Category Archives: spikes

On STDP from Behavior

Figure 6 from ref. 1

I’ve written several times about Spike-Timing-Dependent-Plasticity (STDP), one method by which the individual neurons in the mammalian brain learn; changing their responses to the signals sent from other neurons.

It is believed that STDP is a major route of such learning, both during development and in the adult animal; for instance potentially underlying the associative conditioning famously demonstrated by Pavlov. Indeed, it is just this sort of patterned external sensory stimulus (bell then food) that represents a candidate for learning through STDP. However, connecting the presence of structured environmental variables and underlying brain changes has proven a difficult experimental challenge.

A recent piece of research has achieved just such a feat in the optic tectum of a non-mammal, the developing frog Xenopus laevis1.

I found the figure above to be the most intriguing result from the paper sumarrizing these experiments, published in Nature Neuroscience. The image represents the finding that if the tadpoles are exposed to repetitive flashes of light with a specific time difference between them (top), the neurons in their optic tecta respond by adjusting the latency (time from stimulus to response) of their spike-reactions to single, isolated flashes (middle: neural activity, bottom: histograms of spike latencies).

I am encouraged by this work because it bridges what is currently a rather formidable gap. That between understanding the brain at the level of single neurons and the behavior of an animal as a whole.

References:

1. Pratt, KG, Dong, W & Aizenman, CD (2008) Development and spike timing–dependent plasticity of recurrent excitation in the Xenopus optic tectum Nature Neuroscience 11, 467-475 | doi:10.1038/nn2076

On Learning and Time-Scales

Moo Ming Poo, PhD

On Thursday (2/28/08), I attended a lecture, part of the Neurological Institute at Columbia University’s continuing Seminar series. The talk, titled Activity-Induced Modifications of Neural Circuits was given by Moo Ming Poo, perhaps the most active researcher in this fascinating sub-field, from UC Berkeley. The lecture hall, which has seats for perhaps 80 that are never filled at these events, was packed. Standing room was all that was available, with people spilling into the side aisles and a few intrepid souls (the principle investigator in the lab I work in amongst them) seating themselves in the center aisle as well. Eric R. Kandel, James H. Schwartz, and Thomas M. Jessel (the authors of the most widely used undergraduate and graduate textbook on Neuroscience and all professors at Columbia) were in attendance, as well as countless other researchers and graduate students like myself.

The talk was probably so well attended in part because of Dr. Poo’s notoriety, but also because his work is of some universal intrigue, having relevance in all brain areas and a diverse set research programs.

The title of the seminar refers to the property that individual neurons display in changing the strength of the connections (synapses) between each other in a way that depends on their relative activities. Specifically, if neuron A sends a connection to neuron B, the latter cell needs a way to update the importance of A’s input. Neurons communicate by sending aroundspikes, large pulses of voltage, and it is generally not the case (in the mammalian brain) that a single presynaptic neuron (A) can cause a postsynaptic neuron (B) to fire (spike), rather some several hundred or thousand presynaptic neurons must spike at almost the same time to cause a postsynaptic neuron to fire. I say this because it makes clear the subtle specificity that is needed in synaptic modification, to pick out which of these many inputs have useful information for the cell.

The sensible mechanism, called spike-timing dependent plasticity (STDP), works by up-weighting or strengthening synapses from presynaptic neurons that spiked a short time (20 milliseconds) before the postsynaptic neuron, and down-weighting those in which the presynaptic spike came during a short period after the postsynaptic spike. The large-scale analogy (mentioned by Dr. Poo in his talk) is that of Pavlov’s dog: the canine learned to associate the bell with the meat when the sound preceded the reward, but not when the order was reversed.

In his excellent lecture, Dr. Poo presented some convincing results concerning the molecular mechanisms that might be at work on a microscopic level to achieve this effect, but he concluded with a different and stimulating point.

He presented data from experiments that had been conducted by a post-doctoral researcher in his lab, German Sumbre, using Zebrafish. The results indicated that even very young members of this species are able to learn a predicable series of 60 or so light pulses, delivered every 0.5, 1 or 4 seconds, as indicated by their immaculately timed execution of an escape response called a tail-flick for two or so extra intervals beyond the conclusion of series, right when the pulses would have arrived.

That the fish were able to do this is not incredibly surprising, many animals display this type of predictive behavior. However, it is entirely mysterious how this might be happening at a neuronal level. Dr. Poo has made magnificent progress in understanding how learning proceeds on very short time-scales (10s – 100s of milliseconds), but it is still quite unclear how learning of longer-period phenomena might be achieved by the nervous system. In fact, Dr. Poo appealed to the audience, saying: “If anybody has any ideas how this might be studied, I am anxious to hear them.”

Mirroring the urgency apparent in Dr. Poo’s request, there was a paper published recently on this very topic showing that amoeba are capable of just this sort of learning of intervals1. No doubt this will be a hot topic of research in the near future. Further, this sort of example shows us both how far we’ve come in understanding brains, and the chasms yet to be bridged in moving forward.

References

1. Saigusa T, Tero A, Nakagaki T, Kuramoto Y. (2008) Amoebae anticipate periodic events. Phys Rev Lett. 100(1):018101