AIMC Topic: Neurosciences

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From lazy to rich to exclusive task representations in neural networks and neural codes.

Current opinion in neurobiology
Neural circuits-both in the brain and in "artificial" neural network models-learn to solve a remarkable variety of tasks, and there is a great current opportunity to use neural networks as models for brain function. Key to this endeavor is the abilit...

Hybrid Life: Integrating biological, artificial, and cognitive systems.

Wiley interdisciplinary reviews. Cognitive science
Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural, and computational sciences. Artificial life aims to foster a comprehensive study of life bey...

The neuromechanics of animal locomotion: From biology to robotics and back.

Science robotics
Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating...

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research.

Bioinspiration & biomimetics
Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevan...

Catalyzing next-generation Artificial Intelligence through NeuroAI.

Nature communications
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which c...

Emulating future neurotechnology using magic.

Consciousness and cognition
Recent developments in neuroscience and artificial intelligence have allowed machines to decode mental processes with growing accuracy. Neuroethicists have speculated that perfecting these technologies may result in reactions ranging from an invasion...

Data-driven emergence of convolutional structure in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their input...

Codimension-2 parameter space structure of continuous-time recurrent neural networks.

Biological cybernetics
If we are ever to move beyond the study of isolated special cases in theoretical neuroscience, we need to develop more general theories of neural circuits over a given neural model. The present paper considers this challenge in the context of continu...

Addressing neuroethics issues in practice: Lessons learnt by tech companies in AI ethics.

Neuron
Neurotechnologies raise ethical concerns overlapping with those of other technologies, like artificial intelligence (AI). We discuss how to leverage the experience and lessons learnt by tech companies addressing AI ethics issues to accelerate going f...