BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.16//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.neuropac.info
X-WR-CALDESC:Events for 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250714
DTEND;VALUE=DATE:20250726
DTSTAMP:20260825T003718
CREATED:20250529T111246Z
LAST-MODIFIED:20250529T111246Z
UID:10000338-1752451200-1753487999@www.neuropac.info
SUMMARY:NeuroAI Live Online Course by Neuromatch
DESCRIPTION:Full time\, 2 Week\, Live Instruction Course \nWhat are common principles of natural and artificial intelligence? \nThe core challenge of intelligence is generalization. Neuroscience\, cognitive science\, and AI are all questing for principles that help generalization. Major system features that affect generalization include: task structure (multitasking\, multiple inputs with same output and vice versa)\, microcircuitry (nonlinearities\, canonical motifs and their operations\, sparsity)\, macrocircuitry or architecture (e.g. modules for memory\, information segregation\, weight sharing by input symmetry or common development)\, learning rules (synaptic plasticity\, modulation)\, and data stream (e.g. curriculum). \nWe aim to present current understanding of how these issues arise in both natural and artificial intelligence\, comparing how these system features affect representations\, computations\, and learning. We provide case studies and coding exercises that illustrate these issues in neuroscience\, cognitive science and AI. \n\nLearning Goal 1: A common understanding and vocabulary to describe challenges faced by naturally intelligent systems\n\nDescribe core shared concepts in neuroscience\, cognitive science and machine learning and how they differ to each other\nDescribe and implement different ways in which an ANN can be compared to a BNN\nDescribe multiple scales of computation\, and multiple scales of study (e.g. Marr’s levels\, what/how/why?)\n\n\nLearning Goal 2: Experience a multiplicity of approaches and interests at the intersection of neuro and AI; be able to describe some of these approaches and interests\nLearning Goal 3: Be able to practically implement NeuroAI models\n\nCoding and training models\nAdding more features to existing models\nDebugging (within guardrails)\nInterpreting\, analyzing and critiquing existing models\n\n\nLearning Goal 4: Complete research that deals with difficulties in NeuroAI\n\nWriting down a problem in a way that makes it tractable\nInteracting with other people from other disciplines fruitfully\nDo research (reading papers\, implementing previous SOTA\, coding new methods\, evaluating diff methods) in NeuroAI\nCommunicating their research in ways that are comprehensible to their target audience\n\n\n\nAll our content is open source\, you can see the NeuroAI course book here.
URL:https://www.neuropac.info/event/neuroai-live-online-course-by-neuromatch/
LOCATION:Online
CATEGORIES:School
END:VEVENT
END:VCALENDAR