Tag: Open Data Repositories

My blog post from four days ago was about a paper that described a previously unknown slow integration of action potentials that resulted in the abrupt appearance of action potentials in the distant segments of CA1 hippocampal interneuron axons that continued to fire for tens of seconds to minutes. Their preliminary evidence that gap junctions between axons may be involved led me to pioneering theoretical work by Dr. Roger Traub describing the dynamics of axo-axonal gap junctions and to the recent paper “Mechanisms of very fast oscillations in networks of axons coupled by gap junctions” (published 2010 in the Journal of Computational Neuroscience).

Experiments analyzing the dynamics of axons connected at distal segments by gap junctions were conducted using three different computational models:

• Large network models that included 3,072 axons.
• Small network models that included just a handful of axons for detailed analysis.
• Cellular automata.

In addition, they reproduced and modified the large scale biologically detailed model of Dr. Traub and colleagues from 1999.

Note: All of the computational models, including the modified Traub model, are available from the SenseLab ModelDB repository in the “Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)” record. The computational models are all in the C language except for the cellular automata models, which are in MATLAB code. Data analysis code, which is included, is also written for MATLAB.

The network exhibited three different types of behavior, depending on the gap junction conductance and the fixed somatic voltage:

• externally driven very fast oscillations
• re-entrant activity
• non-oscillatory noisy activity

The results of the large network simulations suggested that the axonal plexus exhibits different behaviors depending on the number of propagation failures in the network. There were a lot of propagation failures during noise, some during re-entrant activity, and none during externally driven very fast oscillations. The failure of action potentials in distant axon branches to reach the cell body has recently been hypothesized to be the cause of spikelets, which have been observed in many types of neurons (see “Axon Segments May Form Distinct Processing Units“).

A major finding of this paper was that highly interconnected distal axon segments (a small subgroup of the total population of axons) were necessary for the emergence of externally driven very fast oscillations and re-entrant activity. These distal axon segments acted as gates like a highly connected node in a small world network. Their results from model networks of electrically coupled cells predict that:

• Target patterns appear across an axon plexus when signal propagation is always reliable.
• Spiral waves appear when propagation fails occasionally.
• Noise appears when propagation failures are frequent.

Interestingly, it’s when the propagation of action potentials fail frequently enough to effectively shut down the highly interconnected axons that the network exhibits noise. The simulations show an abrupt phase change from noise to the re-entrant activity that shows only occasional propagation failure. Re-entrant activity may appear as spiral waves, which have been observed in cerebral cortex of living mammals (see “Spiral Waves in the Brain“). Together these data point to the neuropil as the area where significant signal processing is taking place in the brain.

Other related blog posts:

Spiral Waves in the Brain

Axon Segments May Form Distinct Processing Units

Okay, I’ve spent more time with the paper (and supplementary material) I first mentioned in my blog post three days ago (blog post “Coding in the Brain, Paper Bloat, and the Need to Change the Way Papers are Published“). The authors’ conclusion was that the “cortex is likely to use primarily a rate code.”

Note: The paper is titled “Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex” and was published July 1, 2010 in Nature. Be sure to also look at the 42 pages of supplementary material available here!

On my initial reading of the paper I thought I must have missed the new empirical and/or theoretical evidence that led to their conclusion. As it happens there is none. Their conclusion, which they hedge at several points, is based on a circular argument.

A perturbation was elicited by the researchers, consisting of a single extra spike in one neuron, that produced approximately 28 additional spikes in its postsynaptic targets. The spike produced a detectable increase in firing rate in the local network. The observed amplification was characterized by intrinsic variations in membrane potential on the order of 2.2 to 4.5 millivolts.

The authors concluded that since the additional spike resulted in stimulus independent variations in membrane potential, the variations in membrane potential “are pure noise, and so carry no information at all.” First note that the variations in membrane potential are stimulus independent because the perturbation – the spike elicited by the research team – is stimulus independent.

So really the argument comes down to the following:

• a) Neurons and neuronal circuits are exquisitely sensitive to signals.
• b) The brain, or at least the neocortex, is a noisy system.
• Therefore c) the brain, or neocortex, must be using rate coding.

This adds nothing to the question of what kind or kinds of coding are being used by the cerebral cortex. The same data could be used to make the following argument:

• a) Neurons and neuronal circuits are exquisitely sensitive to signals.
• b) The brain, or at least the neocortex, responds in a spatially and temporally precise manor to a single presynaptic spike.
• Therefore c) the brain, or neocortex, must be using temporal coding.

I’ll conclude with just one more point about the 42 pages of supplementary material and the fact that they did not publish their computational models or data to open online repositories.

The team investigated the relationship between postsynaptic currents and the probability of eliciting an extra spike by constructing a biophysically realistic model of a layer 5 pyramidal cell based on the existing model Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995) published in the SenseLab ModelDB repository. (The model and its associated paper are briefly reviewed in my blog post “Standard Neocortical Pyramidal Neuron Model.”) The authors made three changes to the existing model:

• The temperature was increased from 23 degrees centigrade to 37 degrees centigrade.
• All membrane conductance values were multiplied by 3.
• The passive reversal potential was set to -75 millivolts rather than -70 millivolts.

These changes are easy enough for the reader to make in the original model by Mainen and colleagues. However, the authors specified in the supplementary material that the way they mimicked neuron activity as it would be while the neuron was part of the brain in a living animal “by bombarding the model neuron with synaptic input sufficiently large to generate voltage fluctuations on the order of 3 millivolts at the soma and a firing rate of 2.6 Hertz.” How, exactly, did they set this up so that the reader may replicate their results?

Other related blog posts:

Coding in the Brain, Paper Bloat, and the Need to Change the Way Papers are Published

Standard Neocortical Pyramidal Neuron Model

It was only about 20 years ago when the neuroscience community discovered that dendrites can possess active channels. At that time Dr. Terrence Sejnowski’s team sought to address an apparent paradox seen in recordings from neocortical pyramidal cells. It was clear that these neurons possessed voltage dependent sodium channels on their dendrites that promoted and sustained the propagation of action potentials but they rarely enabled the origin of spikes in the dendrites. December 1995 they published the paper “A Model of Spike Initiation in Neocortical Pyramidal Neurons” in Neuron that described a model pyramidal neuron that took these findings into account and suggested mechanisms underlying the observations.

Note: The pyramidal cell model associated with this paper runs in NEURON and is available from the SenseLab ModelDB repository in the Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995) record. This was one of the models used in the research described in the paper “Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex” (published July 1, 2010 in Nature) discussed in yesterday’s blog post “Coding in the Brain, Paper Bloat, and the Need to Change the Way Papers are Published“.

Sejnowski’s team found that the axon initial segment was critical for spike initiation. A high density of sodium channels in the initial segment provided a very large source current that even supplied a large fraction of the depolarizing current observed in the soma during an action potential. Regardless of the site of stimulation, spikes were initiated at the axon initial segment and subsequently invaded the soma and dendrites.

Note: The cerebral cortex model by Mainen and Sejnowski (1996) extended the standard model described here. For information on their 1996 model please see my blog post “Brain Modeling Using NEURON: Superficial Pyramidal, Deep Pyramidal, Aspiny, and Stellate Neurons.”

Other related blog posts:

Brain Modeling Using NEURON: Superficial Pyramidal, Deep Pyramidal, Aspiny, and Stellate Neurons

Dendritic Signal Processing Simulated Using NEURON

Coding in the Brain, Paper Bloat, and the Need to Change the Way Papers are Published