Aim
Neuroscience: Fundamental Principles to Computational Synapses builds a clear path from core neurobiology to computational models of neurons and synapses. Learn neural signaling, synaptic plasticity, circuit basics, and hands-on simulation workflows for modeling synaptic function.
Program Objectives
- Neuro Basics: neuron structure, membrane potential, action potentials.
- Synapses: chemical/electrical synapses, neurotransmitters, receptors.
- Plasticity: LTP/LTD concepts, learning rules, STDP basics.
- Circuits: excitation/inhibition balance, simple network motifs.
- Computation: integrate-and-fire models, rate coding, spike coding (intro).
- Simulation: build and test synapse models using code-based notebooks (workflow).
- Data View: spike trains, firing rates, basic neural data analysis.
- Capstone: model a synapse and show its learning behavior.
Program Structure
Module 1: Foundations of Neuroscience
- Neurons and glia: roles and organization.
- Membrane potential and ion channels (concepts).
- Action potentials and signal propagation.
- Basic measurement concepts: EEG, fMRI, electrophysiology (overview).
Module 2: Synaptic Transmission
- Chemical vs electrical synapses.
- Neurotransmitters and receptor types (overview).
- EPSPs/IPSPs and synaptic integration.
- Synaptic delays, short-term facilitation/depression (intro).
Module 3: Neural Coding (How Brains Represent Information)
- Rate coding vs spike timing concepts.
- Population coding (intro) and tuning curves.
- Noise, variability, and reliability.
- Intro to spike trains and inter-spike intervals.
Module 4: Plasticity and Learning Rules
- Hebbian learning concepts.
- LTP/LTD and synaptic strength changes.
- STDP basics and timing windows (conceptual + simulation).
- Homeostasis and stability (intro).
Module 5: Computational Neuron Models
- From biology to equations: why model neurons.
- Integrate-and-fire model and variants.
- Synaptic input current models (intro).
- Simulation basics: time step, numerical stability (intro).
Module 6: Computational Synapses
- Static synapse vs dynamic synapse models.
- Short-term plasticity models (intro).
- Long-term plasticity: rule-based synapse updates.
- Parameter tuning and sensitivity checks.
Module 7: From Synapses to Circuits
- Network motifs: feedforward, feedback, inhibition.
- E/I balance and oscillation concepts (overview).
- Simple recurrent network intuition (intro).
- Testing circuit behavior with simulations.
Module 8: Neural Data Analysis (Intro)
- Basic spike analysis: raster plots, PSTH concepts.
- Firing rate estimation and smoothing.
- Cross-correlation concepts (intro).
- Reporting results: plots, assumptions, limitations.
Final Project
- Build a computational synapse model (static + plastic version).
- Run simulations with different spike patterns and parameters.
- Deliverables: notebook + plots + short report explaining learning behavior.
Participant Eligibility
- Students and researchers in neuroscience, biology, biomedical engineering, or AI
- Basic math helpful; basic Python recommended for simulations
- Anyone interested in computational neuroscience fundamentals
Program Outcomes
- Understand neurons, synapses, and plasticity with a computation view.
- Build simple neuron and synapse simulations.
- Analyze basic spike train data and interpret outputs.
- Create a portfolio-ready computational synapse capstone.
Program Deliverables
- e-LMS Access: lessons, notebooks, datasets (sample).
- Toolkit: simulation templates, plotting utilities, report outline.
- Capstone Support: feedback and review.
- Assessment: certification after capstone submission.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Computational Neuroscience Trainee
- Neuroinformatics Research Assistant
- Biomedical Data Analyst (Neuro)
- AI Research Intern (Brain-Inspired Models)
Job Opportunities
- Research Labs/Universities: computational neuroscience and modeling projects.
- Neurotech Companies: signal analysis and modeling support roles.
- Healthcare R&D: neuro data analytics (research-focused).
- AI Labs: brain-inspired computing and simulation workflows.
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