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Geometric interpretation of robustness in spike coding networks


Gonçalves,  Pedro J.
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Calaim, N., Dehmelt, F. A., Gonçalves, P. J., & Machens, C. (2019). Geometric interpretation of robustness in spike coding networks. Poster presented at Computational and Systems Neuroscience (Cosyne) 2019, Lisbon, Portugal.

Cite as: https://hdl.handle.net/21.11116/0000-0006-8EDB-4
Models of face, object, and scene recognition traditionally focus on massively parallel processing of low- level
features, with higher order representations emerging at later processing stages [1,2,3]. However, visual perception
relies on eye movements, which are necessarily sequential. Neurons in entorhinal cortex with grid cell-like firing
have recently been reported in response to eye movements, i.e. in visual space [4,5,6]. A functional explanation
for these ‘visual grid cells’ is so far lacking. Grid cells (GCs) [7] are predominantly known from spatial memory
and navigation studies and exhibit regularly arranged firing fields in the navigation plan. Ensembles of GCs are
thought to underlie path integration and the calculation of goal- directed movement vectors [8,9,10]. Following the
presumed role of GCs in vector navigation, we propose a model of recognition memory for familiar faces, objects,
and scenes, in which GCs encode translation vectors between salient stimulus features. A sequence of saccadic
eye movement vectors, moving from one salient feature to the expected location of the next, potentially confirms an
initial hypothesis (accumulating evidence towards a threshold) about stimulus identity. This identification is based
on the relative feature layout (going beyond recognition of individual features), and implements an relational active
sensing strategy to infer the stimulus identity of exemplars within a stimulus category. Category identification is
hypothesised to occur in earlier occipito-temporal areas, likely via parallel processing. The model constitutes
the first quantitative proposal for a role of GCs in visual recognition. The variance of GC activity along saccade
trajectories exhibits 6-fold symmetry across 360 degrees akin to recently reported fMRI data [5,6]. The mechanism
is robust with regard to partial visual occlusion, can accommodate size and position invariance, and suggests
a functional explanation for medial temporal lobe involvement in visual memory for relational information and
memory guided attention.