Cognitive computation using neural representations of time and space in the Laplace domain
Files
First author draft
Date
2020-03-25
Authors
Howard, Marc W.
Hasselmo, Michael E.
Version
First author draft
OA Version
Citation
M.W. Howard, M.E. Hasselmo. 2020. "Cognitive computation using neural representations of time and space in
the Laplace domain"
Abstract
Memory for the past makes use of a record of what happened when---a function
over past time. Time cells in the hippocampus and temporal context cells in the
entorhinal cortex both code for events as a function of past time, but with
very different receptive fields. Time cells in the hippocampus can be
understood as a compressed estimate of events as a function of the past.
Temporal context cells in the entorhinal cortex can be understood as the
Laplace transform of that function, respectively. Other functional cell types
in the hippocampus and related regions, including border cells, place cells,
trajectory coding, splitter cells, can be understood as coding for functions
over space or past movements or their Laplace transforms. More abstract
quantities, like distance in an abstract conceptual space or numerosity could
also be mapped onto populations of neurons coding for the Laplace transform of
functions over those variables. Quantitative cognitive models of memory and
evidence accumulation can also be specified in this framework allowing
constraints from both behavior and neurophysiology. More generally, the
computational power of the Laplace domain could be important for efficiently
implementing data-independent operators, which could serve as a basis for
neural models of a very broad range of cognitive computations.