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  1. AU="Amil, Adrián F"
  2. AU="Ehrhardt, M J"
  3. AU="Yokuş, Osman"
  4. AU="Gil-Moles, María"
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  1. Article ; Online: Erratum: Entorhinal mismatch: A model of self-supervised learning in the hippocampus.

    Santos-Pata, Diogo / Amil, Adrián F / Raikov, Ivan Georgiev / Rennó-Costa, César / Mura, Anna / Soltesz, Ivan / Verschure, Paul F M J

    iScience

    2021  Volume 24, Issue 5, Page(s) 102501

    Abstract: This corrects the article DOI: 10.1016/j.isci.2021.102364.]. ...

    Abstract [This corrects the article DOI: 10.1016/j.isci.2021.102364.].
    Language English
    Publishing date 2021-05-17
    Publishing country United States
    Document type Published Erratum
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2021.102501
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Entorhinal mismatch: A model of self-supervised learning in the hippocampus.

    Santos-Pata, Diogo / Amil, Adrián F / Raikov, Ivan Georgiev / Rennó-Costa, César / Mura, Anna / Soltesz, Ivan / Verschure, Paul F M J

    iScience

    2021  Volume 24, Issue 4, Page(s) 102364

    Abstract: 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. ... ...

    Abstract 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.
    Language English
    Publishing date 2021-03-26
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2021.102364
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.

    Santos-Pata, Diogo / Amil, Adrián F / Raikov, Ivan Georgiev / Rennó-Costa, César / Mura, Anna / Soltesz, Ivan / Verschure, Paul F M J

    Trends in cognitive sciences

    2021  Volume 25, Issue 7, Page(s) 582–595

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Hippocampus ; Humans ; Neural Networks, Computer ; Supervised Machine Learning
    Language English
    Publishing date 2021-04-24
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2010989-1
    ISSN 1879-307X ; 1364-6613
    ISSN (online) 1879-307X
    ISSN 1364-6613
    DOI 10.1016/j.tics.2021.03.016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans.

    Ahmad, Jumana / Ellis, Claire / Leech, Robert / Voytek, Bradley / Garces, Pilar / Jones, Emily / Buitelaar, Jan / Loth, Eva / Dos Santos, Francisco Páscoa / Amil, Adrián F / Verschure, Paul F M J / Murphy, Declan / McAlonan, Grainne

    Translational psychiatry

    2022  Volume 12, Issue 1, Page(s) 467

    Abstract: Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially ... ...

    Abstract Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.
    MeSH term(s) Humans ; Electroencephalography/methods ; Transcranial Magnetic Stimulation/methods ; Brain ; Schizophrenia/diagnostic imaging ; Magnetic Resonance Imaging ; Biomarkers ; Neural Inhibition/physiology
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-11-08
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2609311-X
    ISSN 2158-3188 ; 2158-3188
    ISSN (online) 2158-3188
    ISSN 2158-3188
    DOI 10.1038/s41398-022-02218-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Towards sample-efficient episodic control with DAC-ML

    Freire, Ismael T. / Amil, Adrián F. / Vouloutsi, Vasiliki / Verschure, Paul F. M. J.

    2020  

    Abstract: The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by ... ...

    Abstract The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task.

    Comment: 8 pages, 3 figures
    Keywords Computer Science - Artificial Intelligence ; Quantitative Biology - Neurons and Cognition ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-12-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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