Grace Lindsay | Discrete Symbols vs Continuous Neurons | NUMEROUS NUMEROSITY 2021

Plenary session kindly contributed by Grace Lindsay in SEMF's 2021 Numerous Numerosity: https://semf.org.es/numerosity/

SESSION ABSTRACT
Artificial neural networks are becoming increasingly popular models of how the brain processes information. They've been shown capable of playing games at human level, predicting neural activity in response to real-world images, and capturing basic dynamics of decision making. In the majority of these networks, individual neurons can take any non-negative real value. Yet in the history of cognitive science, discrete and symbolic processing has been highlighted as a way of describing the mind. I will compare and contrast these views, explaining why continuous values are necessary for neural networks and how people are aiming to connect these two seemingly disparate approaches. Questions will include: What counts as a symbol? How can symbolic/discrete processing arise from continuous neurons? Will different or hybrid structures ultimately be needed to model the mind/brain? Do we need to reconcile these views or is the success of neural networks enough to support them as models on their own?

SESSION MATERIALS
· Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3056446)
· Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (https://arxiv.org/abs/2001.07092)
· On the Binding Problem in Artificial Neural Networks (https://arxiv.org/abs/2012.05208)
· Symbolic Behaviour in Artificial Intelligence (https://arxiv.org/abs/2102.03406)

GRACE LINDSAY
Center for Theoretical Neuroscience (Columbia University): https://ctn.zuckermaninstitute.columbia.edu/
Personal website: https://gracewlindsay.com/
Twitter: https://twitter.com/neurograce?lang=es
LinkedIn: https://www.linkedin.com/in/grace-lindsay-40454126/?originalSubdomain=uk

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