A programmer who is obsessed with giving experimenters
a better environment for developing biologically-guided
neural network designs. Author of
an introductory book on the subject titled:
"Netlab Loligo: New Approaches to Neural Network
Simulation". BOOK REVIEWERS ARE NEEDED!
Can you help?
Linguists have recently discovered [1] that almost all words are metaphorical at their base, and some people (e.g., me) posit that they all are. Though speculative, it is at least conceivable that even the sub-language signaling in the brain, which eventually leads to language, is also metaphorical. Consider that the bell becomes a metaphor for food in the mind of Pavlov's dog.
Language is also able to relate ambiguity about the concepts it conveys. The word “life,” for example, can mean life-biology, or life-consciousness. Up until now, it has been perfectly acceptable to use these two meanings interchangeably. There simply has never been an instance of consciousness that existed outside of a biological body — at least none that we could directly experience with our physical senses.
The book on the Netlab project often returns to the notion that learning is merely a form of adaptation and that, conversely, adaptation is merely a form of long-term learning. This, in turn, all fits under the umbrella notion that memory is behavior.
The idea that learning is adaptation is learning is forwarded as a possibility, mainly as a better means of discussing the concepts. This (in my opinion) provides a clearer and more converged understanding of how memory works in biological organisms. This could be very wrong, of course, so it's important to describe it properly. That way it, and not a straw man, can be critiqued. This article represents one such attempt to properly describe it. . .
Batesian Mimicry
Batesian mimicry is when a non-noxious/poisonous plant or animal projects the appearance of a poisonous plant or animal, allowing it to avoid being eaten by predators.
Those predators, goes the logic, which have partaken of the poisonous organism and survived, would have become very sick, and would have learned to avoid ingesting anything that appears to be that organism in the future. This will include those organisms who are not poisonous, but merely look, or act, like the poisonous organism.
Scientists at UC Berkeley have taken brain scans of subjects in an fMRI machine while they watched a movie clip. They then reconstructed the movie the subjects were watching using only the brain scan data, and a database of 18 million seconds of random video gleaned from the web.
First, they used fMRI imaging to measure brain activity in visual cortex as a person looked at several hours of movies. They then used those data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set). Next, they used fMRI to measure brain activity elicited by a second set of movies that were also distinct from the first set. Finally, they used the computational models to process the elicited brain activity, and reconstruct the movies in the second set.
The amount of new understanding this could allow us to gather about mind-brain correlates and first person knowledge should be considerable. If this lives up to the hype, a lot of new research ideas should come out of it. Keeping fingers crossed here.
In the above clip - the movie that each subject viewed while in the fMRI is shown in the upper left position. Reconstructions for three subjects are shown in the three rows at bottom. All these reconstructions were obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. The reconstruction at far left is the Average High Posterior (AHP). The reconstruction in the second column is the Maximum a Posteriori (MAP). The other columns represent less likely reconstructions. The AHP is obtained by simply averaging over the 100 most likely movies in the reconstruction library. These reconstructions show that the process is very consistent, though the quality of the reconstructions does depend somewhat on the quality of brain activity data recorded from each subject. [source: Gallant Lab (see resources below)]
From the press release:
“Once infected by spores, the worker ants ... leave the nest, find a small shrub and start climbing. The fungi directs all ants to the same kind of leaf: about 25 centimeters [(9.8 inches)] above the ground and at a precise angle to the sun (though the favored angle varies between fungi). How the fungi do this is a mystery.”
The Netlab development effort has led to a new method and device that produces learning factors for pre-synaptic neurons. The need to provide learning factors for pre-synaptic neurons was first addressed by backpropagation (Werbos, 1974). The new method differs from backpropagation in that its use is not restricted to feed-forward only networks. This new learning method, called Influence Learning, is described here and in other entries in this blog (see Resources section below) .
Influence Learning is based on a simple conjecture. It assumes that those forward neurons that are exercising the most influence over responses to the immediate situation will be more attractive to pre-synaptic neurons. That is, for the purpose of forming or strengthening connections, active pre-synaptic neurons will be most attracted to forward neurons that are exercising the most influence.
Perhaps the most relevant thing to understand about this process is that these determinations are based entirely on activities taking place while signals (stimuli) are propagating through the network. Unlike backpropagation, there is no need for an externally generated error signal to be pushed through the network, in backwards order, and in ever diminishing magnitudes.
Support In Biological Observations
While influence learning in artificial neural network simulations is new, it is based on biological observations and underpinnings from discoveries made over twenty years ago. One of the biological observations that led to the above speculation about attraction to the exercise of influence was discussed briefly in the book The Neuron: Cell and Molecular Biology.
An experiment described in that book shows what happens when you cut (or pharmacologically block) the axon of a target neuron. In that experiment the pre-synaptic connections to the target neuron began to retract after its axon was cut. That is, the axons making presynaptic connections to the modified neuron went away when it no longer made synaptic connections to its own post-synaptic neurons.
The book also described how, when the target neuron’s axon was unblocked (or grew back), the axons from presynaptic neurons immediately began to reform and re-establish connections with the target. Based on these observations, the following possibility was asserted.
"...Maintenance of presynaptic inputs may depend on a post-synaptic factor that is transported from the terminal back toward the soma."
The following diagram depicts these observations schematically.
Stanford University School of Medicine has developed a relatively simple new imaging technique that provides a very exact way to capture the synapses of a connectome with pinpoint 3D positional accuracy, and considerable contextual resolution.
Stanford has performed a study (see below), which was admittedly done primarily just to showcase the new technique. That said, the study managed to produce a very impressive new find.
“
In the course of the study, whose primary purpose was to showcase the new technique’s application to neuroscience, Smith and his colleagues discovered some novel, fine distinctions within a class of synapses previously assumed to be identical.
”
One of Netlab's synapse mechanisms and structures is based loosely on a silent-synapse hypothesis of long- vs short-term memory, in which short and long both occur at the same connection-point (synapse). Netlab includes a learning method based on this as well, called weight-to-weight learning. The silent synapse phenomenon has been observed for quite some time in biological studies, and there has been very good evidence to explain some of the underlying mechanisms responsible for the observation. Still there have been many missing pieces to the puzzle.
An Interesting Study
Recently there has been a development that seems to give evidence and details to a related theory/hypothesis of how synapse strength may be mediated through a molecular motor called Myosin II on the post-synaptic side. So suggests one study out of The Scripps Research Institute.
It has been thought for some time now (see background information below) that molecular motors resembling those used to produce movement in muscle tissue, may be a major player in the processes mediating the transfer of memory-connections from short-, to long-term on the post-synaptic side. We now seem to be getting to more detailed understanding of the mechanisms underlying these phenomena. Like so many brain constructs, there does seem be a great deal of variety.
The vernacular that seems to be emerging is that these mechanisms "stabilize" the connection strengths. This might still be jumping the gun on the conclusions, but it is not a bad way to think about it for now.
Gavin Rumbaugh
Are you ready to Rumbaugh? i'm sure he's never heard that joke before
Related/Background:
Remodeling the Plasticity Debate:
The Presynaptic Locus Revisited
A really interesting paper from 2006 published at the journal Physiology. From its description: "The cellular mechanisms contributing to long-term potentiation and activity-induced formation of glutamatergic synapses have been intensely debated. Recent studies
have sparked renewed interest in the role of presynaptic components in these processes. Based on the present evidence, it appears likely that long-term plasticity utilizes both pre- and postsynaptic expression mechanisms."
Actuators (scroll to bottom)
A blog-post here about actuators. Mostly robotics, but a section at the bottom has a couple of nice videos
describing the function and structure of animal muscles.
Stand Out Publishing
