Reverse Engineering the Thought: The Computational Architecture of the Mind

What if AI could help us understand how the human brain represents meaning, perception, and imagination? Join us for an insightful session exploring the intersection of neuroscience, artificial intelligence, and deep neural networks, and how computational models can help decode patterns of brain activity. Title: Reverse Engineering the Thought: The Computational Architecture of the Mind Date: Friday, October 23, 2026 Time: 5:00 PM \- 6:00 PM IST \| 1:30 PM \- 2:30 PM CEST \| 7:30 AM \- 8:30 AM EDT Location: Online \| Register Here What You’ll Learn The Brain as a Neural Network: How biological and artificial neural networks compare, and what fMRI can actually measure. From Meaning to Brain Activity: How models based on word meanings can predict patterns of brain activity. Meaning Across the Cortex: How computational models help map semantic information across different areas of the brain. Decoding the Brain: How deep neural networks can identify and reconstruct what someone may be seeing or imagining from brain activity. Who Should Attend AI, Machine Learning, and Deep Learning professionals. Software Engineers, Solution Architects, Data Scientists, and Researchers. Students and professionals interested in neuroscience, cognition, and AI. Speaker Mauro Antonio de Palma \| Lead Software Engineer & Cloud Architect\, Compugroup Medical SE & Co\. KGaA Mauro Antonio de Palma is a Staff Engineer and Solution Architect with 9+ years of experience across cloud-native platforms, distributed systems, microservices, and AI/agentic workflows. His work includes enterprise architecture, technology modernization, AI-powered DevOps workflows, and large-scale production systems. He is currently pursuing an MSc in Artificial Intelligence at the University of Trento, where his interest in computational neuroscience inspired this session. Don’t Miss Out This is not a traditional "how-to" session. It is an exploration of how deep neural networks and computational models can