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#Neuron Activation Function Tensorflow
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JEHow a Neuron in a Neural Network Works #machinelearning #artificialintelligence #animation #maths #animation #coding #programming #study #education #jeetechacademy #neuralnetworks #delhi #delhiuniversity
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ME🔬 Muscle Contraction Explained in 60 Seconds 💥
Every time you move—even blink—your muscles are contracting! But what really happens during a muscle contraction?
🧠 1. It all starts in the brain
A motor neuron sends an electrical signal (action potential) down to the muscle.
💥 2. Neuromuscular Junction (NMJ)
The signal reaches the synaptic terminal, triggering the release of acetylcholine (ACh) into the synaptic cleft.
⚡ 3. Muscle Activation
ACh binds to receptors on the muscle membrane, opening sodium channels → depolarization → signal spreads along the sarcolemma and into the T-tubules.
🧲 4. Calcium Release
This triggers the sarcoplasmic reticulum to release Ca²⁺, which binds to troponin, shifting tropomyosin and exposing binding sites on actin.
🔗 5. Cross-Bridge Cycle
Myosin heads bind to actin, pull, detach (with ATP), and repeat = contraction!
🛑 6. Relaxation
When the signal stops, calcium is reabsorbed, tropomyosin covers actin, and the muscle relaxes.
➡️ This process is called Excitation-Contraction Coupling!
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FAWhat Would Really Happen If We Used 100% of Our Brain?
We’ve all heard the myth that humans only use 10% of their brains—but what if we could use 100%? Would we become super-geniuses or unlock hidden powers? The truth might surprise you! In reality, our brains are already working at full capacity, just not all at once. Activating every neuron simultaneously would lead to sensory overload, chaos, and even severe seizures. It’s not about using more of your brain—it’s about using it efficiently!
If you enjoyed this mind-blowing fact, don’t forget to like, share, and subscribe for more surprising insights!
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The activation function in a neural network helps decide if a neuron should pass the data forward.
Each input to a neuron is multiplied by a weight and then added to a bias. This sum is then processed by the activation function, which turns the signal on or off in a non-linear way.
The activation function helps the neuron make decisions about the data, while the weights and bias affect how the activation function behaves, allowing the network to learn and understand complex patterns in the data.
Credit: 3Blue1Brown
#deeplearning #datascience #machinelearning #ai #coding
@unfoldedai
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STarduino uno chip -> neuron activation
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All credits to the owner. Thus is not my content. Dm for cred / removal.
#math #stats #cs #stem #mathmemes #engineer #engmemes #engineeringmemes #science #computerscience #robotics
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💡 Flip-Flop Comparison
The primary difference among D, T, and J-K flip-flops lies in the number of inputs and how those inputs affect the next state (output) of the flip-flop upon a clock pulse. The D flip-flop (Data or Delay) is the simplest, featuring only one data input, D. When the clock pulse arrives, the next output, Q_{next}, simply becomes the value of the D input at that instant. This behavior makes it ideal for use as a basic memory cell or in shift registers to store a single bit of data, as its characteristic equation is Q_{next} = D. In contrast, the T flip-flop (Toggle) also has a single input, T. If T=0, the output holds its current state (Q_{next} = Q_{current}), but if T=1, the output toggles (flips) to the opposite state (Q_{next} = \bar{Q}_{current}). The T flip-flop's unique toggle action makes it highly suitable for applications like frequency dividers and simple binary counters.
The J-K flip-flop is the most versatile of the three, as it features two inputs, J (Set) and K (Reset), and overcomes the "forbidden state" issue found in the simpler S-R flip-flop. Its behavior can mimic the other two types while offering more control. If J=0 and K=0, it holds the current state (like T=0). If J=1 and K=0, it sets the output to 1; if J=0 and K=1, it resets the output to 0 (similar to the basic S-R function). Critically, when both inputs are high (J=1 and K=1), the J-K flip-flop toggles the output, behaving identically to the T flip-flop. Therefore, a J-K flip-flop can be easily converted into a T flip-flop by connecting J and K together, or into a D flip-flop with the addition of a NOT gate between J and K. This flexibility means the J-K flip-flop is commonly used for complex counter circuits and other sequential logic designs where different control modes are requir
@statcsmemes
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SCThe intricate process of synaptic transmission involves several key steps:
1. Action Potential Arrival and Neurotransmitter Release:
An electrical signal, known as an action potential, travels down the axon of a neuron. Upon reaching the presynaptic terminal, it triggers the opening of voltage-gated calcium channels. Calcium ions flood into the terminal, causing synaptic vesicles filled with neurotransmitters to fuse with the presynaptic membrane. This fusion results in the release of neurotransmitters into the synaptic cleft, a narrow gap between the presynaptic and postsynaptic neurons.
2. Neurotransmitter Binding and Receptor Activation:
The released neurotransmitters diffuse across the synaptic cleft and bind to specific receptors on the postsynaptic membrane. These receptors can be ionotropic or metabotropic. Ionotropic receptors directly open ion channels, allowing ions like sodium, potassium, or chloride to flow into or out of the postsynaptic neuron. Metabotropic receptors, on the other hand, initiate a cascade of intracellular signaling events, leading to changes in the neuron’s electrical properties.
3. Postsynaptic Potential and Neural Integration:
The binding of neurotransmitters to receptors generates a postsynaptic potential (PSP), which can be either excitatory (EPSP) or inhibitory (IPSP). EPSPs depolarize the postsynaptic neuron, making it more likely to fire an action potential, while IPSPs hyperpolarize the neuron, making it less likely to fire. The neuron integrates the combined effects of multiple EPSPs and IPSPs to determine whether an action potential will be generated. If the net effect is excitatory and exceeds the neuron’s threshold, an action potential is triggered, and the signal is propagated to the next neuron.
Credit video : @facts_and_stuff_by_d
#science #sciencemm #biology #scientificresearch #medstudents #medical #scientificvisualization #scientist #brain #sinapse #videooftheday
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@science.mm_
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COThis is a single artificial neuron. A single artificial neuron learns patterns by combining inputs, weights, and activation to make decisions.
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Inputs are multiplied by weights and summed with a bias.
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The result passes through an activation function (like tanh).
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The final output is the neuron's response based on the input.
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Credit:@artificialintelligencenews.in
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GEA Multi-Layer Perceptron (MLP) is a type of neural network where each layer consists of interconnected “neurons,” which are represented by vectors.
Each neuron in a layer receives input from the previous layer, represented mathematically as a vector. The transformation from one layer to the next is achieved through matrix multiplication (matmul) and non-linear activation functions.
The input vector is multiplied by a weight matrix, then added to a bias vector, and finally passed through an activation function to introduce non-linearity.
C: @3blue1brown
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INNeural networks are machine learning models that consist of many layers of neurons, where each neuron processes multiple inputs and applies mathematical transformations to compute an output value.
When data is fed into the network, it passes through multiple layers, starting with an input layer, followed by hidden layers, then an output layer.
Mathematically, each neuron in a layer receives inputs, computes a weighted sum, applies an activation function, then passes the result to the next layer.
This lets the network capture and learn complex relationships in the data. They are able to approximate almost any function.
C: Emergent Garden
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@infusewithai
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MAWhen a neuron is activated, it sends an electrical signal down its axon, leading to the release of neurotransmitters into the synaptic gap.
These molecules then bind to receptors on the adjacent neuron, triggering a response that continues the signal transmission.
[ 📹 Small World In Motion ]
Over time, repeated communication can lead to the strengthening of these synaptic connections, a process known as synaptic plasticity.
This is crucial for learning, memory formation, and the brain’s ability to adapt to new information or recover from injury.
The formation of new synapses, or synaptogenesis, involves not just neurotransmitter release but also the physical growth and branching of neuronal structures.
This footage captures just one part of the complex and fascinating process by which our brains continuously rewire themselves in response to our experiences.
[ 📹 Small World In Motion ]
#connection #brain #mind #science
@maythesciencebewithyou
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AIA feed-forward neural network processes inputs through sequential layers (input → hidden → output).
Each neuron computes a weighted sum of its inputs, adds a bias, then applies an activation function (ReLU, sigmoid, etc.) to introduce non-linearity.
These transformed values are passed forward, layer by layer, with outputs from one layer becoming inputs for the next, producing predictions through mathematical transformations.
C: emergent garden
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