Jimbo et al '99: plasticity at the network level in culture

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn 🙂 ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (“potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (“depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.

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Amplification using recurrent connectivity

This post has much the same content as this NeuroWiki page; you may wish to read and comment on it there.

I’ve only skimmed this interesting article, so beware that I may not correctly understand it.

This article proposes that recurrent excitation in cortex leads to amplification, and analyzes this using the mathematics of basic amplifiers (taught in introductory electrical engineering courses; i.e. open-loop gain and closed loop gain).

They construct a simulation based on this principal that agrees with some electrophysiological and pharmacological results from neurobiology experiments in layer IV of V1.

At the end, they conjecture that a sensory network could use this principal for noise reduction and possibly pattern recognition.

Douglas, Rodney J.; Koch, Christof; Mahowald, Misha; Martin, Kevan A. C.; Suarez, Humbert H. Recurrent Excitation in Neocortical Circuits. Science, Volume 269, Issue 5226, pp. 981-985.

Real-time feedback in MEAs: review paper

This is a review paper by Steve Potter, Daniel Wagenaar, and Thomas DeMarse on real-time closed-loop feedback control applied to neuronal cultures using MEAs. That is, you stimulate the cultures with a pattern that depends upon what you’re reading from them. This paper seems to be targeted at people who want to start doing these sorts of experiments; most of it is a very readable overview on how to setup a rig to do this, with pointers to other papers that cover the specifics. However, there are a couple of pages summarizing recent research using these techniques.

I’d recommend reading this paper if you want to setup a rig to do this; otherwise, I’d recommend reading pages 18 and 19.

S. M. Potter, D. A. Wagenaar, T. B. DeMarse. Closing the loop: Stimulation feedback systems for embodied MEA cultures. In: Advances in network electrophysiology using multi-electrode arrays, M. Taketani, M. Baudry, eds. Kluwer, New York. In press.

Activity-Driven Computational Strategies of a Dynamically Regulated Integrate-and-Fire Model Neuron

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)