08-51-54_processed_2025-03-28_05-00-28/nwb/behavior_761433_2025-03-27_08-51-54.nwb' io = NWBZarrIO(session_path, "r") nwb = io.read() ``` Because there is only the behavioral task, these files are smaller and simpler than many of the physiology NWB files. ### Trials The trials table documents the parameters and behavior of each trial. ```{code-cell} ipython3 trials = nwb.intervals['trials'][:] trials.head() ``` Let's see what all is documented here: ```{code-cell} ipython3 trials.columns ``` start_time : Start time of trial (Probably in seconds, relative to some arbitrary clock a long time in the past) stop_time : End time of trial (Probably in seconds, relative to some arbitrary clock a long time in the past) animal_response : The response of the animal. 0, left choice. 1, right choice. 2, no response. rewarded_historyL : The reward history of the left lick spout rewarded_historyR : The reward histroy of the right lick spout delay_start_time : The delay start time goCue_start_time : The go cue start time reward_outcome_time : The reward outcome time - when the choice is registered bait_left : Whether the left lick spout has a bait or not bait_right : Whether the right lick spout has a bait or not base_reward_probability_sum : The sum of left and right reward probability reward_probabilityL : The reward probability of the left lick spout reward_probabilityR : The reward probability of the right lick spout reward_random_number_left : The random number used to determine the reward of the left lick spout reward_random_number_right : The random number used to determine the reward of the right lick spout left_valve_open_time : The left valve open time (probably in seconds?) right_valve_open_time : The right valve open time (probably in seconds?) block_beta : The beta of exponential distribution to generate the block length block_min : The minimum length allowed for each block block_max : The maximum length allowed for each block min_reward_each_block : The minimum reward allowed for each block delay_beta : The beta of exponential distribution to generate the delay duration delay_min : The minimum duration(s) allowed for each delay delay_max : The maximum duration(s) allowed for each delay delay_duration : The expected time duration between delay start and go cue start ITI_beta : The beta of exponential distribution to generate the inter-trial interval (ITI) duration(s) ITI_min : The minimum duration(s) allowed for each ITI ITI_max : The maximum duration(s) allowed for each ITI ITI_duration : The expected time duration between trial start and ITI start response_duration : The maximum time in which an animal must make a choice in order to get a reward reward_consumption_duration : The duration for the animal to consume the reward laser_on_trial : Trials with laser stimulation (IGNORE) laser_wavelength : The wavelength of the laser (IGNORE) laser_location : The target brain areas (IGNORE) laser_power : The laser power (IGNORE) laser_duration : The laser duration (IGNORE) laser_condition : The laser on is conditioned on LaserCondition ? (IGNORE) laser_condition_probability : The laser on is conditioned on Laser Condition with probability ? (IGNORE) laser_start : Laser start is aligned to an event ? (IGNORE) laser_start_offset : Laser start is aligned to an event with an offset (IGNORE) laser_end : Laser end is aligned to an event ? (IGNORE) laser_end_offset : Laser end is aligned to an event with an offset (IGNORE) laser_protocol : The laser waveform (IGNORE) laser_frequency : The laser waveform frequency laser_rampingdown (IGNORE) : The ramping down time of the laser laser_pulse_duration (IGNORE) : The pulse duration for Pulse protocol auto_waterL : Autowater given at left lick spout auto_waterR : Autowater given at right lick spout auto_train_engaged : Whether the auto training is engaged auto_train_curriculum_name : The name of the auto training curriculum auto_train_curriulum_version : The version of the auto training curriculum auto_train_schema_version : The schema version of the auto trainin curriculum auto_train_stage : The current stage of auto training auto_train_stage_overridden : Whether the auto training stage is overridden lickspout_position_x : x position (um) of the lickspout (left-right) lickspout_position_y : y position (um) of the lickspout (forward-backward) lickspout_position_z : z position (um) of the lickspout position (up-down) reward_size_left : left reward size (uL) reward_size_right : right reward size (uL) ### Behavioral data The `acquisition` element of the nwb file has the lick and reward times for the two lick spouts. Here we plot those events alongside the reward probability for the two sides ```{code-cell} ipython3 fig, ax1 = plt.subplots(figsize=(12,5)) #plot lick times for left and right reward spouts ax1.plot(nwb.acquisition['right_lick_time'].timestamps[:], nwb.acquisition['right_lick_time'].data[:], 'g.', label="right lick") ax1.plot(nwb.acquisition['left_lick_time'].timestamps[:], nwb.acquisition['left_lick_time'].data[:]-1, 'b.', label="left lick") #plot the reward probability of each trial ax1.plot(trials.start_time.values, trials.reward_probabilityR, 'g', label="right") ax1.plot(trials.start_time.values, trials.reward_probabilityL, 'b', label="left") ax1.set_ylabel("Reward Probability") #plot the reward from right and left spouts on right axis ax2 = ax1.twinx() ax2.plot(nwb.acquisition['right_reward_delivery_time'].timestamps[:], nwb.acquisition['right_reward_delivery_time'].data[:], 'g^', label="right reward") ax2.plot(nwb.acquisition['left_reward_delivery_time'].timestamps[:], nwb.acquisition['left_reward_delivery_time'].data[:], 'b^', label="left reward") ax1.set_ylim(-0.5, 1.5) plt.xlim(trials.start_time[20], trials.stop_time[80]) ```