大脑记忆的建模

据国外媒体报道，科学家近日发现了大脑形成及失去记忆背后的数学方程。他们认为，这些方程可以精确地描述我们唤起回忆的方式。未来某一天，这一发现或许能帮助医生消除或改变病人脑海中与创伤事件有关的回忆。

瑞士洛桑联邦理工学院的科学家们研究了大脑是如何通过突触形成记忆的。突触具有很高的可塑性，因此神经元可以改变信息传递速度和密度，从而改变记忆。

由沃尔夫兰姆·格斯特纳(Wolfram Gerstner)带领的一支研究团队针对所谓的“记忆集合”的形成过程进行了研究。这指的是一组由神经元组成的网络，之间由突触相互连接，可以存储一部分特定的记忆。当人试图唤起某段回忆时，这些特定的记忆碎片就会组合在一起，形成完整的记忆。该研究团队的模拟过程显示，记忆形成和唤起的过程“就像交响乐队一样协调”。

根据其得出的结果，科学家们得到了一组复杂的算法，并称这是目前为止能够最精确地描述记忆形成过程的表示方法。

这一算法通过改良，可以用于研发新的科技，在大脑中激发新的记忆，或是完全抹去以前的记忆。

“如果我们能理解突触形成或解散记忆网络的方式，我们就能在人类认知方式或心理治疗等领域有新的进展。”格斯特纳说道。

此前，在今年三月的一项研究中，研究人员成功将有意识的记忆植入到熟睡的老鼠脑中。

科学家表示，同样的技术将来也可以用来改变人类记忆。对于那些不断在脑海中回想受伤经历的病人来说，这一技术可以大大帮助他们。

当人类或动物睡着时，大脑往往会对白天的经历进行回放，从而强化这段经历，或是记住新的经历。

巴黎高等物理化工学院的科学家成功运用大脑回放的原理，在熟睡的老鼠大脑中创造了新的记忆。

当老鼠在一个台面上四处探索时，研究人员通过电极对其脑细胞进行监控。并标明当老鼠来到特定区域时最为活跃的脑细胞。而当老鼠入睡之后，他们继续对老鼠的脑部活动进行监控。当之前标明的特定脑细胞变得活跃时，研究人员便使用电极刺激其大脑中与“奖励”相关的部分。

而老鼠醒来之后，他们便会匆匆前去能够得到奖励的地方。这说明科学家已经在它们脑中创造了新的记忆。

参考资料

1）记忆形成背后的数学方程：或可消除改变记忆（http://tech.sina.com.cn/d/f/2015-05-20/doc-icpkqeaz4838072.shtml）

2）Modeling memory in the brain（http://actu.epfl.ch/news/modeling-memory-in-the-brain/）

18.05.15 – Scientists at EPFL have uncovered mathematical equations behind the way the brain forms – and even loses – memories.

Memory is one of the most crucial elements of life. Without memory, there is no learning; without learning there is no invention, progress, or civilization. On the flipside, forgetting some experiences, especially traumatic ones, can help regain mental health and function. The key to all this is to understand how the brain forms memories in the first place, and then how it retains and recalls them. Scientists at EPFL have developed a mathematical model to describe how networks of neurons create memories. Published in Nature Communications, the model could clarify longstanding theories of memory formation, and could change the way we understand, simulate and even alter memory formation.

“Fire together, wire together”

Neurons form networks through specialized connections called “synapses”. The neuron sending a signal through a synapse is called “pre-synaptic”, and the one receiving it “post-synaptic”. Synapses show a lot of plasticity, allowing neurons to change their communication speed and intensity. Today, synaptic plasticity is considered the basis of how we learn and make memories.

The most prevalent theory of synaptic plasticity, named after neuroscientist Donald Hebb, states that a synapse becomes stronger when the pre-synaptic neuron fires repeatedly and stimulates the post-synaptic neuron to fire in sync. In such Hebbian synapses, “cells that fire together, wire together.” However, models of Hebbian plasticity fail to simulate memory formation accurately because they cannot account for external biological factors, and also because not all synapses are Hebbian.

A realistic model of memory A research team led by Wolfram Gerstner at EPFL has now developed a model of Hebbian plasticity that succeeds where previous ones have failed. The researchers focused on the formation of what are known as “memory assemblies”, which are networks of neurons, connected via synapses, which can store a particular segment of a memory. When a memory is being recalled, its particular assemblies piece it together to produce a whole.

The researchers used a third-generation neural network model called a “spiking neural network”(SNN). In an SNN, when a neuron fires a signal (a spike), it travels to other neurons, which respond accordingly by increasing or decreasing their own ability to fire a signal, thereby strengthening or weakening the connection.

Different synapses, different timescales

Gerstner’s team simulated hundreds of SNNs in order to explore different types of synapses and multiple forms of synaptic plasticity across different timescales. The simulations suggested that memory formation and recall actually follows a“well-orchestrated combination” of both Hebbian and non-Hebbian rules of synaptic plasticity. In other words, the formation of memory assemblies does not depend only on the signals coming from the pre-synaptic neuron but also on input from external neurons that indirectly modulate the strength of the synapse.

Time was also a crucial component of the simulation. Once a memory assembly had been formed, memories could be recalled even days after by selectively triggering the activity of specific neurons in the memory assembly. Overall, the scientists’ model suggests that the wide diversity of synaptic plasticity in the brain (Hebbian, non-Hebbian and even other types) is “orchestrated towards achieving common functional goals.”

From their results, the scientists were able to derive a complex algorithm that currently is the most accurate representation of this complex phenomenon. The algorithm can be adapted to help develop next-level simulations of memory formation and recall, which can advance our understanding of how the brain works as whole. In addition, the findings can inform strategies for addressing with traumatic memories or even improving educational efforts. “If we can understand how synapses work together to forge or dismantle memory networks, we can advance fields such as cognition and psychotherapy,”says Gerstner.

This study was funded by the European Community’s Seventh Framework Program under grant agreements with FACETS-ITN, BrainScales, and the Human Brain Project, as well as the European Research Council under a grant agreement with MultiRules. Additional funding came from the Brazilian agency CAPES through the Universidade Federal do Rio Grande do Sul (Brazil).

Reference

Zenke F, Agnes EJ, Gerstner W. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications 6:6922, 21 April 2015. DOI:10.1038/ncomms7922