Acquisition and consolidation of spatial memory by hippocampal predicitve computation

2017 Fiscal Year Final Research Report

• Project/Area Number: 15H04265
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Neurophysiology / General neuroscience
• Research Institution: Institute of Physical and Chemical Research
• Principal Investigator: Fukai Tomoki 国立研究開発法人理化学研究所, 脳科学総合研究センター, チームリーダー (40218871)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: 自発発火 / 樹状突起 / 学習と記憶 / 神経回路モデル / シナプス可塑性 / ベイズ推定 / 回路構造

Outline of Final Research Achievements

We studied an inference task model to demonstrate that an adequate network structure naturally emerges from dual Hebbian learning for both synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. We constructed a cortical network model of UP-DOWN transitions to indicate the role of persistent UP states for the prolonged repetition of a selected set of cell assemblies during memory
consolidation. We proposed a network model of sequence learning which instantiates two synaptic pathways, one for proximal dendrite-somatic interactions to generate spontaneous sequences and the other for distal dendritic processing of extrinsic signals. The model performs robust one-shot learning of spatial memory. We showed that redundant synaptic connections between a neuron pair enable near-optimal learning by approximating a sample-based Bayesian filtering algorithm.

Free Research Field

計算論的神経科学、ソフトコンピューティング