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Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

SN1 Reaction: Mechanism

S_N1 Reaction: Mechanism

Kinetic studies of ionization of a tertiary halide in a protic solvent suggest that only the substrate participates in the rate-determining step (slow step). The nucleophile is involved only after the slowest step. The SN1 reaction takes place in a multiple-step mechanism.
Firstly, the haloalkane ionizes to generate a carbocation intermediate and a halide ion. This heterolytic cleavage is highly endothermic with large activation energy. The ionization of the substrate, facilitated by a polar...

SN1 Reaction: Stereochemistry

S_N1 Reaction: Stereochemistry

This lesson provides an in-depth discussion of the stereochemical outcomes in an SN1 reaction.
In the first step of an SN1 reaction, the bond between the electrophilic carbon and the leaving group ionizes to generate the carbocation intermediate. The second step of the mechanism is the nucleophilic attack.
In the formed carbocation, the positively charged carbon is sp2 hybridized with a trigonal planar geometry. As all the three substituents lie on the same plane, a plane of symmetry for the...

SN2 Reaction: Mechanism

S_N2 Reaction: Mechanism

The kinetic studies of SN2 reactions suggest an essential feature of its mechanism: it is a single-step process without intermediates. Here, both the nucleophile and the substrate participate in the rate-determining step.
The presence of the more electronegative halogen in the substrate creates a polarized carbon-halide bond. The halide pulls the electron cloud generating an electrophilic center at the carbon atom. Thus, the carbon atom carries a partial positive charge while the halide has a...

SN2 Reaction: Stereochemistry

S_N2 Reaction: Stereochemistry

In an SN2 reaction, the nucleophilic attack on the substrate and departure of the leaving group occurs simultaneously through a transition state. As the nucleophile approaches the substrate from the back-side, the configuration of the substrate carbon changes from tetrahedral to trigonal bipyramidal and then back to tetrahedral, leading to an inversion in the configuration of the product.
If the substrate is an achiral molecule at the α-carbon, the inversion of configuration is not observed.

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Related Experiment Video

Updated: Jun 14, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

SD²-SNN: Self-distillation and structural decomposition framework for SNNs in continual learning.

Zhenhao Xie¹, Xia Xiao¹, Hongsheng Zhang²

¹School of Microelectronics, Tianjin University, Tianjin, 300072, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 12, 2026

Summary

This study introduces SD²-SNN, a novel framework for Spiking Neural Networks (SNNs) that combats catastrophic forgetting in continual learning. It enhances knowledge retention without external supervision, offering an energy-efficient solution for AI systems.

Keywords:

Continual learning Self-distillation Sparse encoding Spiking neural networks Structural weight decomposition

Related Experiment Videos

Last Updated: Jun 14, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Catastrophic forgetting is a major challenge in Artificial Neural Networks (ANNs) for continual learning.
Existing ANN methods for continual learning often increase computational overhead.
Spiking Neural Networks (SNNs) offer energy efficiency but lack unified continual learning mechanisms.

Purpose of the Study:

To propose SD²-SNN, a framework for SNNs to mitigate catastrophic forgetting without external supervision.
To enhance knowledge retention in SNNs for long sequences of tasks.
To balance plasticity and stability in SNNs for energy-efficient continual learning.

Main Methods:

Implemented a Self-Distillation mechanism to anchor decision boundaries by aligning spike-rate distributions.
Employed Structural Decomposition to create stable shared and dynamic task-specific parameters.
Leveraged inherent SNN sparsity for efficient computation.

Main Results:

SD²-SNN achieved strong and stable performance on image-based and event-based continual learning benchmarks.
Demonstrated high accuracy on Split-CIFAR100, Tiny-ImageNet, and DVS128 Gesture datasets.
Effectively balanced plasticity and stability, outperforming existing methods.

Conclusions:

SD²-SNN offers an effective and energy-efficient solution for continual learning in SNNs.
The proposed framework successfully mitigates catastrophic forgetting without external supervision.
SD²-SNN paves the way for more robust and sustainable AI systems.