The inputs and task goals of different decoders are listed in Table 1. For example, the decoder $network(space,identity)$ received intermediate processing information about space from the brain space network (i.e., inputs were artificial neural activities from the second-to-last layer of the brain $networkspace$) but then was trained to decode information about identity from it. Similar arguments can be applied to other decoders.

Table 1:

Inputs and Task Goals of Different Decoders When the Brain Network Was Trained with a Different or Same Task.

Take the second-to-last-layer
Decoder Nameactivities fromTo do the task
$network(space,identity)$ $networkspace$ identity
$network(location,identity)$ $networklocation$ identity
$network(orientation,identity)$ $networkorientation$ identity
$network(identity,space)$ $networkidentity$ space
$network(shoes,space)$ $networkshoes$ space
$network(identity,location)$ $networkidentity$ location
$network(identity,orientation)$ $networkidentity$ orientation
$network(identity,shoes)$ $networkidentity$ shoes
$network(space,shoes)$ $networkspace$ shoes
$network(space,identity,shoes)$ $network(space,identity)$ shoes
$network(identity,identity)$ $networkidentity$ identity
$network(space,space)$ $networkspace$ space
Take the second-to-last-layer
Decoder Nameactivities fromTo do the task
$network(space,identity)$ $networkspace$ identity
$network(location,identity)$ $networklocation$ identity
$network(orientation,identity)$ $networkorientation$ identity
$network(identity,space)$ $networkidentity$ space
$network(shoes,space)$ $networkshoes$ space
$network(identity,location)$ $networkidentity$ location
$network(identity,orientation)$ $networkidentity$ orientation
$network(identity,shoes)$ $networkidentity$ shoes
$network(space,shoes)$ $networkspace$ shoes
$network(space,identity,shoes)$ $network(space,identity)$ shoes
$network(identity,identity)$ $networkidentity$ identity
$network(space,space)$ $networkspace$ space

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