Where is the error? Hierarchical predictive coding through dendritic error computation

被引:39
作者
Mikulasch, Fabian A. [1 ]
Rudelt, Lucas [1 ]
Wibral, Michael [2 ]
Priesemann, Viola [1 ,3 ,4 ]
机构
[1] Max Planck Inst Dynam & Selforg, Gottingen, Germany
[2] Georg August Univ, Inst Dynam Biol Networks, Gottingen Campus, Gottingen, Germany
[3] Bernstein Ctr Computat Neurosci BCCN, Gottingen, Germany
[4] Georg August Univ, Dept Phys, Gottingen, Germany
关键词
TIMING-DEPENDENT PLASTICITY; LONG-TERM POTENTIATION; VISUAL-CORTEX; BAYESIAN-INFERENCE; NEURONAL CIRCUITS; CELL-TYPE; INHIBITION; FEEDBACK; MODEL; INTERNEURONS;
D O I
10.1016/j.tins.2022.09.007
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on effi- cient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.
引用
收藏
页码:45 / 59
页数:15
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