Young Computational
Neuroscientist Program
Can you solve a mystery of the brain?
A 12-week guided computational research mentorship where school students learn neuroscience, build a working model, run experiments and present real research.
Guided Computational Research Mentorship · Applications close August 31, 2026
Your 12-Week Research Journey
Feedforward networks · Attractor networks · Reinforcement learning
Learn
Neuroscience, math and Python
Build
Create a working computational model
Investigate
Run experiments and interpret results
Present
Share your research at the symposium
Program Stages
- Foundations of neuroscience
- Mathematics as a language for brain models
- Programming with Python
- Feedforward Networks
- Attractor Systems
- Reinforcement Learning
- Guided research training sessions
- Build, run and analyse your own model
- Present at the research symposium
Week-by-Week Curriculum
The Brain as a Scientific Mystery
Mathematics as a Language for Brain Models
Python for Brain Modeling
Neurons and Neural Signaling
Feedforward Neural Networks
Attractor and Recurrent Neural Networks
Reinforcement Learning
Introduction to the Research Question and Model Design
Building the Baseline Model
Running Computational Experiments
Analysis and Neuroscientific Experiments
Research Communication and Symposium
Choose From Six Brain Mysteries
In Stage 03, participants are divided into six teams — two per computational approach. Each team investigates a genuine open question in neuroscience.
Research Question
How can a stationary image produce neural signals that are interpreted as movement?
Central Hypothesis
Particular combinations of contrast, spatial filtering and temporal response differences can produce motion-like activity even when the image is stationary.
Research Question
How can face detection remain intact while face identity recognition fails?
Central Hypothesis
Prosopagnosia can result from disruption of higher-level identity representations or their readout, even when early visual processing and face detection remain functional.
Research Question
Why are some stored memories more vulnerable than others?
Central Hypothesis
Memories with weaker, less distributed or more overlapping representations may have shallower attractor basins and therefore disappear earlier as the network is damaged.
Research Question
How can a temporary emotional change become a persistent mood state?
Central Hypothesis
Strong recurrent feedback can deepen a mood attractor so that the network remains in that state after the original trigger has disappeared.
Research Question
Why might a narrow doorway cause an interruption of otherwise successful walking?
Central Hypothesis
At a spatial bottleneck, action alternatives may become closely matched or the local value gradient may become weak or ambiguous. Reduced dopaminergic action selection could then produce freezing.
Research Question
Could apparently inconsistent behaviour arise from an agent that explores too much?
Central Hypothesis
Excessive exploration can reduce consistent task performance while sometimes improving discovery and adaptation in changing environments.
Expected Outcomes
Covering neuroscience, mathematics, Python and computational modelling
Build and investigate a real computational model of a brain phenomenon
Work alongside mentors through every stage of the research process
Communicate your findings and present your research at the symposium
Important Dates
Application Deadline
August 31, 2026
Registration Deadline
September 15, 2026
Classes Begin
September 21, 2026
Fees & Discounts
For other details including pricing, please write to workshops@neurogati.com.
A selected set of meritorious students will be offered discounts.
Ready to Solve a Brain Mystery?
Join a cohort of curious students in Classes 9–12 and spend 12 weeks doing genuine computational neuroscience research — from first principles to a symposium presentation.
Application Deadline
August 31, 2026
Registration Deadline
September 15, 2026
Classes Begin
September 21, 2026
Questions?
workshops@neurogati.com