Table of contents

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Introduction

• Artificial Intelligence needs a new momentum. Why not look at the brain?

Brains and cognitive abilities

• The primary function of a brain is not to think but to efficiently control complex behavior

• This control is supported by abilities that were progressively acquired and refined through evolution

• Biological intelligence gradually emerged with active perception and cognition

Brain general machinery

• Neurons are sophisticated elementary components of the neural “hardware”

• Neuron plasticity allows to retain memories of previous neural activity

• Interconnected brain structures group neurons into organized network architectures

• Brain activity continuously loops across those structures through parallel pathways

Focus on the neocortex

• The neocortex is divided into hundreds of functionally specialized but anatomically similar cortical areas

• Cortical areas receive and send information in a laminar-specific way

• A majority of long-distance projecting pyramidal neurons cohabits with a minority of local inhibitory cells

• Functional neocortical circuits rely on laminar-specific lateral and radial interactions

• Sensory stimuli, motor actions and spatial navigation offer a window into the cortical code

• The dynamics of cortical activity can only be analyzed in relation to brain oscillations

Back to code

• Next-level artificial neural networks model more realistic neurons, architectures and learning rules

• The transition from artificial networks to artificial agents is a necessary step towards machine intelligence

• The potential emergence of machine intelligence already raises existential questions

Conclusion

• The road towards machine intelligence is inseparable from a mixed AI & neuroscience approach