The GridPlaceMap neural model explains and simulates how both entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model simulates challenging behavioural and neurobiological data by embodying several parsimonious neural designs: Similar ring attractor mechanisms process the linear and angular path integration inputs that drive map learning; the same self-organizing map mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus may use mechanisms homologous to those for temporal learning (“time cells”) through lateral entorhinal cortex to hippocampus (“neural relativity”). Top-down hippocampus-to-entorhinal attentional mechanisms stabilize map learning, simulate how hippocampal inactivation may disrupt grid cells and help to explain data about theta, beta and gamma oscillations.
This is a preview of subscription content, log in to check access.
This research is supported in part by the SyNAPSE program of DARPA (HR0011-09-C-0001).
Fortenberry B, Gorchetchnikov A, Grossberg S (2012) Learned integration of visual, vestibular, and motor cues in multiple brain regions computes head direction during visually-guided navigation. Hippocampus 22:2219–2237CrossRefGoogle Scholar
Gorchetchnikov A, Grossberg S (2007) Space, time, and learning in the hippocampus: how fine spatial and temporal scales are expanded into population codes for behavioral control. Neural Netw 20:182–193CrossRefzbMATHGoogle Scholar
Grossberg S (2009) Beta oscillations and hippocampal place cell learning during exploration of novel environments. Hippocampus 19:881–885CrossRefGoogle Scholar
Grossberg S (2013) Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw 37:1–47CrossRefGoogle Scholar
Grossberg S, Merrill JWL (1992) A neural network model of adaptively timed reinforcement learning and hippocampal dynamics. Cogn Brain Res 1:3–38CrossRefGoogle Scholar
Grossberg S, Merrill JWL (l996). The hippocampus and cerebellum in adaptively timed learning, recognition, and movement. J Cogn Neurosci 8:257–277Google Scholar
Grossberg S, Pilly PK (2014) Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, and oscillations. Philos Trans R Soc B. 369:20120524CrossRefGoogle Scholar
Grossberg S, Versace M (2008) Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Res 1218:278–312CrossRefGoogle Scholar
Grossberg S, Schmajuk NA (1989) Neural dynamics of adaptive timing and temporal discrimination during associative learning. Neural Netw 2:79–102CrossRefGoogle Scholar
Mhatre H, Gorchetchnikov A, Grossberg S (2012) Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex. Hippocampus 22:320–334CrossRefGoogle Scholar
Pilly PK, Grossberg S (2012) How do spatial learning and memory occur in the brain? Coordinated learning of entorhinal grid cells and hippocampal place cells. J Cogn Neurosci 24:1031–1054CrossRefGoogle Scholar
Pilly PK, Grossberg S (2013) How reduction of theta rhythm by medium septum inactivation may disrupt periodic spatial responses of entorhinal grid cells by reduced cholinergic transmission. Frontiers Neural Circu. doi:10.3389/fncir.l2013.00173Google Scholar