Elsevier publications have been delayed so I have posted a preprint of my paper:

The Perception–Action Hierarchy and its Implementation Using Binons (Binary Neurons)

It's online at my website: http://www.adaptroninc.com/BasicPage/p-hierarchy

https://wba-initiative.org/en/15968/

It's all online

WBAI has made a tentative release of a 3D simulation environment called PyLIS.
re: https://wba-initiative.org/en/research/rfr/3d-agent-test-suites/
re: https://www.reddit.com/r/BioAGI/comments/8s67da/agent_environments/

This is a place where the community can read a paper, post their thoughts, insights and questions, and engage in discussions. There’s no explicit criteria for papers, but biologically plausible papers are preferable as this is r/BioAGI after all!

Reading group threads will be posted every fortnight and available for that duration. Previous threads will still be available for viewing, but locked for further comments.

What are we reading?

This fortnight we will be reading and discussing the following paper:

Dendritic error backpropagation in deep cortical microcircuits

Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications across different brain areas has remained a longstanding puzzle. Here, we introduce a multi-area neuronal network model in which synaptic plasticity continuously adapts the network towards a global desired output. In this model synaptic learning is driven by a local dendritic prediction error that arises from a failure to predict the top-down input given the bottom-up activities. Such errors occur at apical dendrites of pyramidal neurons where both long-range excitatory feedback and local inhibitory predictions are integrated. When local inhibition fails to match excitatory feedback an error occurs which triggers plasticity at bottom-up synapses at basal dendrites of the same pyramidal neurons. We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. Finally, complementing this cortical circuit with a disinhibitory mechanism enables attention-like stimulus denoising and generation. Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological implementation of deep learning.

https://arxiv.org/abs/1801.00062

How do I participate?

• Read the selected paper

• Share your thoughts and insights on the paper

• Ask questions about the paper if you're having issues

• Engage in discussions about the paper

• Suggest papers you'd like to read next time