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Spiking associative memory using STDP

Posted by joyboseroy on June 21, 2009

Is it possible to build an associative memory (say Correlation Matrix Memory or CMM) using Leaky Integrate and Fire style spiking neurons that uses the STDP learning algorithm? It should be able to associate a time ordered sequence of spike pulses (forming a neural code) with another, such that on giving the first sequence of spikes as input we get an output sequence of spikes that can be decoded into the associated sequence.

Some ideas:

My previous approach: Hebbian learning, use CMM with rank order codes (weight matrix of 1, alpha, alpha squared etc) and new weight matrix is max of old and the transpose of the two vectors to be associated. Dont know if this is true STDP approach, or if others have tried something like this, or if this is biologically plausible.

Just extending this: a model of associative memory that can be microscoped up to sequence memory level, sequence memory applications level, down to neuron level, spike level, ultimately Calcium and Na ions level.

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Books on neuroscience

Posted by joyboseroy on October 29, 2007

Some very good neuroscience books I have connected with are as follows:

Neuroscience: exploring the brain by Bears, connorts and Paradiso. I bought this book from a second hand bookstore. Its an undergraduate level book. I liked the illustrations, lucid explanations (esp in first edition), spotlight on research techniques like FMRI and EEG, inspiring interviews with leading scholars in the field. Here is the website.

Neurophilosophy by Patricia Smith Churchland. Is got a very good historical overview of the whole field and how different fields like neuroanatomy, physiology, neuroimaging, philosophy are related and how developments in these fields reinforce each other.

Neural networks and brain function by Rolls and Treves. Its a kind of classic, showing how the NN theory links to biological data on different brain regions. Fascinating to read about how computer techniques help and are inspired by biology. Its website is this.

Fundamentals of Computational neuroscience by Trappenberg. This is so good because its so simple and un-intimidating and reasonably lucid, yet is a very good stepping stone for anyone wanting to get into the subject. Good overview of many diefferent issues and tools and branches and fields covered in the interdisciplinary area of comp neuro. The website is here.

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