Top Gun taught us that the best and brightest pilots can perform some amazing aerobatics.  Nobody seems surprised that a good pilot, with some practice, can move seamlessly from the flight maneuvers used on a Boeing 747 to those featured in Blue Angels shows.  While computer autopilots have performed well in commercial aircraft for some time, however, getting an electronic computer to pull a plane successfully through an aerobatic maneuver is almost impossible, and is thus a relatively new field of research. Read the rest of this entry »

One of the major themes in the SyNAPSE project is developing chips that can learn meaningful information, and preserve it over time. In other words: memristors can learn, but we need to ensure that they are stably learning something useful for the system they are embedded in.

Some help to solve this technological problem comes from neuroscience. The question of how can the cerebral cortex develop stable memories while at the same time incorporating new information through an organism lifetime has been a central theme in many research groups. The talk posted on Neurdon describes one of these approaches. Read the rest of this entry »

Marvin Minsky has decided to resuscitate AI from the 80’s ashes with a fresh $5M grant to support an MIT team in a “project to build intelligent machines”. More info here. I have strong doubts on Minsky’s approach, and the new Turing test: “can the computer read, understand, and explain a children’s book”. I would be satisfied with replicating the children…

Ever wondered what neurons do to each other? How does a signal generated in one neuron cause a reaction in another neuron? Neurons behavior is fairly complex (see this post), but with some simplification we can begin to understand, and model, how neurons affect each other and ultimately determine information processing in the brain. Read the rest of this entry »

In collaboration with Robert Thijs Kozma

Robert and I thought that it would be nice to finally define what the main building block of what we are talking about is! What are neurons, and how do they work? How do these relatively simple processing elements give rise to higher perceptual and cognitive functions? We are not going to answer these big questions in this post, but we have to start somewhere…. Let’s take a closer look at what is a neuron, how a simple mathematical model can capture a remarkable spectrum of neuron’s behavior, and let’s look at some simple MATLAB code that would allow neurdons to run a neuron in MATLAB at the end of this post. Read the rest of this entry »

I just finished reading Complexity: A Guided Tour by Melanie Mitchell. The book is meant to be an introduction to complexity theory for the general reader.

The book works as a lucid review of many interesting topics in science and mathematics. I’d read Mitchell’s book on genetic algorithms, and she’s a gifted writer. Here she explores (among other things) dynamical systems, chaos, information theory, genetic algorithms, cellular automata, analogical reasoning, and network theory. She does a great job explaining difficult concepts in a clear manner.

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“You think you know when you learn, are more sure when you can write, even more when you can teach, but certain when you can program.” Alan J. Perlis, Yale University

Neurdons cannot agree more. Reading and writing about neuroscience is not nearly as fun as creating a pulsing neural model! Recently, the Technology Lab at the Department of Cognitive and Neural Systems, where Neurdon was founded, has started to post a number of software tools, most of them in MATLAB, ranging from neural simulation software, to simple neural models, to biologically-inspired machine learning and machine vision tools. Read the rest of this entry »

Another guest editor here… I met Max at this year’s ICCNS and he suggested writing a guest entry for Neurdon. The ideas hopefully compliment some of the stuff Ethan blogged about.

I’m a 4th-year PhD student in the Institute of Cognitive Science at The University of Louisiana at Lafayette. When I entered the program, I was mostly interested in AI and evolutionary algorithms. I wanted to evolve a Go-playing program. But my interests shifted, especially in my first year when I read Jeff Hawkins’ On Intelligence. I thought it was great stuff, and I liked two things central to his framework: 1) The temporal aspect of cognition, and 2) The crucial role of feedback. He made a convincing case that every modality and skill is essentially a matter of learning and processing sequences. So that’s where I started focusing my attention. Read the rest of this entry »

Max asked me to post some information about how time could act as a ‘supervising’ learning signal to create invariant representations in IT (particular in reference to Jim DiCarlo’s work in this area). Since I am lazy, the below post is a modified section of the background from my thesis proposal – hopefully it’s not too boring….

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The challenge of building, within a few decades, a computer chip on the scale of a patch of biological cortex is a race involving many labs in academics and industry around the world.

The basic assumption is that, in order to build machines that imitate the cortex, the intuitive way to go is capture in a chip the architecture and functional principles of cerebral cortex. Building a chip that emulates the cortex needs to solve several challenging problems. For example, how can you pack millions of processing elements and billions of synapses into a small enough chip and be able to perform computations at a speed compatible with human thought. All this must be done without consuming a lot of power. Easy, right? Read the rest of this entry »