19 items tagged
Special seminar on neuro AI, visual neuroscience, interpretability and generative models
Factorized Convolutional Model for Interpreting Neuron Guided Image Synthesis
Participation in KITP Deep Learning program focusing on physics and neuroscience perspectives
Dynamic Alignment of Neural Tuning to Generative Image Manifolds
MTurk MTurk is a online way to recruit subjects and perform tasks, widely used in psychology and machine learning to collect human perception and behavior data. Some terminology: HIT: Basically the task Requester: the experimenter Worker: the subjects An work through of a task (Image classification)
Note on Neural Tuning and Information Given a stimuli with $D$ intrinsic dimensions, we consider how one neuron or a population of neurons is informative about this stimulus space. Specific Information (Mutual Information) Setup for specific information computation is easy given a certain response $r$ , compute the reduction of entropy of stimuli $\mathbb s$ .
Notes on Cortical Waves Methods
Notes on Visual Imagery Definition: Recreate the sensory world in mind in absense of physical stimuli. Usage in daily cognition Closely related to memory. We solve some cognitive task by recreating the visual scene in mind and examine the mind picture! Some tasks are memory about spatial some are feature memory! Usage in creative work Provides another way of thinking, other than verbal and logical induction. Intuition Characteristics of Imagery Is the representation spatial or propositional?
Note on Categorization and Concepts From lecture notes from Science of Behavior Configuration The relative configuration of a single elements Example: Face What defines a face? Components Essential feature Configural property Relative Invariance to many change in Stimuli
Based on Goldstein Book Chapter and lecture from Jeff Beck Note on Forms of Memory Definition pin down can be very tricky! Definition Retaining, retrieving, using information after the original information (stimuli) does not present. (Inner view) Any process that some past experience has an effect on the way the subject think and behave in the future. (Outer View) Thus can generalize into even non-animated things! Memory of magnet Use of Memory Longterm Memory Human: Remember things relevant for life. (name, pw, birthday, info about others, address, knowledge) Ecological: cache for food, foraging location. Shorterm Memory Continuity of awareness Different forms Memory has many forms.
Note on Animal Perception From lecture of Science of Behavior What does it feel to be a bat!? Umwelt: the sensory world of an animal, can be very different from ours. Different precision, range … Use same modality in different ways: Sound Imaging Electro-/Magneto-reception “More extreme your claim, stronger your evidence!”
How to Parse Latin/Greek Anatomical/Physiological Terms TOC {:toc} 大部分现代医学 解剖术语似乎是文艺复兴之后生理学家创造的，但为了显得高端，使用当时的国际学术语言Latin书写。因为当时Latin在当时早已不是日常语言，所以大多是科学家们非常规整的用Classic Latin/Greek 的词根Root 词缀Pre-/Suffix 变位法Conjugation，人工构词产生的，算New Latin。所以知道了词根之后拼写起来单词就会像写中文组词一样，记忆起来也容易1，也更容易根据词形记忆解剖结构的位置。 词根 单个词大多是由词根词缀组合而成 比如 肋骨rib= costa costae(单数形容词) costarum(复数形容词) 腹股沟groin= Ilium Ilio(变位形式) 合在一起成了Iliocostalis 髂肋肌 Wikipedia List of medical roots, suffixes and prefixes
Note on Behavioral Study History of Behavioral Science Two historical trends of study combines into modern comparative cognition / behavioral science study. Comparative Psychology More in context of psychology: Athropocentric
可以以物理, 计算机与神经为例. 物理学中不同尺度, 不同能级的物质会有不同尺度的物理, 单个粒子的规律与大量粒子整体表现出的状态也极为不同. 因而凝聚态物理学家会谈 More is Different: 少量粒子与大量粒子的规律是不同的, 后者的表象规律(phenomenonal law)也同样是有用的物理; 粒子物理学家会谈Hierarchy: 不同能量标度下的物理可以是不同的, 他们只有在特定标度下才能统一到一个框架之中. 于是乎, 研究机械力学的人不非要知道材料分子的属性, 而只用了解几个宏观材料参数; 研究气体流体的人不非得了解电子轨道的性质, 而可以把分子近似成某种模型并讨论其相互作用; 在原子分子里低能情形下, 不必讨论相对论性修整, Schrodinger方程与Dirac方程各有其用途; 讨论源自核外电子轨道性质的化学, 不非得了解原子核的性质; 关心原子核, 核反应的人不非得了解强子对撞机上的粒子实验 (粒子能级远高于原子核反应). 正是因为不同层面的物理规律有所不同, 而用计算机的语言说, 理解更上层的物理不非要理解底层的机制, 一般只需要理解其包装过后的接口 —- 如表观的相互作用. 因此物理学分成了不同领域, 不同的人专注于不同的规律, 而只需要理解部分的物理.
Motivation Although there are a millennium of methods for neural and behavioral signal recording, the questions asked about the neural data is ususally less diverse. Ultimately, everything is number and we process numbers with algorithm.
Note on Computation by Biological Plausible Learning from lecture of Cengiz Penleven 2019 Philosophy Neural dynamics can be a substrate of computation. The neural dynamics and plasticity dynamics can both do optimization, and the biological constraint form a source of constraint on variables.