Table of contents
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Introduction
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Artificial Intelligence needs a new momentum. Why not look at the brain?
Brains and cognitive abilities
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The primary function of a brain is not to think but to efficiently control complex behavior
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This control is supported by abilities that were progressively acquired and refined through evolution
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Biological intelligence gradually emerged with active perception and cognition
Brain general machinery
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Neurons are sophisticated elementary components of the neural “hardware”
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Neuron plasticity allows to retain memories of previous neural activity
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Interconnected brain structures group neurons into organized network architectures
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Brain activity continuously loops across those structures through parallel pathways
Focus on the neocortex
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The neocortex is divided into hundreds of functionally specialized but anatomically similar cortical areas
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Cortical areas receive and send information in a laminar-specific way
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A majority of long-distance projecting pyramidal neurons cohabits with a minority of local inhibitory cells
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Functional neocortical circuits rely on laminar-specific lateral and radial interactions
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Sensory stimuli, motor actions and spatial navigation offer a window into the cortical code
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The dynamics of cortical activity can only be analyzed in relation to brain oscillations
Back to code
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Next-level artificial neural networks model more realistic neurons, architectures and learning rules
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The transition from artificial networks to artificial agents is a necessary step towards machine intelligence
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The potential emergence of machine intelligence already raises existential questions
Conclusion
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The road towards machine intelligence is inseparable from a mixed AI & neuroscience approach