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Evolution has shaped neural circuits in a very specific manner, slowly and aimlessly incorporating computational innovations that increased the chances to survive and reproduce of the newly born species.

The discoveries done by the Evolutionary Developmental (Evo-Devo) biology field during the last decades have been crucial for our understanding of the gradual emergence of such innovations. In turn, Computational Neuroscience practitioners modeling the brain are becoming increasingly aware of the need to build models that incorporate these innovations to replicate the computational strategies used by the brain to solve a given task.

The goal of this workshop is to bring together experts from Systems and Computational Neuroscience, Machine Learning and the Evo-Devo field to discuss if and how knowing the evolutionary history of neural circuits can help us understand the way the brain works, as well as the relative importance of learned VS innate neural mechanisms.

Schedule (Eastern Time)

8-8.10 Intro

8.10-8.35 Pamela Lyon What basal cognition can do for you (neuroscientists) (watch talk in youtube)

8.35-9.00 Luis Puelles Brain evolution crosses an ontogenetic 'ant's country' that escapes analysis (watch talk in youtube)

9.00-9.25 Paul Cisek The evolution of the human brain, long before humans (watch talk in youtube)

9.25-9.50 Robert Yang Evolving the Olfactory System with Machine Learning (watch talk in youtube)

09.50-10.20 Discussion

10.20-10.40 Break

10.40-10.50 Intro

10.50-11.15 Linda Wilbrecht Considering contextual factors at acute and evolutionary timescales in the study of learning and decision making (watch talk in youtube)

11.15-11.40 Ida Momennejad Evolving neural architectures: Homeostatic and ecological meta-learning (watch talk in youtube)

11.40-12.05 Dayu Lin Neural mechanisms of aggression (watch talk in youtube)

12.05-12.30 Tony Zador Learning through the Genomic Bottleneck (watch talk in youtube)

12.30-13.00 Discussion

Relevant references

Lyon, P., Keijzer, F., Arendt, D., & Levin, M. (2021). Reframing cognition: getting down to biological basics.

Puelles, L., Harrison, M., Paxinos, G., & Watson, C. (2013). A developmental ontology for the mammalian brain based on the prosomeric model. Trends in neurosciences, 36(10), 570-578.

Cisek, P. (2019). Resynthesizing behavior through phylogenetic refinement. Attention, Perception, & Psychophysics, 81(7), 2265-2287.

Wang, P. Y., Sun, Y., Axel, R., Abbott, L. F., & Yang, G. R. (2021). Evolving the Olfactory System with Machine Learning. bioRxiv.

Lin, W. C., Delevich, K., & Wilbrecht, L. (2020). A role for adaptive developmental plasticity in learning and decision making. Current Opinion in Behavioral Sciences, 36, 48-54.

Momennejad, I. (2020). Learning structures: Predictive representations, replay, and generalization. Current Opinion in Behavioral Sciences, 32, 155-166.

Wei, D., Talwar, V., & Lin, D. (2021). Neural circuits of social behaviors: innate yet flexible. Neuron.

Zador, A. M. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature communications, 10(1), 1-7.

Speakers

Organizers

For more info contact with: evolvingneuralnetworks@gmail.com