The role of competitive learning in the generation of DG fields from EC inputs

Abstract

We follow up on a suggestion by Rolls and coworkers, that the effects of competitive learning should be assessed on the shape and number of spatial fields that dentate gyrus (DG) granule cells may form when receiving input from medial entorhinal cortex (mEC) grid units. We consider a simple non-dynamical model where DG units are described by a threshold-linear transfer function, and receive feedforward inputs from 1,000 mEC model grid units of various spacing, orientation and spatial phase. Feedforward weights are updated according to a Hebbian rule as the virtual rodent follows a long simulated trajectory through a single environment. Dentate activity is constrained to be very sparse. We find that indeed competitive Hebbian learning tends to result in a few active DG units with a single place field each, rounded in shape and made larger by iterative weight changes. These effects are more pronounced when produced with thousands of DG units and inputs per DG unit, which the realistic system has available, than with fewer units and inputs, in which case several DG units persists with multiple fields. The emergence of single-field units with learning is in contrast, however, to recent data indicating that most active DG units do have multiple fields. We show how multiple irregularly arranged fields can be produced by the addition of non-space selective lateral entorhinal cortex (lEC) units, which are modelled as simply providing an additional effective input specific to each DG unit. The mean number of such multiple DG fields is enhanced, in particular, when lEC and mEC inputs have overall similar variance across DG units. Finally, we show that in a restricted environment the mean size of the fields is unaltered, while their mean number is scaled down with the area of the environment.

Optimal Population Codes for Space: Grid Cells Outperform Place Cells

“…In general, a downstream neuron that decodes the animal's position might have access to only a restricted number of grid cell inputs; predicting the size of grid fields also required us to assume that the number of grid cells is finite. Several theoretical models propose that the ensemble firing of grid cells gives rise to single, isolated place fields in hippocampus by superposition (Fuhs & Touretzky, 2006;Solstad et al, 2006;Rolls et al, 2006;Franzius et al, 2007;Si & Treves, 2009;Cheng & Loren, 2010); arbitrary or all-to-all connections between grid and place cell layers, however, often give rise to multiple firing fields (Solstad et al, 2006). The average of measured firing field to period ratios lies around 0.3 , which is consistent with both the theoretical prediction and the hypothesis that each place cell in DG and CA3 is strongly innervated by only a small subsample of grid cells from each grid module along the dorsoventral band (Solstad et al, 2006).…”

“…Theoretical studies on the coding properties of grid cells (Burak, Brookings, & Fiete, 2006;Fiete, Burak, & Brookings, 2008) have dealt with the spatial range encoded by populations of grid cells, without assuming an explicit noise model. Here, our focus will be on neither the spatial range nor how gridlike firing patterns arise (Fuhs & Touretzky, 2006;McNaughton, Battaglia, Jensen, Moser, & Moser, 2006;Burgess, Barry, & O'Keefe, 2007;Kropff & Treves, 2008;Burak & Fiete, 2009;Remme, Lengyel, & Gutkin, 2010;Zilli & Hasselmo, 2010;Mhatre, Gorchetchnikov, & Grossberg, 2010), nor how grid fields can lead to place fields (Fuhs & Touretzky, 2006;Solstad, Moser, & Einevoll, 2006;Rolls, Stringer, & Elliot, 2006;Franzius, Vollgraf, & Wiskott, 2007;Si & Treves, 2009;Cheng & Loren, 2010). Rather, we extract general observations about grid and place cells from experimental findings and relate these to the resolution of population codes.…”

New and Distinct Hippocampal Place Codes Are Generated in a New Environment during Septal Inactivation

“…Many models predict that hippocampal representations of self-location emerge from two inputs, one from landmarks in the environment and the second from self-motion (Burgess and O’Keefe, 1996, 2011; O’Keefe, 1976; Rotenberg and Muller, 1997). It has been suggested that medial entorhinal cortex (MEC) grid cells provide the self-motion configured spatial framework (McNaughton et al, 2006) and that they are the primary source of spatial information onto place cells (Blair et al, 2008; Cheng and Frank, 2011; de Almeida et al, 2012; Hasselmo, 2009; Hayman and Jeffery, 2008; McNaughton et al, 2006; Molter and Yamaguchi, 2008; Monaco and Abbott, 2011; Rolls et al, 2006; Savelli and Knierim, 2010; Si and Treves, 2009; Solstad et al, 2006). This hypothesis is supported by the findings that grid cells project directly to the hippocampus (Zhang et al, 2013), that entorhinal lesions disrupt the precision and stability of hippocampal place fields (Brun et al, 2008; Miller and Best, 1980; Van Cauter et al, 2008), and that global remapping in the hippocampus co-occurs with grid field realignment (Barry et al, 2012; Fyhn et al, 2007).…”

Locating and navigation mechanism based on place-cell and grid-cell models

“…Consequently, some models were proposed based on place cells in (Arleo et al 2004;Sheynikhovich et al 2005;Krichmar et al 2005;Arleo and Gerstner 2000;Kulvicius et al 2008). It is commonly believed that place cells may be driven by grid cells (McNaughton et al 2006;Solstad et al 2006a, b;Molter and Yamaguchi 2008;Si and Treves 2009 Sreenivasan and Fiete 2011). In the study by Bonnevie et al (2013), experimental data suggest that the feedback from place cells to grid cells is more prominent than that of grid cells on place cells.…”