پاسخ Gemini - قسمت 2

Ten Neural Principles for Aliens to Create a Brain-Like System

1. Modularity: The brain is composed of specialized modules that perform specific functions, such as vision, hearing, and motor control. These modules can operate independently but also communicate with each other to create complex behaviors.

2. Hierarchy: The brain has a hierarchical organization, with higher-level modules controlling and coordinating the activities of lower-level modules. This allows for efficient processing of information and decision-making.

3. Plasticity: The brain is highly plastic, meaning that it can change and adapt in response to new experiences and learning. This allows the brain to learn new skills and knowledge throughout life.

4. Connectivity: The brain is densely interconnected, with billions of neurons communicating with each other through synapses. This connectivity allows for the rapid and efficient transmission of information across the brain.

5. Feedback: The brain uses feedback loops to regulate its own activity and to learn from its mistakes. This allows the brain to constantly adjust and improve its performance.

6. Redundancy: The brain has built-in redundancy, with multiple pathways and mechanisms for performing the same function. This redundancy helps to ensure that the brain can continue to function even if some parts are damaged.

7. Energy efficiency: The brain is remarkably energy efficient, consuming only about 20 watts of power. This is due to the use of specialized neurons and circuits that minimize energy consumption.

8. Parallel processing: The brain can process information in parallel, allowing it to perform multiple tasks simultaneously. This parallel processing capability is essential for the brain's ability to handle the vast amount of information it receives.

9. Self-organization: The brain has the ability to self-organize, meaning that it can create new connections and pathways without external instruction. This self-organization allows the brain to adapt to changing circumstances and to learn new things.

10. Emergence: The brain's complex behaviors and functions emerge from the interactions of its individual components. These emergent properties cannot be predicted from the behavior of the individual components alone.

#NeuralPrinciples #Part4 #gemini

جلسه پنجم معادلات دیفرانسیل و سامانه های پویا

Potentials vs. Phase Portrait
Solving nonhomogeneous ODEs using the method of undetermined coefficients
Example: Modeling a single neuron
* Integrate and fire model
* Solving, plotting, and interpretations


#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه پنجم معادلات دیفرانسیل و سامانه های پویا

Potentials vs. Phase Portrait
Solving nonhomogeneous ODEs using the method of undetermined coefficients
Example: Modeling a single neuron
* Integrate and fire model
* Solving, plotting, and interpretations


#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه ششم معادلات دیفرانسیل و سامانه های پویا


A primer on integrals

Solving nonhomogenous ODEs using the method of variation of parameters

Analyzing the toggle switch model

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه ششم معادلات دیفرانسیل و سامانه های پویا


A primer on integrals

Solving nonhomogenous ODEs using the method of variation of parameters

Analyzing the toggle switch model

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه هفتم معادلات دیفرانسیل و سامانه های پویا - قسمت اول


Integration as the area under the curve
Integration as the antiderivative
Fundamental Theorem of Calculus
Elementary functions
Closed-form and analytical solutions
Numerical Integration
Forward and Backward Euler's formula for integration



#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه هفتم معادلات دیفرانسیل و سامانه های پویا - قسمت دوم

Integration as the area under the curve
Integration as the antiderivative
Fundamental Theorem of Calculus
Elementary functions
Closed-form and analytical solutions
Numerical Integration
Forward and Backward Euler's formula for integration



#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

اسلاید های جلسه هفتم معادلات دیفرانسیل و سامانه های پویا

Integration as the area under the curve
Integration as the antiderivative
Fundamental Theorem of Calculus
Elementary functions
Closed-form and analytical solutions
Numerical Integration
Forward and Backward Euler's formula for integration



#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه هشتم معادلات دیفرانسیل و سامانه های پویا

Numerical Simulation of dynamical systems
More detailed analysis of forward and backward Euler's scheme
Difficulties of Backward Euler's integrator
2nd and 4th Runge Kutta Integrators
A short primer on graph theory and graph visualization
Diffusion/Signal Propagation in complex/timed networks

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

اسلاید های جلسه هشتم معادلات دیفرانسیل و سامانه های پویا

Numerical Simulation of dynamical systems
More detailed analysis of forward and backward Euler's scheme
Difficulties of Backward Euler's integrator
2nd and 4th Runge Kutta Integrators
A short primer on graph theory and graph visualization
Diffusion/Signal Propagation in complex/timed networks

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه نهم معادلات دیفرانسیل و سامانه های پویا

Dynamical Processes on Complex Networks
Oscillators
Synchronization
Kuramoto model of coupled oscillators
The impact of network topology on the tendency of the networks to globally synchronize
- KNN networks
- Random Networks - Erdős-Rényi
Partial Synchronization, Chimera States
- Networks with subpopulations (communities)
Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators


#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

اسلاید های جلسه نهم معادلات دیفرانسیل و سامانه های پویا

Dynamical Processes on Complex Networks
Oscillators
Synchronization
Kuramoto model of coupled oscillators
The impact of network topology on the tendency of the networks to globally synchronize
- KNN networks
- Random Networks - Erdős-Rényi
Partial Synchronization, Chimera States
- Networks with subpopulations (communities)
Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators


#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

https://www.complexity-explorables.org/

این وبسایت مجموعه بسیاری جالبی از شبیه سازی سیستم های پیچیده مختلف هست.

#neurosyntax

در ارتباط با این جلسه دو سخنرانی زیر از Steve Strogatz و Eckehard Schöll رو پیشنهاد میکنم که بسیار جالب هستن.

Eckehard Schöll: Partial synchronization patterns in brain networks

اکهارت شول درباره شرایط ایجاد کیمرا در شبکه های مختلف بحث میکنه با این تفاوت که بجای استفاده از مدل دینامیکی کیوراموتو از مدل نورونی فیگزهیو-ناگومو استفاده میکنه و درباره شبکه های واقعی مغز هم که از دیتاهای MRI ساختاری بدست آمده نتایجی رو ارائه میکنه.

Steven Strogatz: Global Synchronization: New Theorems, New Puzzles

استیو استروگتز در این سخنرانی درباره بسیاری از مسائلی که از مقالاتش سایت کردم صحبت میکنه + درباره مدلسازی بخشی که گفتم هنوز دربارش چیزی نمیدونیم. ویدیوهای دیگری هم در یوتیوب داره اگر با همین عناوین جستجو کنید.

Complex Systems, Dynamics, Control

پلی لیست جدید سیستم های پیچیده، دینامیک و کنترل رو در یوتیوب ایجاد کردم و به زودی تمام 9 جلسه در یوتیوب هم قرار خواهد گرفت + تمام جلساتی که در آینده ضبط بشه.

#neurosyntax

نخستین جلسه کارگاه متلب مجموعه معادلات دیفرانسیل و سامانه های پویا

Solve ODEs in MATLAB: dSolve, Syms, Subs
Numerical simulation of ODEs using ODE45 and ODE23
Forward Euler's scheme
Plotting Phase Portrait of dynamical systems
Eigendecomposition

Simulating Kuramoto Model
Creating different adjacency matrices and graphs:
Complete Graphs,
KNN graphs,
Small world Networks,
Erdős–Rényi random graphs

Simulating an adaptive network with Kuramoto Dynamics: REF

All codes will be shared on the channel.

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

جلسه دهم معادلات دیفرانسیل و سامانه های پویا

Fourier Series
Fourier Transforms
Inverse Fourier
Fourier Transform and derivatives
Laplace Transform
Inverse Laplace
Laplace Transform properties: Linearity, Existence, Uniqueness

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl

اسلاید های جلسه دهم معادلات دیفرانسیل و سامانه های پویا

Fourier Series
Fourier Transforms
Inverse Fourier
Fourier Transform and derivatives
Laplace Transform
Inverse Laplace
Laplace Transform properties: Linearity, Existence, Uniqueness

#CODAC
COmplex Dynamics And Control

#neurosyntax

#ComplexityDynamicsControl