RT Journal Article
SR Electronic
T1 Distinguishing examples while building concepts in hippocampal and artificial networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.02.21.529365
DO 10.1101/2023.02.21.529365
A1 Louis Kang
A1 Taro Toyoizumi
YR 2023
UL http://biorxiv.org/content/early/2023/02/21/2023.02.21.529365.abstract
AB The hippocampal subfield CA3 is thought to function as an autoassociative network that stores sensory information as memories. This information arrives via the entorhinal cortex (EC), which projects to CA3 directly as well as indirectly through the dentate gyrus (DG). DG sparsifies and decorrelates the information before also projecting to CA3. The computational purpose for receiving two encodings of the same sensory information has not been firmly established. We model CA3 as a Hopfield-like network that stores both correlated and decorrelated encodings and retrieves them at low and high inhibitory tone, respectively. As more memories are stored, the dense, correlated encodings merge along shared features while the sparse, decorrelated encodings remain distinct. In this way, the model learns to transition between concept and example representations by controlling inhibitory tone. To experimentally test for the presence of these complementary encodings, we analyze the theta-modulated tuning of phase-precessing place cells in rat CA3. In accordance with our model’s prediction, these neurons exhibit more precise spatial tuning and encode more detailed task features during theta phases with sparser activity. Finally, we generalize the model beyond hippocampal architecture and find that feedforward neural networks trained in multitask learning benefit from a novel loss term that promotes hybrid encoding using correlated and decorrelated representations. Thus, the complementary encodings that we have found in CA3 can provide broad computational advantages for solving complex tasks.Competing Interest StatementThe authors have declared no competing interest.