A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Algorithm </td><td class="align_center">The number of state nodes </td><td class="align_center">The number of action nodes </td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">LPST </td><td class="align_center">70 </td><td class="align_center">50 </td></tr><tr><td class="align_left">STAMN </td><td class="align_center">11 </td><td class="align_center">44 </td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

The number of state nodes and action nodes in STAMN and LPST.

Computational Intelligence and Neuroscience

tab2

Table 2

Table 2: A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State 