Quantification of information processing capacity in living brain as physical reservoir

油藏计算 计算机科学 听觉皮层 桥接(联网) 刺激(心理学) 度量(数据仓库) 人工神经网络 人工智能 神经科学 循环神经网络 数据库 心理学 计算机网络 心理治疗师
作者
Naoki Ishida,Tomoyo Isoguchi Shiramatsu,Tomoyuki Kubota,Dai Akita,Hirokazu Takahashi
出处
期刊:Applied Physics Letters [American Institute of Physics]
卷期号:122 (23) 被引量:3
标识
DOI:10.1063/5.0152585
摘要

The information processing capacity (IPC) measure is gaining traction as a means of characterizing reservoir computing. This measure offers a comprehensive assessment of a dynamical system's linear and non-linear memory of past inputs by breaking down the system states into orthogonal polynomial bases of input series. In this study, we demonstrate that IPCs are experimentally measurable in the auditory cortex in response to a random sequence of clicks. In our experiment, each input series had a constant inter-step interval (ISI), and a click was delivered with a 50% probability at each time step. Click-evoked multi-unit activities in the auditory cortex were used as the state variables. We found that the total IPC was dependent on the test ISI and reached a maximum at around 10- and 18-ms ISI. This suggests that the IPC reaches a peak when the stimulus dynamics and intrinsic dynamics in the brain are matched. Moreover, we found that the auditory cortex exhibited non-linear mapping of past inputs up to the 6th degree. This finding indicates that IPCs can predict the performance of a physical reservoir when benchmark tasks are decomposed into orthogonal polynomials. Thus, IPCs can be useful in measuring how the living brain functions as a reservoir. These achievements have opened up future avenues for bridging the gap between theoretical and experimental studies of neural representation. By providing a means of quantifying a dynamical system's memory of past inputs, IPCs offer a powerful tool for understanding the inner workings of the brain.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
敬鱼发布了新的文献求助10
刚刚
雾里完成签到,获得积分10
刚刚
CCH发布了新的文献求助10
刚刚
1秒前
李健应助王灿章采纳,获得10
1秒前
科研通AI5应助月亮采纳,获得10
1秒前
小王小王发布了新的文献求助10
2秒前
啵赞的龟丝儿完成签到,获得积分10
2秒前
fanfan44390完成签到,获得积分10
2秒前
共享精神应助坚定的寒松采纳,获得10
2秒前
害羞文博发布了新的文献求助10
3秒前
ermu应助felix采纳,获得10
4秒前
毛毛弟发布了新的文献求助10
4秒前
曾无忧应助felix采纳,获得10
4秒前
wjx发布了新的文献求助10
5秒前
5秒前
激动的跳跳糖完成签到 ,获得积分10
6秒前
6秒前
ZeKaWa应助HY采纳,获得10
7秒前
8秒前
xxy发布了新的文献求助30
8秒前
8秒前
Tiramisu628发布了新的文献求助10
9秒前
李健应助小娅娅采纳,获得10
9秒前
冯123发布了新的文献求助10
9秒前
9秒前
9秒前
科研通AI6应助科研通管家采纳,获得10
10秒前
10秒前
搜集达人应助科研通管家采纳,获得10
10秒前
传奇3应助科研通管家采纳,获得30
10秒前
科研通AI6应助科研通管家采纳,获得10
10秒前
10秒前
英勇的飞扬完成签到,获得积分10
10秒前
10秒前
我是老大应助科研通管家采纳,获得10
10秒前
所所应助科研通管家采纳,获得10
10秒前
SciGPT应助科研通管家采纳,获得10
10秒前
Libra应助科研通管家采纳,获得10
10秒前
桐桐应助科研通管家采纳,获得10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Acute Mountain Sickness 2000
A novel angiographic index for predicting the efficacy of drug-coated balloons in small vessels 500
Textbook of Neonatal Resuscitation ® 500
Thomas Hobbes' Mechanical Conception of Nature 500
The Affinity Designer Manual - Version 2: A Step-by-Step Beginner's Guide 500
Affinity Designer Essentials: A Complete Guide to Vector Art: Your Ultimate Handbook for High-Quality Vector Graphics 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5097113
求助须知:如何正确求助?哪些是违规求助? 4309682
关于积分的说明 13427832
捐赠科研通 4137094
什么是DOI,文献DOI怎么找? 2266469
邀请新用户注册赠送积分活动 1269541
关于科研通互助平台的介绍 1205874