Competitive Hebbian learning and the hippocampal place cell system: Modeling the interaction of visual and path integration cues

Abstract

The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.

Path integration in mammals

“…In recent times, a number of attempts have been made to describe PI in terms of arithmetical models (Fujita et al, 1990), flow diagrams (Mittelstaedt, 2000), and especially neural networks (Hartmann and Wehner, 1995;Guazzelli et al, 2001;Wittman and Schwegler, 1995;McNaughton et al, 1996;Samsonovich and McNaughton, 1997;Maurer, 1998;Arleo and Gerstner, 2000;Stringer et al, 2002). Most network models take into account available evidence that external references as well as PI are needed to complement each other, suggesting the binding process discussed in the next section.…”

Delineation of hippocampal subregions using T1-weighted magnetic resonance images at 3 Tesla

“…Animal studies suggest that the ventral (corresponding to the anterior hippocampus in humans) or rostral hippocampus has strong connections with prefrontal regions (Barbas & Blatt, 1995; Carr & Sesack, 1996) and may be involved in context coding (Nadel et al, 2013; Rajah, Kromas, Han, & Pruessner, 2010). In contrast, other data indicate that the dorsal hippocampus (corresponding to the posterior hippocampus in humans) plays a role in spatial behavior (Nadel et al, 2013), thus supporting the idea that this region of the hippocampus is part of a functional network connected to sensory cortical areas including the parietal cortex (Save & Poucet, 2000; Guazzelli, Bota, & Arbib, 2001). Although a distinction in the hippocampal rostrocaudal axis has been made in MR imaging studies at the gross anatomic level, to our knowledge it has not been fully incorporated into the delineation of a specific hippocampal subregion.…”

Adaptive WTA With an Analog VLSI Neuromorphic Learning Chip

“…To this end, it employs competitive Hebbian learning. Note that the term competitive Hebbian learning has been used in the literature with rather different meanings and in different contexts, for example, [16] and [45]. We refer to competitive Hebbian learning as the learning in artificial neurons that form a WTA network due to strong cross inhibition, and are set to learn according to a Hebbian learning scheme only if they are the winner.…”