Two‐Phase Computational Model Training Long‐Term Memories in the Entorhinal‐Hippocampal Region

Abstract

The computational model described here is driven by the hypothesis that a major function of the entorhinal cortex (EC)-hippocampal system is to alter synaptic connections in the neocortex. It is based on the following postulates:(1) The EC compares the difference between neocortical representations (primary input) and feedback information conveyed by the hippocampus (the "reconstructed input"). The difference between the primary input and the reconstructed input (termed "error") initiates plastic changes in the hippocampal networks (error compensation). (2) Comparison of the primary input and reconstructed input requires that these representations are available simultaneously in the EC network. We suggest that compensation of time delays is achieved by predictive structures, such as the CA3 recurrent network and EC-CA1 connections.(3) Alteration of intrahippocampal connections gives rise to a new hippocampal output. The hippocampus generates separated (independent) outputs, which, in turn, train long-term memory traces in the EC (independent components, IC). The ICs of the long-term memory trace are generated in a two-step manner, the operations of which we attribute to the activities of the CA3 (whitening) and CA1 (separation) fields. (4) The different hippocampal fields can perform both nonlinear and linear operations, albeit at different times (theta and sharp phases). We suggest that long-term memory is represented in a distributed and hierarchical reconstruction network, which is under the supervision of the hippocampal output. Several of these model predictions can be tested experimentally. COMPUTATIONAL ASSUMPTIONS IN THE HIPPOCAMPAL-ENTORHINAL CORTEX SYSTEMThe main goal of this chapter is to discuss how the various subnetworks of the entorhinal-hippocampal system can perform different operations depending on the "state" of the brain. We attempt to describe the entorhinal cortex (EC)-hippocampal system as a collection of structure-function relationships. Our basic assumption is that the major function subserved by the hippocampus is to develop a representation that can rehearse past events-episodes-and can make predictions about ongoing events based on previously learned temporal sequences. From this single principle and theoretical considerations, we assign symbolic (mathematical) functions to each anatomical field of the EC-hippocampal loop. We also derive that an important function of the c

Qualitatively Different Hippocampal Subfield Engagement Emerges with Mastery of a Spatial Memory Task by Rats

“…This view is consistent with the proposed importance of temporoammonic but not trisynaptic circuit inputs to CA1 in the maintenance of spatial representations (Brun et al, 2002), and in the consolidation of intermediate-or long-but not short-term memory (Remondes and Schuman, 2004;Vago et al, 2007). Our findings support the view that, via their physiological differences and their parallel inputs from the entorhinal cortex, hippocampal subfields can contribute to spatial tasks in distinct ways (Lörincz and Buzsáki, 2000;Witter et al, 2000;Brun et al, 2002;.…”

“…The early ability to discriminate arms and the accompanying dentate gyrus Zif268 pattern fit the putative roles for the dentate gyrus in spatial pattern separation (O'Reilly and McClelland, 1994;McClelland and Goddard, 1996;Rolls, 1996;Lörincz and Buzsáki, 2000;Gilbert et al, 2001), spatial novelty (Lee et al, 2005), and "error" processing (Lörincz and Buzsáki, 2000). Our findings also fit well with the proposed role of the dentate gyrus in driving the establishment of new spatial representations in CA3 (McNaughton and Morris, 1987;Treves and Rolls, 1992), which itself may be accompanied by late, not early changes in CA3 communication (Rekart et al, 2007).…”

“…Proposals include the idea that the dentate gyrus is more important for the encoding of spatial information, whereas CA1 is more critical for consolidation or retrieval processes Remondes and Schuman, 2004). Much of the evidence for this view has come from lesion studies (for review, see , along with some support from computational modeling and physiological evidence (McClelland and Goddard, 1996;Redish and Touretzky, 1997;Lörincz and Buzsáki, 2000;Rolls and Kesner, 2006). There are, however, inherent limitations in testing this proposal with the lesion approach.…”

Interaction between neocortical and hippocampal networks via slow oscillations

“…Importantly, the 'content' of the SPW events is determined by previous experience of the animal (Buzsáki, 1989;Wilson and McNaughton, 1994;Kudrimoti et al, 1999;Nadasdy et al, 1999;Lee and Wilson, 2002). Because of its behaviorrelevant content and because of the 3-5-fold gain of network excitability during the SPW , this endogeneous hippocampal-output pattern, which is active during 'off-line' states of the hippocampus, might have a crucial role in transferring transient memories from the hippocampus to the neocortex for permanent storage (Buzsáki, 1998;Buzsáki, 1989;Buzsáki and Chrobak, 1995;Chrobak and Buzsáki, 1998b;Siapas and Wilson, 1998;Lorincz and Buzsáki, 2000).…”

Memory Capacity for Sequences in a Recurrent Network with Biological Constraints

“…It is generally assumed that the hippocampus can operate in at least two states (Lörincz & Buzsàki, 2000). One state, called theta, is dedicated to fast, or one-shot, learning; the other state, referred to as sharp-wave ripple, is dedicated to the replay of stored sequences.…”