We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Using an Octopus Brain To Understand Memory

An octopus.
Credit: Diane Picchiottino / Unsplash.
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 2 minutes


Prof. Hochner (Hebrew University) and Prof. Lichtman (Harvard) reveal Octopus vulgaris' neural intricacies for learning via advanced mapping. Unique interneuron types in a feedforward structure challenge traditional models, shedding light on cognitive mechanisms and memory networks.

Key takeaways

  • Octopus Learning Unveiled: Prof. Hochner and Prof. Lichtman's study uncovers the intricate neural architecture governing Octopus vulgaris' learning processes, providing insights into memory networks and cognitive functions.
  • Advanced Neural Mapping: Collaborating with Harvard, researchers used innovative tissue preparation and machine learning algorithms to construct a 3D representation of octopus brain structure. Findings challenge conventional neural network models.
  • Distinct Neural Design: Octopus's vertical lobe operates in a unique feedforward manner, with two types of interneurons—SAMs and CAMs—playing vital roles in learning and memory processes. Octopuses offer insights into cognitive mechanisms.

The neural architecture of Octopus vulgaris

New study by Prof. Benny Hochner from the Alexander Silberman Institute of Life Sciences at The Hebrew University and Prof. Jeff Lichtman from Harvard University has unveiled the intricate neural architecture governing the enigmatic learning processes of Octopus vulgaris. This research introduces a promising model for delving into memory networks, with implications for both cephalopods cognition, considered the most intelligent invertebrates, and broader insights into memory processes, including those in humans.

The octopus, separated from us by 700 million years of evolution, showcases cognitive abilities rivalling higher vertebrates. Prof. Hochner’s team at Hebrew University focused on the octopus’s central nervous system’s vertical lobe, crucial for learning and memory. They aimed to compare neural networks and mechanisms across species.

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE

Collaboration with Prof. Jeff Lichtman’s laboratory at Harvard University, leveraged innovative automated tissue preparation and new machine learning reconstruction algorithms. This cutting-edge technology enabled the cutting and sorting of ultra-thin sections, each a mere 30 millionths of a millimeter thick, constructing a three-dimensional representation of the structural elements comprising the network.

Prof. Hochner remarked, “Our previous studies revealed a fascinating phenomenon of long-term synaptic strengthening (Long-Term Potentiation – LTP) even within the vertical lobe of the octopus. This phenomenon, recognized as a universal synaptic process essential for learning and memory, caught our attention. We meticulously charted the connectivity of the vertical lobe using the precision of an electron microscope, achieving a resolution on the order of about 4 millionths of a millimeter. Together with Prof. Jeff Lichtman’s team at Harvard University, we engineered a robotic system alongside a sophisticated computational algorithm, uniquely capable of organizing hundreds of ultra-thin sections (each only 30 millionths of a millimeter thick) into a comprehensive 3D structure. This innovative approach enabled us to trace the connectome – namely the intricate synaptic connections among the neural elements composing the network.”

Under the leadership of postdoctoral researchers Dr. Flavie Bidel from Hebrew University and Dr. Yaron Meirovitch from Harvard University, a minute tissue volume representing the vertical lobe was meticulously reconstructed to unveil the connectome. Through the application of advanced machine learning algorithms and precise annotation, the researchers charted the wiring of the vertical lobe within octopus brains. This challenged established notions of neural network functionality in the context of learning and memory. Unlike typical models, the vertical lobe’s network operates in a feedforward configuration, like a one-way street, with information only from the input neurons to output neurons that control octopus behavior.

Central to this simplicity is the organizational structure of approximately 25 million interneurons, divided into two distinct groups: Simple Amacrine Cells (SAMs) and Complex Amacrine Cells (CAMs). The SAMs, numbering around 23 million, specialize in learning visual characteristics through synaptic reinforcement. In contrast, the CAMs, totaling approximately 400,000, play a pivotal role in consolidating activity levels.

The two types of cells send their axonal branches to connect with bigger cells in the output layer. Simple cells, that transmit “learned” information, make the big cells active, while complex cells make them less active, controlling how the brain works efficiently.

This evolutionary adaptation underscores the octopus’s unique cognitive prowess, contributing to our understanding of neural mechanisms important for cognitive functions. The research reveals Octopus vulgaris as an invaluable model organism for in-depth exploration of memory acquisition networks and opens doors to further unraveling the intricacies of cephalopod cognitive processes, enriching our understanding of memory across various species.

Reference: Bidel F, Meirovitch Y, Schalek RL, et al. Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network. eLife. 2023;12:e84257. doi: 10.7554/eLife.84257

This article has been republished from the following materials. Article summaries may have been generated by fact-checked AI models. Note: material may have been edited for length and content. For further information, please contact the cited source.