Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network

By Alessandro Treves, E. T. Rolls. 1992

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, methodology): “…regions, therefore, are viewed by a wide range of experimentalists and theoreticians as a cardinal feature of hippocampal representation and computation (J. K. Leutgeb et al., 2007; Marr, 1971; McClelland et al., 1995; McNaughton & Morris, 1987; O’Reilly & McClelland, 1994; Treves & Rolls, 1992).”
A theory of hippocampal function in memory (1996, methodology): “The quantitative analysis shows that the perforant path input would not produce a pattern of firing in CA3 that contains sufficient information for learning (Treves and Rolls, 1992).”
Memory for events and their spatial context: models and experiments. (2001, background): “…from the dentate gyrus onto the CA3 pyramidal cells act as`detonator' synapses (being physically large and near to the cell body) that impose the new pattern of activity during encoding, while recurrent collaterals dominate retrieval (McNaughton & Morris 1987; see also Treves & Rolls 1992).”
Tracking the Flow of Hippocampal Computation: Pattern Separation, Pattern Completion, and Attractor Dynamics (2015, background): “When CA3 is presented with input cues from EC in a novel environment, the representation is sparsified in the DG and the powerful mossy fiber inputs from DG to CA3 impose a pattern of activity on CA3 that becomes associated with the same EC cues that drove DG (Treves & Rolls, 1992).”
The atoms of neural computation (2014, background): “As another example, the cortex is closely coupled to the hippocampus (Hasselmo & Wyble, 1997; Treves & Rolls, 1992) to enable various forms of memory storage and retrieval, which may also be coordinated via the thalamus (Ketz et al., 2014).”
What is the function of hippocampal theta rhythm?—Linking behavioral data to phasic properties of field potential and unit recording data (2005, background): “I appreciate comments on the manuscript from Lisa Giocomo, Amy Griffin, Christina Rossi, Chantal Stern, and Eric Zilli.”
Computational influence of adult neurogenesis on memory encoding. (2009, unknown): “Several hippocampal studies have suggested that the DG's pattern separation function is only required during initial memory formation, with memory retrieval bypassing the DG via the direct EC-CA3 projection (Kesner et al., 2004; Treves and Rolls, 1992).”
A Role for Hilar Cells in Pattern Separation in the Dentate Gyrus: A Computational Approach (2009, background): “…an elegant series of computational models of the hippocampus, and have showed that these models can successfully perform pattern storage and completion, and account for a range of empirical data (Rolls, 1989a,b, 1996, 2007; Treves and Rolls, 1992, 1994; Rolls and Kesner, 2006; Rolls et al., 2006).”
Considerations arising from a complementary learning systems perspective on hippocampus and neocortex (1996, background): “In this case, the resulting pattern separation in CM is far greater than ir would be in the absence of the DG input. supporting the idea char one function of DG is ro enhance the separation of parrerns on C-\3 (Treves and Rolls, 1992).”
A putative role for neurogenesis in neuro-computational terms: inferences from a hippocampal model. (2009, unknown): “…also suggests that the two input pathways to CA3 should have different functions: the patterns to be stored would be presented through the mossy fibers (coming from DG), while the cue initiating memory retrieval would enter through the perforant pathway (coming from EC) (Treves & Rolls, 1992).”