Prediction in cultured cortical neural networks

Abstract

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short- and long-term memory. To answer these questions, we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long-term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20 h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 31, 2023
Source ID
10.1093/pnasnexus/pgad188

Entities

People

  • Joost Le Feber
  • Martina Lamberti
  • Michel J. Van Putten
  • Sarah Marzen
  • Shiven Tripathi

Organizations

  • Air Force Office of Scientific Research
  • Indian Institutes of Technology
  • Pitzer College
  • University of Twente

Tags

Fields of Study

  • Biology
  • Psychology

Readers

  • Neural Network Machine Learning.
  • Neuroscience
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks