Невродинамика

Човешкият мозък е съвършена самонастройваща се система ,

Която се самообучава , за да преведе индивида през всички препятствия

И да го съхрани . Създадените модели на поведение показват при какви дразнения , как реагира нашият организъм.

Невроните с невронните връзки приличат на интернет.

Лекции на Вулфрам Герстнер

https://neuronaldynamics.epfl.ch/

From single neurons to networks and models of cognition

Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski

What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience.

CNS1.1 - Neurons and synapses: Overview

https://www.youtube.com/watch?v=f_9UE5P3KCo
 

• Lecture 1. A first simple neuron model (83 min)
  Part 1 - Neurons and Synapses : Overview (10 min)
  Part 2 - The Passive Membrane (21 min)
  Math detour - Linear differential equation (22 min)
  Part 3 - Leaky Integrate-and-Fire Model (8 min)
  Part 4 - Generalized Integrate and Fire Models (17 min)
  Part 5 - Quality of Integrate-and-Fire Models (5 min)
• Lecture 2. The Hodgkin-Huxley model and detailed ion-current based neuron models (77 min)
  Part 1 - Biophysics of neurons (5 min)
  Part 2 - Reversal potential and Nernst equation (11 min)
  Part 3 - Hodgkin-Huxley Model (23 min)
  Part 4 - Threshold in the Hodgkin Huxley Model (26 min)
  Part 5 - Detailed Biophysical Models (12 min)
• Lecture 3. Synapses, dendrites and the cable equation (69 min)
  Part 1 - Synapses (15 min)
  Part 2 - Synaptic short term plasticity (9 min)
  Part 3a - Dendrite as a Cable (11 min)
  Part 3b - Derivation of the Cable Equation (10 min)
  Part 4 - Cable equation (10 min)
  Part 5 - Compartmental Models (14 min)
• Lecture 4: Two-dimensional models and phase plane analysis (165 min)
  Part 1 - From Hodgkin Huxley to 2D (18 min)
  Math detour 1 - Separation of time scales (11 min)
  Math detour 2 - Exploiting similarities (16 min)
  Part 2 - Phase Plane Analysis (17 min)
  Part 3a - Analysis of a 2D neuron model - pulse input (12 min)
  Part 3b - Analysis of a 2D neuron model - constant input (9 min)
  Math detour 3 - Stability of fixed points (19 min)
  Part 4a - Type I and Type II Neuron Models (16 min)
  Part 4b - Firing threshold in 2D models (21 min)
  Part 5 - Nonlinear Integrate-and-Fire Model (16 min)

II. Generalized Integrate-and-Fire Neurons

• Lecture 5. Variability of spike trains (96 min)
  Part 1 - Variability of spike trains (6 min)
  Part 2 - Sources of Variability? (10 min)
  Part 3a - Three definitions of rate code (12 min)
  Part 3b - Poisson Model, survivor function, and interval distribution (15 min)
  Math detour - Poisson Process - A modern approach (20 min)
  Part 4a - Stochastic spike arrival (15 min)
  Part 4b - Membrane potential fluctuations (13 min)
  Part 5 - Stochastic spike firing in integrate and fire models (5 min)