Publications

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal–hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems. © 2021 Elsevier Ltd

JTD Keywords: computational model, dentate gyrus, error backpropagation, granule cells, grid cells, hippocampus, inhibition, input, neural-networks, neurons, transformation, Artificial intelligence, Artificial neural network, Back propagation, Backpropagation, Brain, Cognitive systems, Counter current, Error back-propagation, Error backpropagation, Errors, Expressing interneurons, Hippocampal complex, Hippocampus, Human experiment, Input and outputs, Learning, Mammal, Mammalian hippocampus, Mammals, Neural networks, Nonhuman, Review, Self-supervised learning

The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals “countercurrent” to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.

JTD Keywords: cognitive neuroscience, grid cells, long-term, networks, neural networks, novelty, oscillations, pattern separation, region, representation, working-memory, Cognitive neuroscience, Neural networks, Rat dentate gyrus, Systems neuroscience

We present a method for using long-term organotypic slice co-cultures of the entorhino-hippocampal formation to analyze the axon-regenerative properties of a determined compound. The culture method is based on the membrane interphase method, which is easy to perform and is generally reproducible. The degree of axonal regeneration after treatment in lesioned cultures can be seen directly using green fluorescent protein (GFP) transgenic mice or by axon tracing and histological methods. Possible changes in cell morphology after pharmacological treatment can be determined easily by focal in vitro electroporation. The well-preserved cytoarchitectonics in the co-culture facilitate the analysis of identified cells or regenerating axons. The protocol takes up to a month.

JTD Keywords: Cajal-retzius cells, Green-fluorescent-protein, In-vitro model, Rat hippocampus, Nervous-tissue, Brain-slices, Dentate gyrus, Gene-transfer, Cultures, Damage