neurodudes

How do crickets know when they are chirping?

These questions appear to be answered with the discovery of a motor interneuron in the cricket that is resposible for “corallary discharge” or forwarding neural signals from motor systems to sensory systems. By inhibiting auditory neurons during chirping, the animal can “counter the expected, self-generated sensory feedback”.

Over at the synapse blog, it is pointed out that the cerebellum may have this function in vertebrates.

Henry Markram, the director of the IBM-sponsored Blue Brain Project has written an article in the latest issue of Nature Reviews: Neuroscience that provides the most technical details about the project to date.

From the article:

“The three-dimensional neurons are then imported into BlueBuilder, a circuit builder that loads neurons into their layers according to a ‘recipe’ of neuron numbers and proportions. A collision detection algorithm is run to determine the structural positioning of all axo-dendritic touches, and neurons are jittered and spun until the structural touches match experimentally derived statistics. [...] Probabilities of connectivity between different types of neuron are used to determine which neurons are connected, and all axo-dendritic touches are converted into synaptic connections. The manner in which the axons map onto the dendrites between specific anatomical classes and the distribution of synapses received by a class of neurons are used to verify and fine-tune the biological accuracy of the synaptic mapping between neurons. It is therefore possible to place 10–50 million synapses in accurate three-dimensional space, distributed on the detailed three-dimensional morphology of each neuron.”

–Stephen

This project was announced several months ago, but I didn’t see a post here so I thought I would add it.

The project, dubbed “Blue Brain“, represents a team up between Henry Markram, (who co-authored the chapter on the neocortex in the acclaimed reference The Synaptic Organization of the Brain), and IBM’s Blue Gene super computer.

From the New Scientist article: For over a decade Markram and his colleagues have been building a database of the neural architecture of the neocortex, the largest and most complex part of mammalian brains.

Very thin slices of mouse brain were kept alive under a microscope and probed electrically before being stained to reveal the synaptic, or nerve, connections. “We have the largest database in the world of single neurons that have been recorded and stained,” says Markram.

–Stephen

A neat paper from 1999 that I saw.

This post is mostly identical to the corresponding page on NeuroWiki. You may wish to read/discuss it there instead.

This paper presents a leaky integrate and fire model which adapts to the average rate of incoming spikes. The model has two modes, integration mode and coincidence detection mode.

Specifically, the model is an extension to the Morris-Lecar model in which maximal conductance changes over time according to a simple calcium dynamics model. This change allows the neuron to adapt to different average rates of input.

Interestingly, directly after you change the average rate of presynaptic input, the neuron may be transiently pushed into a different mode. Specifically, if you bump up the level of activity, the neuron is pushed towards coincidence detection mode, and if you suddenly decrease the level of activity, the neuron is pushed towards integration mode.

The paper also contains a bunch of citations to introduce the spike rate vs spike timing code debate.

I’m not quite sure if the neuron’s “default” mode (that is, when the average rate of incoming spikes is fixed) is always the integrator mode, or if you can change that by changing the parameters. I don’t think there’s any hysteresis (that is, I don’t think that the neuron gets “stuck” in one mode or another; I think it only switches modes transiently and then returns to its default slowly over time as it adapts), but I’m not sure. Anyone care to clarify?

Michele Giugliano, Marco Bove, Massimo Grattarola. Activity-Driven Computational Strategies of a Dynamically Regulated Integrate-and-Fire Model Neuron. Journal of Computational Neuroscience 7(3): 247-254 (1999)

Pach clamp experiments on pyramidal dendrites support a model where a single pyramidal cell is equivalent to a 2-layer AI-style neural net.
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