CA3 Retrieves Coherent Representations from Degraded Input: Direct Evidence for CA3 Pattern Completion and Dentate Gyrus Pattern Separation

By Joshua P. Neunuebel, James J. Knierim. 2014

465 citations recorded by the source corpus.

Summary Theories of associative memory suggest that successful memory storage and recall depends on a balance between two complementary processes: pattern separation (to minimize interference) and pattern completion (to retrieve a memory when presented with partial or degraded input cues). Putative attractor circuitry in the hippocampal CA3 region is thought to be the final arbiter between these two processes. Here we present the first direct, quantitative evidence that CA3 produces an output pattern closer to the originally stored representation than its degraded input patterns from the dentate gyrus (DG). We simultaneously recorded activity from CA3 and DG of behaving rats when local and global reference frames were placed in conflict. CA3 showed a coherent population response to the conflict (pattern completion), even though its DG inputs were severely disrupted (pattern separation). The results thus confirm the hallmark predictions of a longstanding computational model of hippocampal memory processing.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018, background): “, in the dentate gyrus, which are responsible for pattern separation through the orthogonalization of incoming inputs supporting the auto-associative storage and retrieval of item-specific information from individual episodes (Yassa and Stark, 2011; Neunuebel and Knierim, 2014).”
Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “Slight alterations in environmental cues resulted in dentate activity patterns that were less correlated than those in the entorhinal cortex (Neunuebel et al., 2013; Neunuebel and Knierim, 2014).”
Tonic Inhibitory Control of Dentate Gyrus Granule Cells by α5-Containing GABAA Receptors Reduces Memory Interference (2015, methodology): “The involvement of the DG in interference reduction has been demonstrated through studies providing at least partial empirical support to computational models (Gilbert et al., 2001; McHugh et al., 2007; Sahay et al., 2011; Morris et al., 2012; Neunuebel and Knierim, 2014).”
Sparse activity of identified dentate granule cells during spatial exploration (2016, background): “Sparse coding in the DG has been postulated to subserve ‘pattern separation’, a key function during memory processing (McNaughton and Morris, 1987; Leutgeb et al., 2007; Myers and Scharfman, 2009; Neunuebel and Knierim, 2014).”
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. (2016, unknown): “Evidence suggests that the dentate gyrus (DG) subregion of the hippocampus performs pattern separation, orthogonalizing incoming inputs before auto-associative storage in the CA3 region [131 – 137].”
Strong Evidence for Pattern Separation in Human Dentate Gyrus (2016, background): “…the hippocampal DG region in rodents supports pattern separated representations (Leutgeb et al., 2007; McHugh et al., 2007; Colgin et al., 2008; Neunuebel and Knierim, 2014; Lee et al., 2015), previous studies in humans have provided only indirect evidence for a role for the DG in this process…”
Functional Imaging of Dentate Granule Cells in the Adult Mouse Hippocampus (2016, unknown): “Through in vivo electrophysiological recordings and optogenetic silencing, the activity of dentate granule cells, the main excitatory neuronal cell population of the DG, has been associated with hippocampus-dependent behavioral pattern separation but also pattern completion (Leutgeb et al., 2007; Na”
Tracking the Flow of Hippocampal Computation: Pattern Separation, Pattern Completion, and Attractor Dynamics (2015, methodology): “DG performs pattern separation on its EC inputs Given that the MEC and LEC are the major inputs to the DG, we can begin to measure the Δinput – Δoutput curve of the DG (Neunuebel & Knierim, 2014).”
Spatial representations of granule cells and mossy cells of the dentate gyrus (2017, background): “Although accumulating evidence strongly supports the role of the DG in pattern separation (Neunuebel and Knierim, 2014; Hunsaker et al., 2008; Nakashiba et al., 2012; Yassa and Stark, 2011; Rolls and Kesner, 2006), the precise computational and circuit mechanisms underlying this role remain under de”
Pattern separation in the hippocampus through the eyes of computational modeling (2017, background): “…the values of both f 1 and f 2 metrics range from 0 (identical patterns) to 1 (completely different patterns) Leutgeb, 2007; McHugh et al., 2007; Neunuebel & Knierim, 2014) and human subjects (Ally, Hussey, Ko, & Molitor, 2013; Bakker et al., 2008; Berron et al., 2016; Lacy et al., 2011),…”
Physiological properties and behavioral correlates of hippocampal granule cells and mossy cells (2017, unknown): “…et al., 1983; Leutgeb et al., 2007 or slow firing: Mizumori et al., 1989; Jung and McNaughton, 1993; Gothard et al., 2001; Nitz and McNaughton, 2004; Neunuebel and Knierim, 2012, 2014) and to a lack of reliable data about mossy cell firing patterns and behavioral correlates (Henze and Buzsáki,…”