Distributed fading memory for stimulus properties in the primary visual cortex.
Distributed fading memory for stimulus properties in the primary visual cortex.
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DOI：
10.1371/journal.pbio.1000260
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发表时间：
2009-12
期刊：
影响因子：
9.8
通讯作者：
Maass W
中科院分区：
文献类型：
--
作者：
Nikolić D;Häusler S;Singer W;Maass W
大脑对视觉刺激有一对一的记忆。对一幅图像的神经反应包含的关于当前图像的信息，与它对之前呈现的另一幅图像的信息一样多。目前尚不清楚早期视觉区域的分布式神经元反应如何携带与刺激相关的信息。我们从猫的初级视皮层进行了多电极记录，并应用机器学习的方法来分析大约100个神经元组成的大型集合中刺激相关信息的时间演变。我们使用了最多三个不同视觉刺激(字母表的字母)的序列，呈现时间为100ms，间隔为100ms或更长。通过复杂的机器学习方法，即具有非线性核函数的支持向量机，可以提取关于视觉刺激的大部分信息，也可以通过简单的线性分类来提取，例如可以由单个神经元来实现。新的刺激并不会抹去先前刺激的信息。对最近一次刺激的回应包含了关于这次刺激和前一次刺激的大致相同数量的信息。这种信息既被编码在神经元集合的放电率(反应幅度)中，也被编码在使用短时间常数进行积分时(例如，20ms)，在单个棘波的精确定时(≤∼20ms)中，并且在刺激偏移量之外持续数100ms。结果表明，我们记录的网络具有褪色记忆，并且能够利用关于时间顺序刺激的信息进行在线计算。这一结果对假设对顺序输入逐帧分析的模型提出了挑战。研究人员通常认为，神经元反应主要携带有关引起这些反应的刺激的信息。我们在这里表明，当多个图像以快速序列显示时，对图像的响应包含关于前一图像的信息与关于当前图像的信息一样多。重要的是，这种记忆能力只扩展到序列中最近的刺激。这种影响只能部分通过神经元反应的适应性来解释。这些发现是在分析高维数据的新方法的帮助下做出的，高维数据是通过并行记录许多神经元(例如，100个)的反应而获得的。这些方法使我们能够研究大脑皮层神经元可以接触到的神经活动的信息内容，即只在很短的时间间隔内收集信息。这种一次性记忆具有类似于视觉信息的标志性存储--当我们闭上眼睛时，视觉场景的详细图像会停留一小段时间(<；1 S)。因此，一回记忆可能是象征性记忆的神经基础。我们的结果与最近对神经元局部皮质网络(“通用皮质微电路”)的详细计算机模拟一致，该模拟表明，随着时间的推移整合信息是这些网络的基本计算操作。
The brain has a one-back memory for visual stimuli. Neural responses to an image contain as much information about the current image as it does about another image presented immediately before. It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤∼20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs. Researchers usually assume that neuronal responses carry primarily information about the stimulus that evoked these responses. We show here that, when multiple images are shown in a fast sequence, the response to an image contains as much information about the preceding image as about the current one. Importantly, this memory capacity extends only to the most recent stimulus in the sequence. The effect can be explained only partly by adaptation of neuronal responses. These discoveries were made with the help of novel methods for analyzing high-dimensional data obtained by recording the responses of many neurons (e.g., 100) in parallel. The methods enabled us to study the information contents of neural activity as accessible to neurons in the cortex, i.e., by collecting information only over short time intervals. This one-back memory has properties similar to the iconic storage of visual information—which is a detailed image of the visual scene that stays for a short while (<1 s) when we close our eyes. Thus, one-back memory may be the neural foundation of iconic memory. Our results are consistent with recent detailed computer simulations of local cortical networks of neurons (“generic cortical microcircuits”), which suggested that integration of information over time is a fundamental computational operation of these networks.
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DOI：
10.1073/pnas.94.23.12699
发表时间：
1997-11-11
影响因子：
11.1
作者：
Fries, P;Roelfsema, PR;Singer, W
通讯作者：
Singer, W
影响因子：
--
作者：
FANO, U
通讯作者：
FANO, U
影响因子：
3.4
作者：
GERSTEIN, GL;PERKEL, DH
通讯作者：
PERKEL, DH
影响因子：
1.9
作者：
Britten, KH;Newsome, WT;Movshon, JA
通讯作者：
Movshon, JA
影响因子：
25
作者：
Fries, P;Neuenschwander, S;Singer, W
通讯作者：
Singer, W