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 »

When I read (and wrote about) the recent controversy between Modha and Markram, I had this inescapable déjà vu feeling….weird, where did I hear that somebody already simulated a “brain” of the scale of the human brain? Of course!…. Eugene Izhikevich, a very bright (and VERY funny) neuroscientist that, in 2007, visited our center CELEST. During that visit, he showed what at that time (in 2005, and may be up to today) was one of the “largest scale” neural simulation. Read the rest of this entry »

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 »

“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 »

All this Neurdon hullabaloo over memristors and Kurzweilian futurism has got me thinking about the inevitable media question concerning all this: Will our RoboSlave Bots learn to love us in a somewhat creepy, Haley Joel Osment “Artificial Intelligence: AI” kind of way? In other words, will humans be able to one day produce conscious, silicon-based offspring? There are obviously a cornucopia of contingencies when discussing artificial sentience, however, I am going to not-so-subtly sidestep all the philosophical snafus and approach the problem from a modeler’s POV. Read the rest of this entry »

A recent article on the WSJ (In Search for Intelligence, a Silicon Brain Twitches) reviews the Blue Brain project based at the École Polytechnique Fédérale de Lausanne in Switzerland. The Blue Brian project, led for the last four years by Henry Markram, has focused in building a biologically accurate rat cortical column. 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 »

Feinerman et al. Figure 1b: logic components fabricated from hippocampal neurons

Neuromorphic technology is a young field, with little in the way of established paradigms or techniques. Most of the recent related work, however, focuses on silicon implementation of neural-inspired mechanisms. Feinerman et al. buck the trend and build reliable computation devices using actual neurons.

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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 »