DOI: 10.5061/dryad.n8pk0p37z
title: Recorded neural data from: Nonlinear feedback modulation contributes to the optimization of flexible decision-making

description: Neural activity in the primate brain correlates with both sensory
 evaluation and action selection aspects of decision-making. However, the
 intricate interaction between these distinct neural processes and their
 impact on decision behaviors remains unexplored. Here, we examined the
 interplay of these decision processes in the posterior parietal cortex
 (PPC) when monkeys performed a flexible decision task. We found that the
 PPC activity related to monkeys’ abstract decisions about visual stimuli
 was nonlinearly modulated by monkeys’ following saccade choices directed
 outside each neuron’s response field. Recurrent neural network modeling
 suggested that the feedback connections, which matched the learned
 stimulus-response associations during the task, might mediate such
 feedback modulation. Further analysis on network dynamics revealed that
 selectivity-specific feedback connectivity intensified the attractor
 basins of population activity underlying saccade choices, thereby
 increasing the reliability of flexible decisions. These results highlight
 an iterative computation between different decision processes, mediated
 primarily by precise feedback connectivity, contributing to the
 optimization of flexible decision-making.
description: # Recorded neural data from: Nonlinear feedback modulation contributes to
 the optimization of flexible decision-making This dataset contains the
 recorded single-neuron data from two macaque monkeys on a reaction-time
 version of the flexible visual-motion discrimination (FVMD) task. This
 dataset is used in the study
 [https://doi.org/10.7554/eLife.96402.2](https://doi.org/10.7554/eLife.96402.2). #### Description of the data and file structure Files are stored in MATLAB (.mat) format #### File Naming Each file contains neural recording data from one session of motion discrimination experiments. The files are named according to: `MONKEY_DM_CO_YYYYMMDD_SESSION.mat` Where: * **MONKEY**: Subject identifier * "Bootsy": First monkey subject * "Meceo": Second monkey subject * **DM**: Motion discrimination task * **CO**: Coherence task variant * **YYYYMMDD**: Recording date (Year-Month-Day) * **SESSION**: Session number for that day For example, `Bootsy_DM_CO_20180502_2.mat` indicates data from monkey Bootsy, motion discrimination coherence task, recorded on May 2nd, 2018, session 2. #### Task Description The motion discrimination task involves: * Visual motion stimuli with varying coherence levels * Neural recordings from lateral intraparietal (LIP) cortex neurons * Behavioral choice data during motion discrimination decisions #### File Content Each file contains one variable: **session** (struct): Main data structure with two fields: * **x** (struct): Trial-by-trial experimental data containing: * `trial_type`: Trial classification * `spike_times`: Neural spike timing data * `spike_signals`: Neural spike signals * `sample_coherence`: Motion coherence levels for each trial * `sample_motion_dir`: Motion direction for each trial * `sample_category`: Stimulus category information * `condition_number`: Experimental condition identifier * `task_cond_num`: Task condition number * `TrialError`: Trial outcome/error codes * `EyePosition`: Eye tracking data * `LFP`: Local field potential recordings * Timing events: `fixation_on`, `fixation_off`, `sample_stimulus_on/off`, `test_stimulus_on/off`, `ISI_start/end`, etc. * Behavioral events: `lever_hold`, `lever_release`, `reward_time`, `sac_target_on` * **NeuronInfo** (struct): Neuron metadata containing: * `Waveforms`: Spike waveform data Data represents "good neurons" that met quality criteria for motion discrimination analysis. #### File List * Bootsy_DM_CO_20180502_3.mat * Bootsy_DM_CO_20180502_4.mat * Bootsy_DM_CO_20180504_1.mat * Bootsy_DM_CO_20180504_2.mat * Bootsy_DM_CO_20180514_1.mat * Bootsy_DM_CO_20180514_2.mat * Bootsy_DM_CO_20180515_1.mat * Bootsy_DM_CO_20180515_5.mat * Bootsy_DM_CO_20180516_4.mat * Bootsy_DM_CO_20180516_5.mat * Bootsy_DM_CO_20180518_3.mat * Bootsy_DM_CO_20180518_4.mat * Bootsy_DM_CO_20180522_1.mat * Bootsy_DM_CO_20180525_3.mat * Bootsy_DM_CO_20180530_3.mat * Bootsy_DM_CO_20180530_5.mat * Bootsy_DM_CO_20180531_3.mat * Bootsy_DM_CO_20180531_4.mat * Bootsy_DM_CO_20180602_1.mat * Bootsy_DM_CO_20180606_1.mat * Bootsy_DM_CO_20180607_1.mat * Bootsy_DM_CO_20180607_2.mat * Bootsy_DM_CO_20180607_3.mat * Bootsy_DM_CO_20180608_1.mat * Bootsy_DM_CO_20180608_2.mat * Bootsy_DM_CO_20180608_3.mat * Bootsy_DM_CO_20180608_4.mat * Bootsy_DM_CO_20180609_1.mat * Bootsy_DM_CO_20180609_2.mat * Bootsy_DM_CO_20180609_4.mat * Bootsy_DM_CO_20180609_5.mat * Bootsy_DM_CO_20180611_3.mat * Bootsy_DM_CO_20180611_4.mat * Bootsy_DM_CO_20180611_5.mat * Bootsy_DM_CO_20180612_1.mat * Bootsy_DM_CO_20180612_3.mat * Bootsy_DM_CO_20180612_4.mat * Bootsy_DM_CO_20180612_5.mat * Bootsy_DM_CO_20180614_1.mat * Bootsy_DM_CO_20180614_2.mat * Bootsy_DM_CO_20180614_3.mat * Bootsy_DM_CO_20180614_4.mat * Bootsy_DM_CO_20180614_5.mat * Bootsy_DM_CO_20180615_2.mat * Bootsy_DM_CO_20180615_3.mat * Bootsy_DM_CO_20180615_4.mat * Bootsy_DM_CO_20180616_1.mat * Bootsy_DM_CO_20180616_3.mat * Bootsy_DM_CO_20180616_4.mat * Bootsy_DM_CO_20180616_5.mat * Bootsy_DM_CO_20180805_1.mat * Bootsy_DM_CO_20180805_2.mat