Adaptive action selection during stimulus categorization is an important feature of flexible behavior. To examine neural mechanism underlying this process, we trained mice to categorize the spatial frequencies of visual stimuli according to a boundary that changed between blocks of trials in a session. Using a model with a dynamic decision criterion, we found that sensory history was important for adaptive action selection after the switch of boundary. Bilateral inactivation of the secondary motor cortex (M2) impaired adaptive action selection by reducing the behavioral influence of sensory history. Electrophysiological recordings showed that M2 neurons carried more information about upcoming choice and previous sensory stimuli when sensorimotor association was being remapped than when it was stable. Thus, M2 causally contributes to flexible action selection during stimulus categorization, with the representations of upcoming choice and sensory history regulated by the demand to remap stimulus-action association.

Behavioral  experiments were controlled by custom Matlab (Mathworks) scripts and digital I/O devices (PCI-6503, National Instruments Corporation). Neural signals were amplified and filtered using the Cerebus 32-channel system (Blackrock Microsystems). Task-related behavioral events were digitized as TTL levels and recorded by the Cerebus system. Data analyses were performed in Matlab.

Dataset: Behavior

Each mat file contains behavior data from one session of one mice performing the flexible visual categorization task, and can be opened in MATLAB.

variables:

log.pic_Fname: the name and path of visual stimuli, indicates the spatial frequency used.

log.res_vector_cw: outcome,block,stimulus of each trial.

    sign of the number means correct/wrong. + is correct, - is wrong.

    tens place is block indentity. 1 is low boundary block, 2 is high boundary block.

    ones place is visual stimulus identity. 1 is the lowest spatial frequency in the stimulus set,
        7 is the higher spatial frequency. check BasicIn.stim_list for exact number.

    as such, -14 means a trial peformed in the low boundary block, with the intermediate SF (the 4th stimulus, reverisng stimulus) shown.
        
        since it is a wrong trial, with reversing stimulus in the low boundary block, from the rule of the task,

        it indicates that the action the mouse took in that trial is left choice (see paper).

log.rsp_vector_cw: response latency of each trial.

Dataset: Recording

Each mat file contains electrophysiology recording data from one mice performing the flexible visual categorization task, and can be opened in MATLAB.

Data shared here have gone through cross-channel duplicate screening and unit tracking procedures described in the paper.

Spike times are aligned to the onset of "Go" signal of each trial.

Variables:

BasicIn.stim_list: spatial frequency of visual stimuli used, in cyc/deg.

BasicIn.coarse_binsize: bin size for data in res_psth_cbin, in ms.

BasicIn.t_pre: time window before "Go" signal, in ms.

BasicIn.t_post: time window after "Go" signal, in ms.

BasicIn.pre_bin_num: number of timebin before "Go" signal, according to BasicIn.t_pre and BasicIn.coarse_binsize.

BasicIn.post_bin_num: number of timebin after "Go" signal, according to BasicIn.t_post and BasicIn.coarse_binsize.

id: existence of tracked units in each session.

res_block_num_each_unit: number of blocks for each tracked unit.

    For example, "[6;8;7]" in the 6th cell means for the 6th tracked unit, data are from 3 sessions, and the mouse performed 6、8、7 blocks
during each session.

res_block_size_each_unit: size of each block for each tracked unit.

res_psth_1ms: psth in 1ms timebin.

res_psth_cbin: psth in timebin of BasicIn.coarse_binsize.

res_spk_train: spk time for each unit during each trial.

res_vector_cw_each_unit: behavior trial information (outcome,block,stimulus) of each tracked unit.

wmean: average waveform of tracked unit.