AIMC Topic: Entorhinal Cortex
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Bio-inspired multi-scale fusion.
Biological cybernetics
Apr 22, 2020
We reveal how implementing the homogeneous, multi-scale mapping frameworks observed in the mammalian brain's mapping systems radically improves the performance of a range of current robotic localization techniques. Roboticists have developed a range ...
Modeling place cells and grid cells in multi-compartment environments: Entorhinal-hippocampal loop as a multisensory integration circuit.
Neural networks : the official journal of the International Neural Network Society
Sep 6, 2019
Hippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental data show that different subsets of place cells are controlled by vision, self-motion or a...
Modeling grid fields instead of modeling grid cells : An effective model at the macroscopic level and its relationship with the underlying microscopic neural system.
Journal of computational neuroscience
Jul 8, 2019
A neuron's firing correlates are defined as the features of the external world to which its activity is correlated. In many parts of the brain, neurons have quite simple such firing correlates. A striking example are grid cells in the rodent medial e...
Phase relations of theta oscillations in a computer model of the hippocampal CA1 field: Key role of Schaffer collaterals.
Neural networks : the official journal of the International Neural Network Society
Apr 8, 2019
The hippocampal theta rhythm (4-12 Hz) is one of the most important electrophysiological processes in the hippocampus, it participates in cognitive hippocampal functions, such as navigation in space, novelty detection, and declarative memory. We use ...
Merging information in the entorhinal cortex: what can we learn from robotics experiments and modeling?
The Journal of experimental biology
Feb 6, 2019
Place recognition is a complex process involving idiothetic and allothetic information. In mammals, evidence suggests that visual information stemming from the temporal and parietal cortical areas ('what' and 'where' information) is merged at the lev...
Vector-based navigation using grid-like representations in artificial agents.
Nature
May 9, 2018
Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcem...
Cognitive memory and mapping in a brain-like system for robotic navigation.
Neural networks : the official journal of the International Neural Network Society
Dec 7, 2016
Electrophysiological studies in animals may provide a great insight into developing brain-like models of spatial cognition for robots. These studies suggest that the spatial ability of animals requires proper functioning of the hippocampus and the en...
Characterizing the Input-Output Function of the Olfactory-Limbic Pathway in the Guinea Pig.
Computational intelligence and neuroscience
Jul 28, 2015
Nowadays the neuroscientific community is taking more and more advantage of the continuous interaction between engineers and computational neuroscientists in order to develop neuroprostheses aimed at replacing damaged brain areas with artificial devi...
A hierarchical model of goal directed navigation selects trajectories in a visual environment.
Neurobiology of learning and memory
Jul 29, 2014
We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model's fle...
The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks.
eLife
Jul 4, 2025
Critical network states and neural plasticity enable adaptive behavior in dynamic environments, supporting efficient information processing and experience-dependent learning. Synaptic-weight-based Hebbian plasticity and homeostatic synaptic scaling a...
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