The Materialistic Mind – Your Brain’s Ingredients | Synapses, Neurons and Brains | Coursera

Hi everyone,

Last week, I continued watching the “Synapses, Neurons and Brains” course from Coursera.org. The second lesson was about “The Materialistic Mind – Your Brain’s Ingredients”, which discussed the structure of the nervous system, the neuron doctrine and the theory of dynamic polarisation. It was a very interesting lecture that compared the ideas of two great minds: Camillo Golgi and Santiago Ramón y Cajal.

Although I had had M.D. classes that explained about neuron cells, axons, dendrites, synapses and everything else contained in the structure of the nervous system, it was enlightening to have a thorough explanation of the structure, the way neurons are “connected” not only in local but also in different regions of the brain (e.g., from frontal lobe to temporal lobe).

It was also interesting to understand there are different neuron types based on different classification methods (e.g., anatomical features, functional features, electrical activity pattern, chemical characteristics or gene expressions), but they all share the same components: soma, axon and dendrites, which all contribute to the communication and flow of information (electrical activity) that runs in the brain.

If I was to point some interesting questions, they would be:

  1. Is it possible that our conscious, knowledge and memories are not stored in our brain but are simply the result of the electrical activity going through predefined circuits?
  2. And that learning may be creating new paths through the trillions of paths that are inactive or nonexistent?
  3. If I was to develop a new Genetic Neural Network, using this assumption – one that changes not only the weight of its nodes but the structure of the network itself – is it okay to assume the network would be able to learn instead of being trained?

Sincerely,

 

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Brain Excitements for the 21st Century | Synapses, Neurons and Brains | Coursera

Hi everyone,

I’ve started watching the “Synapses, Neurons and Brains” course, from Coursera.org, with Idan Segev and Guy Eyal. The course presents current “brain-excitements” worldwide and acquaints students with the operational principles of neuronal “life-ware”. It also highlights how neurons behave as computational microchips and they constantly change.

There are 9 lessons over 10 weeks. The first lesson was about “Brain Excitements for the 21st Century” and it presented some current projects like: the Connectomics, the Brainbow, Brain-Machine/Computer Interfaces’ concepts and challenges, Optogenetics, and the simulation of the brain – The Blue Brain Project.

The lesson provided a lot of references for future research (projects and researchers) and introduced interesting facts and concepts. Regarding my Nerve Gear research, the Connectomics and BMI/BCI projects seemed to be coupled tightly with my goals. And the challenges introduced in the BMI section are part of my future work, or so I hope.

The Connectomics project aims to create a complete 3D reconstruction of the brain, a “blue print”, which can connect the structure to the behaviour/function of the brain, and enable realistic computer simulations.

The Brainbow projects aims to create a structural basis for learning in the brain, allowing us to know how the brain learns in real-time; it also aims to tag and create a genetic-characterisation of the different cell-types; finally, it aims to trace short and long range connections in brain circuits.

Brain-Machine Interfaces will be covered in future lessons as well as in different posts, since its the main focus of this Nerve Gear research. Regarding the challenges introduced in this lesson, there are: (1) Develop chronic brain nano-probes; (2) Develop telemetric communication with the brain; (3) Develop real-time multi signal processing methods; and (4) Improving robotic arm and “Closing the loop” Stimulation + recording.

The Optogenetics project aims to optically stimulate and record the activity from single neurons in the living brain, using genetically modified cells that react to light variation.

Finally, the Blue Brain Project is a computer simulation of neuronal circuits that aims to integrate anatomical and physiological data to provide a better “understanding” of the brain, using the “Blue-Gene” IBM Computer to create mathematical models of neurons’ spiking activity, connect model components as in real cortical circuits, and simulate electrical activity.

 

Next lesson will be about “The Materialistic Mind – Your Brain’s Ingredients”.

Sincerely,

 

Back to Square One

Hi everyone,

It has been too long since my last post. Here’s a recap of 2016 so far:

  • I started this project in January while looking for job opportunities;
  • I started a recruiting process in February;
  • In March, the company started the internship process;
  • In April, I was still looking for a job; they were still processing my internship;
  • I worked for a start-up in May;
  • I left the start-up in June (it wasn’t what I was looking for);
  • I finished all the work I had left in BEST, in June/July, except for EBEC;
  • I went to Belgrade, Serbia, in August to finish my mandate as EBEC PR Manager; I also met with 22 European students in BEST Porto Summer Course 2016, BeSmart – Shape the City.

I cannot start a PhD right now for lack of resources so, I’m looking for more job opportunities. In the meanwhile, I’ve wondered about doing another M.D. in either Digital Marketing or Game Development.

Nevertheless, I’m available, which means I can restart this blog. Problem is: I’m back to square one – I need to refresh what I’ve learned, create all my mind maps, connect all the dots and start over.

Yet again, I’m looking for a job to support my plans, which means all my availability can disappear suddenly. If not, here is the plan: (re)start researching about Brain-Computer Interfaces, Computational Neuroscience (Neuro-Informatics); re-watch the TV Series “Through the Wormhole” and comment about it.

Until next time, またね

Sincerely,

 

Computational Neuroscience

Hi everyone,

After a long period of job hunting, volunteering work and some other professional, social and personal affairs, I am back. These last few months helped me to further define what I want to follow in the next years and where I think my research may lead me.

After discussing my goals with a friend, I enrolled in a Computational Neuroscience course, at Coursera, by PhD Rajesh P. N. Rao and Adrienne Fairhall, here.

I divided the course in its several weeks so I can check and work on it until September. And after that, only the future knows where I’ll go.

I’ve also started researching universities to do a PhD myself and, although I like the idea of staying in my hometown and synchronise/finish my goals altogether, perhaps my future is out there.

So, between Neuroinformatics and my pursuit of happiness, or Informatcs and Computer Engineering PhD, I can only tell a few steps ahead. For now, I need to work and gather the resources to change the world.

See you next time

Through the Wormhole

Hello,

I’ve recently started to watch the TV Series “Through the Wormhole with Morgan Freeman”. I’ve fell in love with the amazing content and wonders of the universe, the cosmos, the quantum, or the deepest corners of the human brain.

My goal is to share my research time with the episodes and balance my publications. Some of them will maintain focus on Brain-Computer Interfaces and Virtual Reality, while others will start to focus on “Space, Time, Life itself”.

I am also monitoring the impact that a feasible job opportunity will have on my schedule so, perhaps I will change my agenda and post twice a week: one article about my research and one article about my perspective on the series topics.

Neural network classification of late gamma band EEG features

It has been a while since my last post. I guess job hunting has an higher priority, at the moment but, today, I was able to go through another paper. Today I read “Neural network classification of late game band electroencephalogram features” from Ravi, K V R, and Ramaswamy Palaniappan (2006).

I’ve always been fascinated with artificial intelligence and, especially, the way we try to recreate the human brain neural network. In this study, Neural Networks are used to classify individuals, much like current biometric sensors, iris or face recognition systems or vocal/sound recognition techniques.

It’s a different study from the previous ones I’ve read. I enjoyed reading how EEG data, after much development, is able to provide features that can be used to classify and identify individuals. The techniques used in this study are worth future study from my side, like the Principal Component Analysis (PCA), the Butterworth forth and reverse filtering, or the Simplified Fuzzy ARTMAP (SFA) classification algorithm.

It was nice to understand how new approaches stand against old approaches, like in this study, where the authors concluded that Back-propagation  (BP) algorithm prevailed a better method than SFA. Nonetheless, it’s also true that development on specific parts (preprocessing, signal processing, data analysis) can be enhanced and, perhaps, change the outcomes of future experiments.

On a similar approach, I’d like to make one or two readings on Genetic Neural Networks.

Perspectives of BCI by 2006

Today I read another article: “Brain-machine interfaces: past, present and future” by Mikhail A. Lebedev and Miguel A.L. Nicolelis.

They analysed Brain Machine Interfaces (BMIs) through a study of past research and analysis of experimental tests, both on human subjects and on monkeys or rats. And they highlight some obstacles that need to be cleared before BMIs can achieve the potential it has to improve the quality of life of many, especially, in the use of prosthetics.

They classified BMIs as Invasive and Non-invasive. The latter is supported by recordings of EEG from the surface of the head without needing brain surgery, and it provides a solution for paralysed people to communicate. However, neural signals have a limited bandwidth.

Within Invasive BMIs, which implants intra-cranial electrodes with higher quality neural signals recording, there are Single Recording Site and Multiple Recording Sites methods. Both these approaches can then be applied to small samples, local field potential (LFPs) or large ensembles.

Their conclusion suggested that in the upcoming 10—20 years, development of neuroprosthetics would allow for wireless transmission of multiple streams of electrical signals, to a BMI capable of decoding spatial and temporal characteristics of movements in addition to cognitive characteristics of intended actions.

The goal would be for this BMI to control an actuator with multiple degrees of freedom that could generate multiple streams of sensory feedback signals to cortical and/or somatosensory areas of the brain.

My conclusion is that after 10 years, we have seen a lot of improvement in this field. For example, in a recent article on the New Scientist website, entitled “Bionic eye will send images direct to the brain to restore sight”, Arthur Lowery, from Monash University in Clayton, Victoria, is working on restoring sight to blind volunteers with a bionic eye capable of providing around 500 pixels image. Read more here.