Biological cognition is based on self-generated learning objectives. However, the mechanism by which this epistemic autonomy is realized by the neuronal substrate is not understood.
Artificial neural networks based on error backpropagation lack epistemic autonomy because they are mostly trained in a supervised fashion. In this respect, they face the symbol grounding problem of artificial intelligence.
We propose that the entorhinal-hippocampal complex, a brain structure located in the medial temporal lobe and central to memory, combines epistemic autonomy with intrinsically generated error gradients akin to error backpropagation.
We present evidence supporting the hypothesis that the counter-current inhibitory projections of the entorhinal-hippocampal complex implement a continuous self-supervised error minimization between network input and output.
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.
Figure 1: Forward Excitatory and Counter-Current Inhibitory Circuitry of the Entorhinal-Hippocampal Complex (EHC) Supporting Self-Supervision and Epistemic Autonomy. (A) Forward and backward hippocampal circuits. Left: The feed-forward information flow of the hippocampal trisynaptic pathway and its constituents19. The pathway (grey arrow) comprises projections from layer II entorhinal cortex (EC) stellate cells to the dentate gyrus (DG) and CA3 via the medial (MPP, light green) and lateral (LPP, yellow) perforant path (PP), mossy fiber projections of DG granule cells to CA3 pyramidal neurons (dark green), and CA3 projections to CA1 pyramidal neurons (the Schaffer collaterals, pink). The feedforward input is completed with direct projections from layer III EC neurons projecting to CA119. The output of the hippocampus (HPC) originates in CA1 and passes via the subiculum (not shown) to the EC LV/VI (purple). Right: Cortical input and hippocampal output coincide in EC, allowing the EHC comparator to compute the mismatch between the 2 signals. (B) Left: Counter-current inhibitory circuit complementing the forward excitatory loop (Box 3). Right: We hypothesize that this counter-current circuit carries error signals (yellow) that define a gradient that shapes synaptic plasticity along the forward excitatory loop, therefore implementing a biological version of error backpropagation. (C) Top: The synergy between the forward and feedback circuits shapes the continuous synaptic update in the forward loop such that it increasingly minimizes the error between the HPC input and output signals of the EC comparator. Middle: In this self-supervised learning scenario, environmental change (pink line) is reflected in the error amplitude, where error magnitude activates distinct physiological and behavioral responses. Bottom: Small amplitude errors perturbate the firing rate of principal cells, for instance, expressed as firing rate modulation in spatial navigation tasks. In contrast, large magnitude errors signal novelty and drive relearning supporting the reconstruction of this novel signal, leading to global remapping (see20 for a possible threshold-triggered synaptic mechanism based on neuronal depolarization levels). (D) Left: The interplay between excitatory and inhibitory cells in the EHC comparator. Cortical signals coded by input neurons (yellow) are propagated throughout the trisynaptic circuit (green arrow) to neurons reflecting the HPC reconstruction of the input signal (blue). Comparator neurons (brown) receive both the reconstruction and an inhibitory copy of the input activity (grey), which in turn modulate the firing level of counter-current GABAergic interneurons that backpropagate the error signal (orange line). At this stage, recurrent GABAergic projections within the HPC modulate network-level synaptic distributions, leading to a convergence of cortical and hippocampal signals and thus performing self-supervision. Right: Dependent on its magnitude, mismatch error activates a range of molecular, physiological, and behavioral phenomena observed during spatial navigation. See[36,42,91,92,93].
![Figure 1: Forward Excitatory and Counter-Current Inhibitory Circuitry of the Entorhinal-Hippocampal Complex (EHC) Supporting Self-Supervision and Epistemic Autonomy. (A) Forward and backward hippocampal circuits. Left. The feed-forward information flow of the hippocampal trisynaptic pathway and its constituents19. The pathway (grey arrow) comprises projections from layer II entorhinal cortex (EC) stellate cells to the dentate gyrus (DG) and CA3 via the medial (MPP, light green) and lateral (LPP, yellow) perforant path (PP), mossy fiber projections of DG granule cells to CA3 pyramidal neurons (dark green), and CA3 projections to CA1 pyramidal neurons (the Schaffer collaterals, pink). The feedforward input is completed with direct projections from layer III EC neurons projecting to CA119. The output of the hippocampus (HPC) originates in CA1 and passes via the subiculum (not shown) to the EC LV/VI (purple). Right. Cortical input and hippocampal output coincide in EC, allowing the EHC comparator to compute the mismatch between the 2 signals. (B) Left. Counter-current inhibitory circuit complementing the forward excitatory loop (Box 3). Right. We hypothesize that this counter-current circuit carries error signals (yellow) that define a gradient that shapes synaptic plasticity along the forward excitatory loop, therefore implementing a biological version of error backpropagation. (C) Top. The synergy between the forward and feedback circuits shapes the continuous synaptic update in the forward loop such that it increasingly minimizes the error between the HPC input and output signals of the EC comparator. Middle. In this self-supervised learning scenario, environmental change (pink line) is reflected in the error amplitude, where error magnitude activates distinct physiological and behavioral responses. Bottom. Small amplitude errors perturbate the firing rate of principal cells, for instance, expressed as firing rate modulation in spatial navigation tasks. In contrast, large magnitude errors signal novelty and drive relearning supporting the reconstruction of this novel signal, leading to global remapping (see20 for a possible threshold-triggered synaptic mechanism based on neuronal depolarization levels). (D) Left. The interplay between excitatory and inhibitory cells in the EHC comparator. Cortical signals coded by input neurons (yellow) are propagated throughout the trisynaptic circuit (green arrow) to neurons reflecting the HPC reconstruction of the input signal (blue). Comparator neurons (brown) receive both the reconstruction and an inhibitory copy of the input activity (grey), which in turn modulate the firing level of counter-current GABAergic interneurons that backpropagate the error signal (orange line). At this stage, recurrent GABAergic projections within the HPC modulate network-level synaptic distributions, leading to a convergence of cortical and hippocampal signals and thus performing self-supervision. Right. Dependent on its magnitude, mismatch error activates a range of molecular, physiological, and behavioral phenomena observed during spatial navigation. See [36,42,91,92,93].](/doc/ai/nn/2021-santospata-figure1-hippocampusselfsupervisionlearning.jpg)