It’s important to point out that my PhD program focuses on behavioral, systems, theoretical, cognitive and computational neuroscience. This is in contrast with molecular and cellular neuroscience. If you don’t know what that means, don’t worry I didn’t either when I started.
During the summer of 2015, I got married. Once the buzz of that exhilarating experience wore off, I realized my days of career exploration were limited. The next step would be children. Before that could happen, I would need a stable income. And so began a period of intense self-reflection.
I needed to choose a career path that would provide intrinsic fulfillment and sustained motivation.
I will detail more of that journey in another post when I present my personal statement.
For now, let me state the outcome of that journey: I chose to apply to the PhD program at the Center for Complex Systems and Brain Sciences at Florida Atlantic University in Boca Raton, FL. I was subsequently accepted and would begin coursework January 11th, 2016.
It may come as a surprise, but I greatly disliked science as a child. I struggled with biology in high school and earned a C in my college life sciences class. I found very little of it interesting. I ended up earning bachelor’s degrees in math and philosophy. Later I would go on to earn a master’s degree in computer science. These were the topics I enjoyed and excelled at. I also founded a tech startup, Zylo, which specializes in custom web and app design and general IT support services. This background would serve as a great strength for the PhD program. However, there was a healthy dose of science that I would have to learn and practice. So, I began to explore how to prepare for my program.
First, I had to identify topics I needed to learn about. This was a long process that involved both research online and discussions with those in the field. In the end, I was able to come up with the following topics as most relevant to my program:
- Time series analysis
- Modeling of complex systems
- Philosophy of mind
- Linear and nonlinear systems
- Data science
- Matlab (programming language)
- R (programming language)
I anticipate adding much more over the next 4 years. This is by no means an exhaustive list, but it’s a good start.
I realized that in order to really learn, I had to immerse myself in the field every day. So, I began writing down all the different ways I accomplished this. While reflecting on this rather long list, I had an epiphany.
Effective learning involves 4 main activities: reading, listening, explaining, and discussing.
Reading and listening were ways to consume knowledge. Explaining and discussing were ways to disseminate and analyze knowledge, respectively. Explaining and discussing also involve other people, most of the time.
The goal is to practice as many of these activities as possible each day. If you can get all 4 in one day, that’s ideal. I found that on days when I read, listened, explained and discussed, I was certainly more mentally exhausted. However, that mental exercising led to a greater proficiency in topics at a quicker speed.
The activities appear to impact each other repeatedly in a cyclic fashion as illustrated by the model below.
The more one reads and listens to material on the topics, the better one is able to explain and discuss those topics. Also, the more one explains and discusses the topics, the more motivated one is to read and listen further on those same topics. This, at least, was my experience.
Eventually our brains begin to adapt to all this new information. We begin to internalize everything. And finally, we begin to articulate and communicate the very topics we’ve been struggling to learn. They become a natural part of our lives. The more I’ve repeated this process, the quicker I’ve been able to grow into the neuroscience field.
Read & listen <–> Explain and discuss
This cyclic process is appropriate for learning any new field, skill or topic.
Now that you can grasp my approach (I hope), let’s dive into each of these activities further.
Oh man is there a lot to read. Forget research papers at the beginning. I tried to read some and was instantly lost and confused after the first couple sentences of the abstract. Unfortunately, research paper authors aren’t known for their prowess with the written word. I needed a gentle introduction to topics from authors with a refined and polished approach. Amazon’s best seller lists were my new best friend. Reviews on Amazon are extremely helpful in finding quality literature on topics.
I found a slew of books which I immediately began reading. Have I finished any of them? Absolutely not. Thankfully the beginning of these books, and MOOCs for that matter, usually include a general overview of all the relevant topics in a subject area.
By simply completing the first couple chapters, one can develop a basic understanding of the subject presented.
I read these books very very slowly, spending a long time on each page. Sometimes I will re-read a few sentences up to 10 times just to imprint the concept. I will set down the book and try to speak some of the main ideas aloud. I will also copy and paste small sections into OneNote to serve as notes to refer back to in the future. These tricks help to record the subject into memory. However, it also makes it almost impossible to ever finish.
- MATLAB for Neuroscientists 2nd Ed. by Pascal Wallisch
This is a great introduction to MATLAB for most people, whether you’re into neuroscience or not. The authors have written it for those who have never programmed before but who have an understanding of some basic calculus and trigonometry. It has helped me get comfortable with the general syntax and functions available. So far, I’ve used it to help me prepare for my research analysis on visual working memory in monkeys.
- An Introduction to the Event Related Potential Technique by Steven J Luck
I got a lot of value out of the first chapter in this book. I won’t be practicing the ERP technique in my research, but it was very helpful to learn about what it was and when it is useful. The overview was comprehensive enough to establish the context of neural analysis and where the ERP technique fits in.
- Analyzing Neural Time Series Data by Mike X Cohen
I will be utilizing time series analysis in my research, so I was quite excited to read through this book. However, I quickly realized I had no idea about the topics being discussed by just the second chapter. So, I ended up supplementing this book with Luck’s “Intro to the ERP Technique” and Wallisch’s “MATLAB for Neuroscientists”. Cohen discusses the ERP technique as if you should already be familiar with it. So Luck’s book allowed me to familiarize myself with it. Also, he discusses MATLAB as if the reader has used it in the past. There is no introduction to its capabilities or how to use it. For this, Wallisch’s book was the perfect companion. Once I got through the beginning of those two books, I was able to continue reading this one with a much higher level of comprehension.
UPDATE: Dr. Cohen did point out that there is a step-by-step introduction to MATLAB programming in the three scripts corresponding to chapter 4 on his website. You are able to follow along by reading the comments. I would also like to point out that this text is not about ERPs. Dr. Cohen simply mentions it very briefly as a contrast to the analysis that is his focus for this text. I’m just weird and got thrown by seeing a word I didn’t recognize so I spun out and found an entire book on the topic. I don’t recommend doing that.
- The Future of the Mind by Michio Kaku
Wow, I love this book. I saw Michio Kaku on the “Daily Show” in early 2014. I was so inspired by the interview, I immediately got his book. If you haven’t seen the interview, I highly suggest searching for it. The problem is, I sometimes get excited about things, obtain them, then forget about them. Unfortunately, that’s what happened to Kaku’s book. It sat, unread, for about a year and a half. I finally rediscovered it randomly when I was looking for something else in the summer of 2015. I was like, “oh yeah, this guy was cool.” I started reading it while I was still reading the Dune series (which is massive). I’ve barely made any progress in that series since. Kaku’s book changed the course of my entire life. This is the book that inspired me to look into neuroscience. And guess what? I haven’t finished it yet. But, I’m really far! It breaks down the current state of the neuroscience field with just enough details to get you thinking but not enough to overwhelm. Kaku presents technology like sophisticated brain-to-machine interfaces that I thought were pure science fiction. For example, we have computer chips that can be implanted in a human’s brain which allow the human to interact with a computer simply by thinking. Every few pages Kaku hits you with something amazing like that and then explains it like you could go join in this research tomorrow if you wanted. So, if you have any interest in the brain, the mind, neuroscience, AI, or even science fiction, read this book and prepare to be blown away.
- The No Bullshit Guide to Math & Physics by Ivan Savov
Ivan is the man. He writes in a straightforward manner with enough detail speckled in to get the point across. I have a bachelor’s degree in mathematics, and I haven’t used most of what I learned in over 10 years. I found this book extremely helpful in bringing that knowledge back to the surface. If you want to refresh on your math skillset, then see it applied, this is the book for you. He begins from base principles in arithmetic then proceeds to layer on algebra, trigonometry, geometry and calculus. The instruction is delivered at a moderate pace with accessible examples. I suggest reading through the “high school” portion at the beginning because there’s a ton of useful material in there. I certainly didn’t remember it all.
- Matter and Consciousness by Paul M Churchland
This books explores philosophy of the mind. It is a fantastic introduction to the main questions philosophy of mind is concerned with such as “what is a mind?”. I haven’t gotten very far in the text, but I am looking forward to diving deeper into it over time. It is important to consider the questions philosophy of mind raises especially when designing experiments. Doing so allows experimenters to more honestly reflect on why their work is relevant, where it fits into the bigger context of the field and what its limitations are. This is just one of the many reasons it’s important to read this book if you are in the field of neuroscience. Even if you aren’t, Churchland’s delivery is appropriate for anyone with an interest in the subject, regardless of background.
- Statistics 4th Ed by David Freedman
This is the best book on statistics available today. Freedman employs a unique approach whereby he utilizes relevant real-world examples to teach every concept he discusses, from the correlation coefficient to experimental design. As a result, the reader immediately gets an understanding of how concepts are applied. Many times we rely on abstractions to teach concepts. Not so for Freedman. I strongly recommend this text for anyone with an interest in better understanding the subject of statistics.
- Rhythms of the Brain by Gyorg Buzsaki
This text was suggested by a post-doc and Amazon reviews are favorable.
- After Phrenology by Michael Anderson
Anderson’s book was the topic of a very interesting Brain Science podcast episode. In it, Anderson clearly explains topics the book covers. He does such a good job that I had to run out and grab the book.
- Radical Embodied Cognition by Anthony Chemero
This book discusses the idea that our engagements with the world are mediated by our physical bodies. I was introduced to it through another interesting Brain Science podcast episode.
- Nonlinear Dynamics and Chaos by Stephen Strogatz
Strogatz is a brilliant mathematician who is a leading mind in nonlinear dynamics. There is a really cool TED Talk circulating online where he discusses some of the main ideas about chaos theory and dynamical models. I actually began this book, then quickly realized I needed to supplement with additional educational material like Savov’s text before attempting it.
There are two main formats to listen to material online: MOOCs (massive open online courses) and podcasts. I found most of the following list during internet searches. Some of them came through references to each other’s work. There’s a nice little neuroscience internet community out there! In no certain order here they are:
- Neuroscience I, II & III by Harvard via EdX
This is hands-down the single best MOOC about neuroscience currently available online. They utilize awesome interactive illustrations to explain complex systems of interaction in the brain on a molecular and cellular level. The videos are dynamic so you aren’t just looking at a guy talking at you the entire time. It makes for a very effective & entertaining approach.
- Introduction to Complexity by the Santa Fe Institute via Complexity Explorer
So, I didn’t even know this area of research existed. In fact, I think most people do not realize this exists. I think we can all agree that our world has complex systems in it, from the stock market to our brains. These complex systems are very difficult to characterize and make predictions about. It turns out that there is an entire field devoted to studying these kinds of systems. This MOOC will introduce you to the vocabulary and methodologies used in complex systems research. You will learn about really unusual and fascinating topics like chaos theory and strange attractors. The course is thoughtfully constructed. It’s highly educational and interesting. Check out the intro to see if you’ll like it.
- Principles of fMRI by Johns Hopkins via Coursera
fMRI (functional magnetic resonance imaging) is one of the ways we can visualize the activity going on in our brains. It is a popular noninvasive neuroimaging technique used in neuroscience research that allows us to measure and map brain activity. However, it certainly has its drawbacks. If you want to learn about a popular approach to brain imaging, this is a decent MOOC for you. However, the delivery of the content is not as polished as the Harvard or Sante Fe Institute courses.
- Data Science Trek by Johns Hopkins via Coursera (individual courses are free)
This is a collection of multiple courses that train best practices in data science. The information is relevant and delivered in an established order. The courses can be taken individually for free, or you can pay for the entire trek. I picked out a couple of the courses to take for free. It was helpful to understand what best practice is for obtaining, cleaning then analyzing different data sets. The information learned here can be used in any field. I found the delivery of the content in these videos to be of a higher quality than the fMRI course. However, they still weren’t as well-done as the Santa Fe Institute or Harvard MOOCs.
I found neuroscience podcasts on iTunes and listened while driving, cleaning, cooking and eating. They provide insight into the dynamics of social interaction and hierarchies in the world of academia. They will also help you find high-quality literature.
- Brain Science with Ginger Campbell, MD
This is probably the most interesting podcast in the field of neuroscience. I have found multiple books as a result of listening, which are listed under the “Read” section. Campbell is a well-spoken and effective communicator. She discusses relevant topics. Her guests are a pleasure to listen to. I have really enjoyed each episode so far. iTunes Link.
There’s a reason this is the number one podcast in the “Science & Medicine” category on iTunes. Radiolab has compelling stories and impeccable production by WNYC. The only drawback is the content of the episodes are only generally related to science. There is no direct connection to neuroscience here. In fact, the last episode I listened to was about a feud two ice cream sellers had with each other. I really do not know what that had to do with science or medicine, but it was an interesting story nonetheless. iTunes Link.
- The Bench Warmers Podcast
This is a relatively new podcast produced by graduate students at the University of Rochester. I highly recommend this one if you are a current grad student. Their goal is to showcase stories from current grad students to explore what life is like as a grad student in the sciences. They delve into relevant topics like imposter syndrome and the nature of the relationship between PI (principal investigator) and student. I have found it very insightful. iTunes Link.
- Axons and Axioms
I like this podcast because of its philosophical slant. The platform is two guys talking about interesting and entertaining topics in neuroscience. One is a philosopher and the other a psychologist. No frills in the production quality here, but the content carries the show. iTunes Link.
So the academic journal “Nature” has a podcast and this is it. Each month they release a new episode where the host identifies authors of a few different papers for a quick review of their findings. The papers are chosen from the latest published journal from the prior month. It’s a fantastic way to stay up on the latest advances in neuroscience instead of trying to monitor every paper that comes out. iTunes Link.
- On Your Mind Podcast
I found this podcast while researching PhD programs in my area. I saw that the hosts had interviewed a grad student from the FAU – Max Planck program in Jupiter. That’s one of the programs I explored before finding the Center’s program at FAU Boca. So, I listened to his interview and found it very helpful in illustrating what that program was like. I continue to listen to each new episode now. It is based in Canada and run by a couple grad students. They review a paper during part of the show, but it is usually over my head. The other parts of the show involve them discussing what they have found interesting in the field of neuroscience lately. I have found this part of the show to be very helpful. They are the ones who recommended the Bench Warmers podcast and other websites like Useful Science that I’ve subsequently researched and found interesting. Overall, the show produces very relevant content. iTunes Link.
Now that you have read and listened to a ton of new material, it’s time to progress to the next two activities in the learning process.
Explain & Discuss
I put these two activities together because typically you will start off explaining something and your audience, or person you’re talking to, will discuss it with you. There are instances where it is a strict one-way dialog like a video or presentation. However, chances are at this stage in the game you are not ready for any of that yet.
Who do I talk with?
Anyone who cares enough to listen to you. Really. It is important to talk to people both inside and outside the field.
If you ever find yourself struggling to explain a concept to someone who is not in the field, you need to go back and learn more about that topic.
Here is a list of the kinds of people I have been able to speak with:
- Administrators of academic programs
- Speakers at a neuroscience seminar
- Business colleagues
- Significant other
My poor wife was the unfortunate recipient of a number of these discussions. I am thankful that she is supportive enough to listen to my ever-improving explanations of complex topics.
And sometimes, if there is no one to talk to, talk aloud to yourself. Yep, that’s right. You may feel crazy, but speaking as if someone else is in the room will force you to articulate the topic in a rational way. I would find myself doing it regularly at least once a week. I suggest restricting this practice to your own residence while no one is home, however. You need people to at least think you’re normal.
So that is how I prepared for my PhD. The methods I used can be applied when learning any new subject, however. Just follow the process:
- Identify the relevant topics
- Read and listen to material on the topics
- Explain and discuss the topics with others
It is important to continually cycle through these activities, especially those in steps 2 and 3. Eventually a fourth step emerges:
4. Write about the topics
I am currently on that step right now. So, please bear with me. I will get better and so will you!