世界上哪些大學計算神經科學比較好？

01-02

我是大三學生，想申計算神經科學的phd，不過這個學科比較細，排名不太好找，所以想問下這個問題？
具體分國內和國外的情況吧，主要是做跟認知有關的方面

簡單回答一下。如果是自己想找讀博士的方向的話，可以關注一下這個領域的會議

Computational and System Neuroscience

http://www.cosyne.org/c/index.php?title=Cosyne_15

這個會議主要是phd和postdoc的成果，可以通過查看前幾年的發表情況來推測哪些老闆/院校在圈內比較活躍、funding比較充足（也就意味著申請成功的可能性相對更大）。

說說美國比較著名的院所。由於這算是個交叉學科，所以很多做計算方向的老闆並不掛靠在Neuroscience系。有的是在單獨的Institute，有的掛靠在其他院系比如Psychology，Physics，EE/CS，Medical School等等。具體的情況如果想盡量了解肯定需要做一些功課。

（我大概按地理位置回憶，排名不分先後。。）

西海岸：

UCSD - Salk Institute （這個是Terry Sejnowski坐鎮)

UC Berkeley (有個Redwood Center)

UC San Francisco (這個學校以醫學院為主)

UCLA

USC

Stanford

U Washington

東海岸：

Harvard（有個http://cbs.fas.harvard.edu/）

MIT （CSAIL裡面有一些教授做這個方向）

Yale

NYU（這個學校本身這個方向就很強大，而且傳聞最近也在大量挖別的學校的教授，可以重點關注）

Columbia

CSHL（這個就是著名的冷泉港，好像postdoc多一些，phd機會也有不過比較少）

Princeton （這個學校目前在招兵買馬到處挖人）

UPenn （這兩年要成立Neuroscience Center，可能也會挖人）

John Hopkins University

Janelia Farm (是個獨立的lab，目前只跟JHU合作培養phd，但與jhu本身招生可能是不同的渠道)

南部：

UT Austin

Baylor College of Medicine

歐洲的不是很了解，大概列舉比較有名的一些：

Gatsby Unit (UCL)

Max-Plank Institute

Ecole Normale Superieure

Champalimaud Centre for the Unknown

Ecole Polytechnique Federale de Lausanne (EPFL)

加州伯克利的紅杉中心

http://redwood.berkeley.edu

印第安納大學的研究中心

http://psych.indiana.edu

還有UCL的Gatsby Center都是很不錯的認知與神經學的研究中心。

樓上說的都很好補充個NYU

Caltech - Computation and Neural Systems，美國第一個 computational neuroscience的 PhD Program

本來想Phd磕鹽來著發現自己不是這塊料.... 把整理的資料貢獻出來吧，選的教授都是有很強主觀偏好的，總體上講是比較computation的方向，可以看出不同大學community的大體特點

Stanford:

神經動力學和計算 https://ganguli-gang.stanford.edu/ 有點靠深度學習混日子的感覺

通過神經科學研究情緒 https://spl.stanford.edu/

一個比較雜的老頭老師 http://stanford.edu/~jlmcc/ 什麼都搞

研究神經機理致力於解決神經紊亂的 http://med.stanford.edu/scsnl.html 印度老頭

Vision and Neuro-Development https://svndl.stanford.edu/research 主要搞perception和相關的decision making

decision making, executive control, and learning and memory https://poldracklab.stanford.edu/

從Neural和Psychological角度看Memory http://memorylab.stanford.edu/Research/Research.html

CMU:

fMRI analysis http://www.stat.cmu.edu/GSS/eddy.html

My research largely falls at the intersection of philosophy, cognitive science, and machine learning, using ideas and frameworks from each to inform the others. My primary research in recent years has been in computational cognitive science: developing fully-specified computational models to describe, predict, and most importantly, explain human behavior (in causal cognition, concepts and categories, and most recently, linguistics).

Learning and reasoning in humans (including causation, concepts, and more)

Ethics regulation of autonomous systems

Philosophy of psychology

Philosophy, including philosophy of science and causation

Machine learning http://www.andrew.cmu.edu/user/ddanks/pubs.html

Brain Computer Interface http://www.cnbc.cmu.edu/~schase/index.php

This new technological focus led her to deconstruct the linguistic elements of conversation and storytelling in such a way as to embody machines with conversational, social and narrative intelligence so that they can interact with humans in human-like ways. Increasingly, however, her research has come to address the impact and benefits of technologies such as these on learning and communication. http://www.justinecassell.com

The concern is very much with how it is all put together and this has led to the focus on what are called "unified theories of cognition." A unified theory is a cognitive architecture that can perform in detail a full range of cognitive tasks. Our theory is called ACT-R (Anderson Lebiere, 1998) and takes the form of a computer simulation which is capable of performing and learning from the same tasks that subjects in our laboratories work at. http://act-r.psy.cmu.edu/peoplepages/ja/ja-interests.html

Nonparametric prediction of time series; learning theory and nonlinear dynamics; information theory; stochastic automata, state space and hidden Markov models; causation and prediction; large deviations and ergodic theory; neuroscience; statistical mechanics and self-organization; social and complex networks; heavy-tailed distributions. http://www.stat.cmu.edu/~cshalizi/ I will not be taking any new students until 2018--2019 at the earliest. If you are not at CMU, by all means apply to our graduate program. I have no influence over admissions, and don"t want any, so writing me about that is a waste of your time. I have no openings for post-docs or other employees. Nor will I make an exception for you.)

Music cognition analysis http://www.contrib.andrew.cmu.edu/~randall/

Visual recognition involves topographically-constrained cooperation and competition among multiple, interacting regions, each of which is only partially selective for a specific domain. http://www.cnbc.cmu.edu/~plaut/Research.html Statistical Learning

Linda"s primary pedagogical and research interests are in the neuroscience of decision making, with an focus on how the social and emotional brain affect the decision making process. Methodologically, she conducts behavioral experiments with human participants using psychophysiology and neuroimaging methods: MEG, EEG and structural MRI. Scientifically, she tests a priori hypotheses based on theory developed from extant literature from across neuroscience sub-disciplines: systems, behavioral, decision, and cognitive; while at the same time being open to unexpected and unpredicted but robust patterns of results in the data born of sophisticated statistical learning techniques. http://www.andrew.cmu.edu/user/lhmoya/

Tom Mitchell

Can computers learn to read the web?

See our never-ending language learning (NELL) research

How does the human brain represent word meanings?

machine learning to analyze human brain activity (fMRI) http://www.cs.cmu.edu/~tom/

Information decoding http://www.stat.cmu.edu/~vventura/

We have two major aims:

(1) To develop and apply novel signal processing and machine learning algorithms to explain the high-dimensional structure and timecourse of neural population activity.

(2) To apply this knowledge to the design of next-generation biomedical devices that interface with large populations of neurons.

The work is at the intersection of signal processing / machine learning, biomedical engineering, and basic neuroscience. https://users.ece.cmu.edu/~byronyu/research.html

The general research goal is to develop a unified theory of cognition that is driven by and accounts for the brain activation in the cortex, at the level of large scale neural networks that perform cognitive computations. The goal is to explain how thought emerges from brain function and how it is affected by brain dysfunctions. http://www.ccbi.cmu.edu

Neurostats and spike train data http://www.stat.cmu.edu/~kass/

Our main laboratory"s research tasks are interactive computer simulations that represent decision making situations. They are environments characterized by the need for people to make multiple, interdependent, real-time decisions in reaction to both external changes and to the effects of their past decisions. We have created DMGames in many diverse contexts, including examples for dynamic resource allocation, medical diagnosis, supply chain management, climate change and CO2 accumulation, as well as other generic choice and control task. http://www.hss.cmu.edu/departments/sds/ddmlab/Introduction.html

Upenn:

Dynamics changes in network architecture, interaction between topological properties of networks or other constraints, and influence of network topology on signal propagation and system function http://www.danisbassett.com/research-projects.html Danielle Bassett

I study thinking, reasoning, social cognition, and moral judgment.http://web.sas.upenn.edu/ggoodwin/

Currently, his lab uses computational and theoretical methods to study how texture and shape are represented in the visual system, and how physical space is represented in the hippocampus and entorhinal cortex. His lab is also studying how circuits in the olfactory system adaptively represent information about odor mixtures and their valence. https://psychology.sas.upenn.edu/people/vijay-balasubramanian-0

My lab studies the neural mechanisms underlying visual scene perception, event perception, object recognition, and spatial navigation in humans. We are interested in both perception and memory; thus, for example, our research on navigation explores topics in both scene/place/landmark recognition and spatial memory. Recent work has focused on using functional magnetic resonance imaging (fMRI) and other cognitive neuroscience techniques to understand how scenes, objects, landmarks, events, and environmental spaces are represented in specific brain systems. https://psychology.sas.upenn.edu/people/russell-epstein Professor Russell Epstein will be considering new graduate students for admission for Fall 2018.

By analyzing how brain activity, including the responses of individual neurons, correlates with task variables, we are able to study the neurophysiological basis of memory with a high degree of spatial and temporal resolution. Current projects include studies of spatial navigation using a virtual taxi driver game, and computational modeling of the role of temporal context in visual and verbal memory. https://psychology.sas.upenn.edu/people/michael-kahana Professor Michael Kahana will be considering new graduate students for admission for Fall 2018.

Human experimental and mathematical psychology: short-term dynamics of visual representation; brain and biomechanics in timing repeated movements; retrieval from active memory: reaction-time methods and information-processing models. https://psychology.sas.upenn.edu/people/saul-sternberg

My research focuses on word recognition, word learning, and lexical representation in infants and young children. Current projects include perceptual experiments with infants, statistical and acoustic analyses of infant-directed speech corpora, and perceptual learning studies of adults. https://psychology.sas.upenn.edu/people/daniel-swingley Professor Daniel Swingley will be considering new graduate students for admission for Fall 2018.

Recent projects emphasize (1) functions of the frontal lobe in the regulation of thought and behavior, especially in relation to language and memory processes; and (2) the organization and neural substrates of concept knowledge (especially knowledge of visual attributes) and the relation between conceptual information and perception and language. https://psychology.sas.upenn.edu/people/sharon-thompson-schill

My research group develops scalable machine learning and text mining methods for use in natural language processing, psychology, and medical research. Projects ranged from crowd-sourced decision making to mining social media to better understand connections between mental and physical well-being. https://psychology.sas.upenn.edu/people/lyle-h-ungar-0

The Brainard Lab studies human vision, both experimentally and through computational modeling of visual processing. Our primary concern is with how the visual system estimates object properties from the information available in the light signal incident at the eye. To study this general problem, we conduct psychophysical experiments to investigate questions such as how object color appearance is related to object surface properties under a wide range of illumination conditions and how color is used to identify objects, and formulate computational models of the results. In addition, we are interested in developing machine visual systems that can mimic human performance and in understanding the neural mechanisms of vision. https://psychology.sas.upenn.edu/people/david-brainard

how auditory information is integrated with cognitive processes such as attention, motor planning, or memory, and how auditory and visual information is combined to form unified sensory percepts. https://psychology.sas.upenn.edu/people/yale-cohen-0

Visual Memory https://psychology.sas.upenn.edu

"Believing is seeing." - My research interest is to understand how our visual percept of the world is shaped by our beliefs and expectations about what there is to be perceived. https://psychology.sas.upenn.edu/people/alan-stocker Professor Alan Stocker will be considering new graduate students for admission for Fall 2018.

Computer Vision華人導師 http://www.cis.upenn.edu/~jshi/

又是個搞AI的 http://www.cis.upenn.edu/~kostas/

Human vision. Our primary concern is with how the visual system estimates object properties from the information available in the light signal incident at the eye.http://color.psych.upenn.edu

Auditory https://geffenlab.weebly.com/research1.html

My group』s research focuses on learning representations that enable autonomous systems to efficiently reason about real-time behaviors in an uncertain world. In particular, much of our work has used low-dimensional representations to overcome the curse of dimensionality in perception, planning and control tasks. We use machine learning algorithms and computational neuroscience models, in addition to implementations on a variety of robotic platforms to study how to build better sensorimotor systems that can adapt and learn from experience. http://www.seas.upenn.edu/~ddlee/ 可怕的搞凝聚態物理的大牛

My work uses mathematical and computational cognitive models, and tests the predictions of these models with behavioral experiments. https://psychology.sas.upenn.edu/people/sudeep-bhatia

Chris"s laboratory explores how neural circuits generate behavior using the nematode C. elegans as a model. This 1-mm-long worm has only 302 neurons, and is the only animal for which the complete "wiring diagram" of synaptic connectivity has been determined. http://www.seas.upenn.edu/directory/profile.php?ID=170

Neuroengineering | Imaging | Signal Processing http://www.seas.upenn.edu/directory/profile.php?ID=12

research in the computational perception and cognition (CPC) laboratory is aimed at understanding how prior beliefs and expectations shape perception. http://www.sas.upenn.edu/~astocker/lab/index.php

My lab"s focus is on how the nervous systems encodes information. In particular, we are studying: (i) the possible role of oscillatory activity in the gamma band (20-80 Hz) for encoding visual stimuli in corticothalamic networks; (ii) the role of corticothalamic feedback. Responses to visual stimuli are recorded from cortex and thalamus with combined intracellular and optical recordings using voltage sensitive dyes and calcium indicators. These two methods are also applied to brain slices in order to understand the dynamics of visual cortex microcircuitry. http://www.med.upenn.edu/apps/faculty/index.php/g309/p16954

Human memory and its neural mechanisms: especially episodic memory, spatial memory, and recognition memory http://memory.psych.upenn.edu/Main_Page

Princeton:

搞Consciousness和awareness的心理學哲學義大利後裔老頭 http://www.princeton.edu/~graziano/ 感覺略玄學略水

We focus on understanding how subjects cope with computationally demanding decision situations, such as choice under uncertainty or in tasks, current projects include investigating how the brain controls its own decision-making computations https://psych.princeton.edu/person/nathaniel-daw

understand the roles of these brain regions in executive control and how complex behavior arises through their interactions with each other and with the rest of the brain. https://psych.princeton.edu/person/timothy-buschman

Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making https://psych.princeton.edu/person/yael-niv

learning and memory phenomena https://psych.princeton.edu/person/kenneth-norman we are developing (along with other Princeton researchers) new machine learning methods for analyzing distributed patterns of neural activity

My research sits at the interface between computational neuroscience and statistical machine learning https://psych.princeton.edu/person/jonathan-pillow http://pillowlab.princeton.edu/people.html 這個不錯

My research investigates the thoughts, cognitive processes, and behaviors that arise from these self and social forces using a combination of functional neuroimaging, behavioral experiments, and data science methods. https://psych.princeton.edu/person/diana-tamir

My research focuses on unraveling the different computational processes involved in solving this motor control problem. https://psych.princeton.edu/person/jordan-taylor http://ipalab.princeton.edu 寫2018Fall招學生

Mental models and reasoning http://mentalmodels.princeton.edu/about/

The Hasson Lab attempts to develop complementary paradigms to study the neural activity that drives human behavior under natural and realistic conditions: Mapping of temporal receptive windows and Two-brain interactions. https://www.hassonlab.com

A closed lab still updated http://mentalmodels.princeton.edu/about/ http://moax.behaviometrix.com/

ORFE

Stochastic Analysis

Stochastic Control and Stochastic Games (Probabilistic Approach to Mean Field Games)

Stochastic PDEs (Random Media and Disordered Systems), Stochastic Flows, Numerical Simulations

Financial Mathematics / Engineering

Energy and Commodity Markets

High Frequency Markets and Systemic Risk

Environmental Economics (Weather and Emissions Markets)

Signal / Image Analysis

Time Frequency Transforms (Wavelet Gabor): Speech Processing, Underwater Acoustic

Medical Imagery, PDE and Global Optimization Approaches to Image Enhancement http://www.princeton.edu/~rcarmona/

Collective behavior in animal groups and Human decision-making dynamics http://www.princeton.edu/~naomi/

Signal processing in fMRI and machine learning http://www.ee.princeton.edu/ramadge/doku.php?id=research

? Data-driven computational methods for visualizing mental and neural representations

? How statistical learning shapes social face perception.

? Evaluative processes in the perception of novel 3D objects.

? How individual biases can propagate into shared false beliefs. http://tlab.princeton.edu

We use computational models to explore how the brain gives rise to learning and memory phenomena, and then we test the predictions of these models using neuroimaging studies where we decode people』s thoughts as they learn and remember. With other Princeton researchers, we are also developing new machine learning methods for analyzing distributed patterns of neural activity. We use these new analysis tools to track how thoughts and memories change over time. http://compmem.princeton.edu

MIT:

Statistical methods and signal-processing algorithms for neuroscience data analysis http://csbi.mit.edu/people/brown.html http://www.neurostat.mit.edu/ http://imes.mit.edu/

Visual Recognition http://csbi.mit.edu/people/dicarlo.html

Learning mechanism http://csbi.mit.edu/people/poggio.html

Understand neural plasticity: how the brain is wired and how this brain wiring leads to brain function http://csbi.mit.edu/people/sur.html

Learned behavior http://bcs.mit.edu/users/feemitedu

(1) how neurons and neural circuits generate and control dynamic patterns of activity, and (2) how those patterns encode behaviorally relevant information. http://bcs.mit.edu/users/mjazmitedu

Mechanism http://ekmillerlab.mit.edu/publications/

墨西哥計算角度認識智能大雜燴 http://bcs.mit.edu/users/tpoggiomitedu

Behavioral study, abstract thoughts http://bcs.mit.edu/users/saxemitedu 寫了對學生的要求,也許是比較著急招學生?

bridges computational models of cognitive development and behavioral studies in order to understand the origins of inquiry and discovery http://eccl.mit.edu

ow the human brain learns to recognize objects through visual experience and how objects are encoded in memory http://web.mit.edu/sinhalab/

computational basis of human learning and inference using empirical methods http://bcs.mit.edu/users/jbtmitedu

we study how the central nervous system in the brain enables our mind, with a focus on learning and memory. http://bcs.mit.edu/users/tonegawamitedu

The goals of this research are to understand how our own visual system operates, and how to construct artificial systems with visual capabilities including, for example, aids for the visually impaired. http://bcs.mit.edu/users/sullmanmitedu 可以用來保底

understand the nature of the computational system of human language in its many guises http://bcs.mit.edu/users/wexlermitedu 研究語言的計算基礎

use a technique that allows us to simultaneously record the activity of hundreds of individual neurons across multiple brain regions in freely behaving animals to gain a mechanistic understanding of how animals learn and remember. http://bcs.mit.edu/users/mwilsonmitedu

非線性系統,控制上比較牛逼 http://web.mit.edu/nsl/www/

MIT MEDIA LAB社會動力學! https://www.media.mit.edu/groups/human-dynamics/overview/ Human Dynamics is not currently accepting new students.

智能穿戴 https://www.media.mit.edu/groups/conformable-decoders/overview/

Collective Learning - transforming data into knowledge https://www.media.mit.edu/groups/collective-learning/overview/

Understanding brain in computational way http://cocosci.mit.edu

Harvard:

視覺和認知的綜合教授 http://visionlab.harvard.edu/VisionLab/index.php

Much of their research has focused on the respective contributions of 「fast」 automatic processes (such as emotional 「gut reactions」) and 「slow」 controlled processes (such as reasoning and self-control). The lab has applied this dual-process framework to classic hypothetical dilemmas, real temptations toward dishonesty, beliefs about free will and punishment, belief in God, wishful thinking, cooperation, and conflict resolution. More recent work aims to understand the infrastructure of complex thought. http://www.joshua-greene.net/

研究兒童語言能力發展的 https://software.rc.fas.harvard.edu/lds/research/snedeker/

Computational Cognitive Neuroscience Lab http://gershmanlab.webfactional.com/index.html

研究記憶的 https://scholar.harvard.edu/schacterlab/pages/research

Cognitive and Neural Organization in High-level space http://konklab.fas.harvard.edu

Visual perception, attention, and memory http://scorsese.wjh.harvard.edu/George/

研究睡眠和夢 http://www.hms.harvard.edu/dms/neuroscience/fac/Stickgold.php

Principles and mechanisms used by neural circuits to generate complex, learned behaviors http://www.hms.harvard.edu/dms/neuroscience/fac/Olveczky.php

developmental cognitive neuroscience (Children) http://www.hms.harvard.edu/dms/neuroscience/fac/Nelson.php

Network http://www.hms.harvard.edu/dms/neuroscience/fac/Lee_Wei-Chung.php

understand the fundamental computations and their neurobiological implementations that allow the nervous system to support such efficient behavior by using tools from machine learning and neuroscience. http://www.hms.harvard.edu/dms/neuroscience/fac/DrugowitschJan.php

Brain state and network http://www.hms.harvard.edu/dms/neuroscience/fac/Buckner.php

Yale

Yale Psychology Department不看成績單的,要三封推薦信,但是需要學術成績單,和學術作品

only about fifteen new students out of about seven hundred applicants can be admitted each year.

Using behavioral, fMRI, computational modeling, and patient studies, we have made some progress in understanding how statistical learning works, how it is supported in the brain, and how it influences other cognitive processes.

Repetition Attenuationhttp://ntblab.yale.edu/research/

The main goal of our research in the Lee Lab is to understand the cortical (e.g., prefrontal cortex) and subcortical (e.g., basal ganglia) mechanisms that enable animals to choose appropriate behaviors and to improve their decision-making strategies by evaluating the outcomes of previous actions. http://leelab.yale.edu/research/index.aspx

Computational modeling http://medicine.yale.edu/lab/shepherd/projects/computational_modeling.aspx

My laboratory employs functional magnetic resonance imaging (fMRI) to study visual attention, memory, decision-making, perception, and performance. http://psychology.yale.edu/people/marvin-chun

Cognition and memory http://psychology.yale.edu/people/marcia-johnson

We are interested in studying the nature of those gists and what they do and do not capture about real world causal relations. http://psychology.yale.edu/people/frank-keil

A developmental perspective that explores how young children cognitively grasp the many levels and types of causal structure inherent in the world. http://psychology.yale.edu/people/frank-keil

Different cognitive processes like perception, attention, learning, and memory – and the underlying brain systems that support them – are often studied in isolation. This is productive and necessary, but my lab takes the complementary perspective of trying to understand how these systems interact. _Statistical http://psychology.yale.edu/people/nicholas-turk-browne 這個老師又能從數據角度,又能從psychology理論角度

NYU

Phd Programs http://gsas.nyu.edu/admissions.html http://psych.nyu.edu/programs/cp/

I build computational models of everyday cognitive abilities, focusing on problems that are easier for people than they are for machines. The human mind is the best known solution to a diverse array of difficult computational problems: learning new concepts, learning new tasks, understanding scenes, learning language, asking questions, forming explanations, amongst many others. Machines also struggle to simulate other facets of human intelligence, including creativity, curiosity, self-assessment, and commonsense reasoning. http://psych.nyu.edu/lake/#publications

Columbia

In our lab, we are interested in intermediate levels of visual processing: levels which are lower than the perception of "objects」 and "scenes" but higher than the pointwise processing of the retina. https://psychology.columbia.edu/content/norma-graham

Explicit memory and implicit memory https://psychology.columbia.edu/content/daphna-shohamy

Our research focuses on how the brain supports learning, memory, and decision making in humans. We adopt an integrative approach that draws broadly on neuroscience to make predictions about cognition, and combine functional brain imaging (fMRI) with behavioral, pharmacological, and patient studies in humans. https://shohamylab.zuckermaninstitute.columbia.edu/research-projects

The ultimate goal of my lab is to understand the relationship between neuronal activity dynamics and the states of cognitive experience, like memories and thoughts. http://www.neurosciencephd.columbia.edu/profile/daronov

What drives motivation http://www.neurosciencephd.columbia.edu/profile/ebmartin

We particularly focus on statistics for understanding computation in neural systems: we use our brain in everything that we do, but we understand relatively little about how it works at a computational level. For example, how do populations of neurons control complex, sophisticated movement? http://stat.columbia.edu/~cunningham/

We want to understand how the brain generates intelligent behavior - i.e., how it learns, reasons and makes decisions in a changing world. A central interest in the lab is the neural basis of working memory and selective attention. http://www.neurosciencephd.columbia.edu/profile/jgottlieb

John Hopkins Univ.

In the mind, the present moment is a convergence point of two information streams: one, a continuous flow of sensory input from the outside world; and two, a series of elements from our past experiences, i.e., memories. Memories may be triggered by sensory stimuli, they may themselves cue more memories, and they may change the way incoming stimuli are interpreted, all of which become part and parcel of our current experience. http://jchenlab.johnshopkins.edu

感覺我們學校本部的神經科學非常強大，如果可以的話，嘗試申請一下碩士

不知道 CMU 和 Pitt 聯合的 CNBC 如何？

http://www.cnbc.cmu.edu/