253 research outputs found
Linking Cellular Mechanisms to Behavior: Entorhinal Persistent Spiking and Membrane Potential Oscillations May Underlie Path Integration, Grid Cell Firing, and Episodic Memory
The entorhinal cortex plays an important role in spatial memory and episodic memory functions. These functions may result from cellular mechanisms for integration of the afferent input to entorhinal cortex. This article reviews physiological data on persistent spiking and membrane potential oscillations in entorhinal cortex then presents models showing how both these cellular mechanisms could contribute to properties observed during unit recording, including grid cell firing, and how they could underlie behavioural functions including path integration. The interaction of oscillations and persistent firing could contribute to encoding and retrieval of trajectories through space and time as a mechanism relevant to episodic memory.Silvio O. Conte Center (NIMH MH71702, MH60450); National Institute of Mental Health Research (MH60013, MH61492); National Science Foundation (SLC SBE 0354378); National Institute of Drug Abuse (DA16454)
Analyses of Markov Decision Process Structure Regarding the Possible Strategic use of Interacting Memory Systems
Behavioral tasks are often used to study the different memory systems present in humans and animals. Such tasks are usually designed to isolate and measure some aspect of a single memory system. However, it is not necessarily clear that any given task actually does isolate a system or that the strategy used by a subject in the experiment is the one desired by the experimenter. We have previously shown that when tasks are written mathematically as a form of partially observable Markov decision processes, the structure of the tasks provide information regarding the possible utility of certain memory systems. These previous analyses dealt with the disambiguation problem: given a specific ambiguous observation of the environment, is there information provided by a given memory strategy that can disambiguate that observation to allow a correct decision? Here we extend this approach to cases where multiple memory systems can be strategically combined in different ways. Specifically, we analyze the disambiguation arising from three ways by which episodic-like memory retrieval might be cued (by another episodic-like memory, by a semantic association, or by working memory for some earlier observation). We also consider the disambiguation arising from holding earlier working memories, episodic-like memories or semantic associations in working memory. From these analyses we can begin to develop a quantitative hierarchy among memory systems in which stimulus-response memories and semantic associations provide no disambiguation while the episodic memory system provides the most flexible disambiguation, with working memory at an intermediate level
Sources of the spatial code within the hippocampus
Neurons in the hippocampus are thought to provide information on an animal's location within its environment. Input to the hippocampus comes via afferents from the entorhinal cortex, which are separated into several major pathways serving different hippocampal regions. Recent studies show the significance of individual afferent pathways in location perception, enhancing our understanding of hippocampal function
Cognitive computation using neural representations of time and space in the Laplace domain
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.First author draf
- …