Hello!
I am using nilearn to plot some brain data.
Specifically, I created a 3d array matching the dimensions of the original data, then I assigned values to specific coordinates. I would like to plot the resulting array using plot_stat_map.
This is the code I'm using:

array_3d = np.full((53, 63, 52), np.nan)
for index, row in data.iterrows():
x = int(row['x'])
y = int(row['y'])
z = int(row['z'])
intensity = row['intensity']
array_3d[x, y, z] = intensity
array_3d[10,49,26]
nifti = nib.Nifti1Image(array_3d, np.eye(4))
plotting.plot_stat_map(nifti)
The issue is that if I use the default bg_image in plot_stat_map, the plotted intensities look to be confined in a small space of the bg_img, rather than covering it all. If I use plot_stat_map(nifti,bg_img = None), the plot looks as intended, without any background image.

Here are the two images https://imgur.com/a/rUgwjmn

At KU's BBQ program and very devoid of any friends in the field. I read all sorts of papers and only have my professor to annoy haha.

I study NLP primarily, but obviously like all things neuroscience and ML.

Paper: https://arxiv.org/abs/2403.16933

Abstract:

Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (BPTT). However, BP(TT) is well-known to rely on biologically implausible assumptions, in particular with respect to spatiotemporal (non-)locality, while forward-propagation models such as real-time recurrent learning (RTRL) suffer from prohibitive memory constraints. We introduce Generalized Latent Equilibrium (GLE), a computational framework for fully local spatio-temporal credit assignment in physical, dynamical networks of neurons. We start by defining an energy based on neuron-local mismatches, from which we derive both neuronal dynamics via stationarity and parameter dynamics via gradient descent. The resulting dynamics can be interpreted as a real-time, biologically plausible approximation of BPTT in deep cortical networks with continuous-time neuronal dynamics and continuously active, local synaptic plasticity. In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation. For the forward computation, it enables the mapping of time-continuous inputs to neuronal space, performing an effective spatiotemporal convolution. For the backward computation, it permits the temporal inversion of feedback signals, which consequently approximate the adjoint states necessary for useful parameter updates.

https://arabsinneuro.org/school/

AiN have opened the summer school applications for 2024,
They welcome Arabic-speaking participants from every corner of the globe, fostering a vibrant and inclusive learning environment.
The school is dedicated to fill the interdisciplinary gap in many education systems by gathering students from biological and computational backgrounds and introducing topics essential for computational neuroscientists such as python programming, neurobiology, calculus, statistics and linear algebra. The school is also dedicated to cater to non-native English speakers in the Arabic-speaking countries which is why the language of instruction is in Arabic. The school is supported by the generous funding from Simons Foundation.
School dates: August 11-30 2024
Application deadline: May 7, 2024

Share it with any arabic speaking friend or collegue that might be interested in applying.

Hello. So I have a somewhat complicated plan/ path I feel like I wanted to share and as well gain feedback of the best way to reach my goal. I am about to graduate highschool, I am going to attend Hendrix College and was selected as an incoming freshman to lead their student lead neuroscience department in terms of projects. I am studying their “Study of the Mind” major which is a combination of neuroscience, computer science,biophysics, and philosophy. I am looking to gain a masters in computational neuroscience and a PhD in the same thing. I want to eventually create accurate models of the brain in order to deeply tackle the question of consciousness (I am far aware that this is seen as an impossible subject to tackle) but regardless, it is and has been a dream of mine to tackle since I was 8 years old. I am autistics and have been reading papers on consciousness for a long period of my life, but I eventually want to create a computational method of therapy for those struggling with not getting help from verbal therapy. Anywho, the main question I have is, what are some things I NEED to do in college to further studies and opportunities , and as well things I SHOULD do, but wouldn’t be 100% necessary. Thank you very much.

Hi all. I am planning to study a master course in comp neuro organized by UoSheffield in the UK.

They offer basic modules for modeling neurons as well as cognitive functions. In addition, some knowledge about the research methods in the field and analysis programming techniques are also taught in the core modules.

Besides, there are some optional courses to take, but the intake quota for these courses is at most two of them only. They are:

Cognitive neuroscience

System neuroscience

Neural imaging 1: analyzing and processing data from electrophysiology, optical methods and calcium imaging

Neural imaging 2: fMRI techniques and related analyzing methods

If I want to do further study in the field after the master program, what kind of knowledge, for the optional modules in this program, would benefit me the most?

I wonder whether the imaging techniques and analysis methods are really important to me when I go to apply for any positions about research. If not so, I would prefer to study the other two modules.

I am a double major in Neuroscience and Pure Mathematics at an R1 in the US. My Math major GPA is just a little bit lower (3.3) than my Neuroscience GPA (3.5). I'm an undergrad TA for the general physiology course at my university that seats 1000 students every semester. I don't plan to join a research group in my remaining time as an undergraduate and will apply next year to the Neuroscience MS at my institution to gain research experience to eventually apply for a Ph.d. Sometimes I think about pursuing graduate Mathematics than graduate Neuroscience and I think about how I would much prefer to have a career in Neuroscience than Mathematics.

At the very start of undergrad I was a Physics and Pure Math double major and eventually walked away from Physics and went into Neuroscience. I have Modern Physics and 3rd year E&M completed on my transcript from the Physics major though. I walked away from Physics for a variety of reasons but one of the reasons why was because I took a lot of time off undergrad when Covid hit (which is also one reason why I'm not applying immediately to a Ph.d) and fell out of love with Physics.

Same as title. I applied to a couple of unis for masters hoping to work in Neuro-AI under some professor there and get a good GPA plus research experience to eventually apply for a PhD in neuroscience since my undergrad GPA is very low and I dont have a formal background in neuroscience. I had high hopes for a uni in Canada since my prospective supervisor there was perfectly matching my research interests, knew the current prof I worked under personally and had also approved my application but unfortunately I was informed today that the department rejected my application because of my GPA.

I am currently waiting for decisions for 2 more masters programs Trento cognitive science and Brain and Cognitive Sciences at UPF Barcelona.

What are some other masters programs in unis with comp neuro researchers in Europe (not US since its very expensive) where I can still apply to in the current cycle.

My stats

6.62/10 GPA B.Tech Electronics and Telecommunication Tier 2 uni in India(19-23)

My Research Experience

1 yr of research exp under a prof at an ivy league uni

1 yr on a funded project with an Italian uni where I did my thesis too

2 yrs or research experience in my unis AI research Center

6 Months at a Healthcare startup.

Pubs- 3 under review

This is probably the last time I will be applying since if I don't get in this time its solely because of my GPA, which can't be changed so no point in trying again next year or anything.

Hello everyone,

I recently applied to PhD programs in Computational Neuroscience at several top institutions, including Harvard, UCSD, and the University of Chicago. I also applied for two courses at the University of Chicago: "Brains, Minds and Machines" and "Methods in Computational Neuroscience". Unfortunately, I received rejections from all these programs without detailed feedback. Some advisors from McGill mentioned they were not looking for someone with a medical background or they were seeking a different profile.

CV Images:

1: https://drive.google.com/file/d/1nSg7_2mf1v_OIj_PdeGnFiTr2TnMd6gb/view?usp=drivesdk

2: https://drive.google.com/file/d/173V2V0jttlDtoU-0ZnxxHQ0ofRXplw0r/view?usp=drivesdk

Given this situation, I'm seeking advice on how to improve my application for future submissions. Would you recommend pursuing a second master’s degree focused on Computational Neuroscience, or are there other steps I could take to enhance my profile? Any insights or personal experiences would be greatly appreciated!

Thank you!

For example, I see dimensionality reduction used a lot but I don't know from the ocean of ML what parts should I learn.
And also which ML or DL has more usage in CompNeuro more? From what I heard DL use a lot more but I'm not sure about it.

​

I have a BA in math, my GPA was low (just under 3.0), I didn't do an internship, and no undergrad research experience. I realized in my last semester that comp neuro is at the intersection of many of my interests. But because there was no time left to prepare, I have a lackluster resume. What can I do to give myself a fighting chance?

I'm currently teaching myself programming and I'm applying to every lab possible, but it's been 4 months since I graduated and I still don't have a job. I'm starting to feel so desperate that I'm applying to phlebotomy positions just for anything that might remotely help in the future.

Thank you for any suggestions

Hi all,

I'm a first-year undergrad getting a B.A. in cognitive science. I'm thinking about pursuing a PhD in cognitive or computational neuroscience. To be competitive at top programs, how much hard science/math do I need to take? I can take a biology of the brain class but all other neuroscience classes (brain architecture, neurobiology, etc) require a general bio prereq which is notoriously difficult and a weed-out class. Do I need to take this prereq and then these micro-level, science-heavy neurobio classes to be competitive? Or can I take more psych classes (neural networks, cognitive neuroscience, developmental neuro, neuropsychology)?

Note: I took AP Calc AB and BC in high school.

I had a doubt and hoped to discuss it. I'm a beginner in this field

Applications for CAMP 2024 are now open! Apply now @ http://camp.iiserpune.ac.in/ . Last date for applying: 23rd April, 2024. #camp2024 . Please spread the word

https://preview.redd.it/zfft40b611sc1.png?width=1200&format=png&auto=webp&s=f3ede0a5430e4a82a8033668228e65d7ed391ddb

I am in the midst of preparing my application for the Computational Neuroscience master's program at the University of Tuebingen, previously known as "Neural Information Processing", targeting enrollment for this winter semester. A key component of the application process is a subject test scheduled for early May. The guidelines suggest a strong foundation in maths (specifically linear algebra and analysis), statistics, elementary probability theory, and physics is crucial for the test.

Given the broad spectrum of topics and the demanding nature of the program, I'm turning to this community for deeper insights into the subject test to enhance my preparation strategy:

1. Types of Questions: What kinds of questions were asked during the subject test? Were they more focused on mathematics and statistics, or did they also cover specific neuroscience topics?
2. Preparation Tips: How did you prepare for the test? Are there specific topics in linear algebra, analysis, statistics, or physics that I should focus on more intensely?
3. Test Duration and Structure: How long does the test take, and what is its structure? Is it more theoretical, or does it involve practical problem-solving as well?
4. Personal Experiences: Can anyone share their personal experience with the subject test? What did you find challenging, and how did you overcome it? Any advice for prospective students would be incredibly valuable.

Thank you in advance for sharing your experiences, advice, and any resources you might have.

​

If you are interested in the operation of Izhikevich neurons, this simulator will help you understand their operation.

Windows only 64 bit. Never a charge
NeuronLab Simulator (seti.net)

Just out of curiosity.

Would appreciate any sources, thanks in advance.

Paper: https://www.nature.com/articles/s41593-024-01607-5

Code: https://github.com/ReidarRiveland/Instruct-RNN/

Abstract:

A fundamental human cognitive feat is to interpret linguistic instructions in order to perform novel tasks without explicit task experience. Yet, the neural computations that might be used to accomplish this remain poorly understood. We use advances in natural language processing to create a neural model of generalization based on linguistic instructions. Models are trained on a set of common psychophysical tasks, and receive instructions embedded by a pretrained language model. Our best models can perform a previously unseen task with an average performance of 83% correct based solely on linguistic instructions (that is, zero-shot learning). We found that language scaffolds sensorimotor representations such that activity for interrelated tasks shares a common geometry with the semantic representations of instructions, allowing language to cue the proper composition of practiced skills in unseen settings. We show how this model generates a linguistic description of a novel task it has identified using only motor feedback, which can subsequently guide a partner model to perform the task. Our models offer several experimentally testable predictions outlining how linguistic information must be represented to facilitate flexible and general cognition in the human brain.

Paper: https://www.sciencedirect.com/science/article/pii/S2211124723012500

Code: https://zenodo.org/records/8333600

Summary:

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable—yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.