记忆依赖计算和学习在尖端神经网络通过赫比安可塑性.
IEEE transactions on neural networks and learning systems
|December 19, 2023
概括
赫比可塑性通过启用记忆功能来增强尖端神经网络 (SNN). 这种增强记忆的SNN架构增强了神经形态系统的概括,学习和认知能力.
科学领域:
- 神经形态工程的神经形态工程
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 尖端神经网络 (SNN) 对于节能神经形态硬件至关重要,但缺乏生物记忆能力.
- 生物记忆,对于长期信息保留至关重要,与赫比恩可塑性有关,但其在SNN中的作用尚未得到充分研究.
- 与生物系统相比,当前的人工SNN经常在概括和复杂的认知任务方面扎.
研究的目的:
- 提出赫比安可塑性作为生物和人工神经系统中计算的基础.
- 引入一种新的增强内存的SNN架构,结合了Hebbian突触可塑性.
- 为了证明SNNs的增强的计算和学习能力,丰富了赫比亚的可塑性.
主要方法:
- 开发了一个新的SNN架构,集成内存组件.
- 通过Hebbian突触可塑性机制丰富了SNN架构.
- 评估了记忆增强SNN在各种认知任务中的表现.
主要成果:
- 用赫比亚语丰富的SNN显示了计算和学习能力的显著改善.
- 在分布外概括,一次性学习和跨模式关联方面观察到更好的表现.
- 该架构显示了语言处理和基于奖励的学习任务的改进能力.
结论:
- 赫比亚语的突触可塑性是提高SNN能力的基本原则.
- 具有Hebbian可塑性的增强记忆的SNN提供了增强的多功能性和认知功能.
- 这种方法为构建强大的认知神经形态系统提供了一条途径.
更多相关视频
14:27Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
Published on: August 11, 2019
12.6K
11:31Ex Vivo Optogenetic Interrogation of Long-Range Synaptic Transmission and Plasticity from Medial Prefrontal Cortex to Lateral Entorhinal Cortex
Published on: February 25, 2022
2.4K
相关概念视频
Long-term Potentiation
2.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
Hebbian LTP
LTP can occur when...
2.8K
Neuroplasticity
367
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
367
The Role of Ion Channels in Neuronal Computation
3.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.2K
Higher Mental Functions of Brain: Learning and Memory
819
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
819
Long-term Depression
2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
Calcium Ion Concentration Mechanism
If over...
2.5K
Storage
86
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
86
