neurodudes

Computational neuroscience is a field where many successful researchers have a strong physics background. So far, the physics approach has provided a strong foundation from which to understand the brain. Recently, however, the influence of a computer science perspective has become more prominent. How can we understand the different perspectives that these disciplines bring to the field? Can we observe the influence of physics methodologies on the modern study of the brain? And if so, what is the consequence of our understanding of the brain through the lens of physics versus the lens of computer science?

One consequence may be the way that computational neuroscience models time in the brain. The study of physics generally conceptualizes time as continuous. Time is something to be plotted on the x-axis of a graph where some other quantity of interest is plotted on the y-axis.

In computer science, on the other hand, real time is rarely conceptualized explicitly. Computer scientists do not plot quantities against time unless they are profiling software for performance purposes, and even then, time is more generally thought of as number of operations. Thinking about operations generally leads computer scientists to think about time as discrete events.

I posit that the distinction between continuous and discrete time creates a foundational difference between the physics approach and the computer science approach to understanding how the brain works. Due to the discrete time conceptualization, computer scientists are more comfortable explaining the function of brain systems in terms of chains of events with definite beginnings and definite ends. Physicists, on the other hand, are more comfortable explaining the brain in terms of dynamics, which do not require definite beginnings or definite ends. Computer scientists care more what the consequence of an event is in the brain, whereas physicists are more concerned with an concise account of the dynamics of what is occurring.

This divide is visible in the distinct modeling approaches of neurons that derive from these two disciplines. The canonical neuronal model contributed by the physics philosophy is the multi-compartmental conductance based (Hodgkin-Huxley like) model. This model is concerned with matching waveforms of current and voltage traces with those that are measured in real neurons. This model helps us to understand how changes of the properties of excitable membranes over time result in changes of neuronal behavior over time. The computational complexity of these models is thought to prevent more than a few hundred neurons modeled in this way from being analyzed concurrently.

Alternatively, the canonical neuronal model contributed by the computer science philosophy is the integrate-and-fire neuron. This model does away with modeling conductances explicitly as functions of time and simply performs a weighted sum of its inputs at each time step. Here a time step is a discrete event whose duration is a parameter of the model. The simplicity of this model allows large networks to be constructed, which are useful for modeling systems of many thousands of neurons.

The physics approach provides insight into the activity of single cells and small networks, whereas the computer science approach provides insight into the activity of large networks. Neither approach is optimal and neither approach provides all the tools that are necessary to truly understand the brain. As these two perspectives are better understood, the field of computational neuroscience can benefit from finding creative ways to merge these two conceptions of time into models that capture both small scale and large scale neuronal activity.

In conclusion, I have demonstrated that what begins as a division between discrete and continuous time amounts to a divide between a bottom-up and a top-down approach. Furthermore, I have shown that understanding the relative contributions of different sciences to computational neuroscience is important for understanding the paradigms that pervade the field.

Transcranial magnetic stimulation (TMS) is a popular technology for stimulating human cortical neurons, due to its safety, noninvasiveness, and efficacy. A TMS device is just a little coil of wire, through which 10,000 Amps of current is cranked during a period of only a few hundred microseconds; the resultant rapidly-changing magnetic field induces eddy currents in the brain. Depending on the protocol used, TMS can drive/inhibit a region of cortex corresponding to roughly a cubic centimeter or two, and is being explored for the treatment of depression, the reduction of auditory hallucinations during schizophrenia, and the alleviation of tinnitus and migraines. Thousands of papers on medicine and psychology have been written using this tool.

Yet the device itself is expensive and rare — they can run from $20,000 to $50,000 or even more, despite the fact that they are, in essence, a coil, a switch, a bank of capacitors, and a power supply. Much of the art lies in making the devices safe and fail-proof. Is it possible to hack/engineer a system that is safe, fault-tolerant, efficacious, and inexpensive? And furthermore, can we facilitate a community that will devise such devices, and share information about protocols and approaches to brain hacking?

This past August at Foo Camp, a hackers’ conference in Northern California, a group of people got together and set out to do just that. We are designing a safe, noninvasive, modular, and “open source” brain stimulator that will open up the field of circuit modulation to a wider audience. Members of the group include therapists and mental health professionals, engineers, programmers, and others interested in either the development of such devices, or the sharing of information on this front. Key to the design is safety — we want to make sure that the devices we create are as safe as devices on the market. Also, all the information is released under the Creative Commons “Attribution and Sharealike” license. This is a new model for “open source” medical device development — which may move it beyond the domain of simply creating “cool toys,” and to creating real devices.

You can find out more information, or contribute to the project, or learn from the project, at
http://transcenmentalism.org/OpenStim/

-Ed

It seems Markram is again back to getting some interesting results. Recently a new discovery from the Brain Mind Institute of the EPFL shows that the brain adapts to new experience by unleashing a burst of new neuronal connections, and only the fittest survive. The research further shows that this process of creation, testing, and reconfiguring of brain circuits takes place on a scale of just hours, suggesting that the brain is evolving considerably even during the course of a single day.

The paper can be found Here.

Im a molecular/cellular neurobiologist. I do however, have a deep interst in neural prosthetics, bionics research. Is there a place for me in this field?

This past May, the Almaden Research center, part of IBM research, invited some provocative speakers on the topic of “Cognitive Computing” to come and speak. Since IBM recently invested a lot of money into understanding the brain with the Blue Brain project, it seems like this meeting was a way to figure out the next step along this path.

Powerpoint presentations and videos of the event are available online.

From the synopsis:

The 2006 Almaden Institute will focus on the theme of “Cognitive Computing” and will examine scientific and technological issues around the quest to understand how the human brain works. We will examine approaches to understanding cognition that unify neurological, biological, psychological, mathematical, computational, and information-theoretic insights. We focus on the search for global, top-down theories of cognition that are consistent with known bottom-up, neurobiological facts and serve to explain a broad range of observed cognitive phenomena. The ultimate goal is to understand how and when can we mechanize cognition.

Confirmed speakers include Toby Berger (Cornell), Gerald Edelman (The Neurosciences Institute), Joaquin Fuster (UCLA), Jeff Hawkins (Palm/Numenta), Robert Hecht-Nielsen (UCSD), Christof Koch (CalTech), Henry Markram (EPFL/BlueBrain), V. S. Ramachandran (UCSD), John Searle (UC Berkeley) and Leslie Valiant (Harvard). Confirmed panelists include: James Albus (NIST), Theodore Berger (USC), Kwabena Boahen (Stanford), Ralph Linsker (IBM), and Jerry Swartz (The Swartz Foundation).

A genetically encoded fluorescent amino acid — Summerer et al. 103 (26): 9785 — Proceedings of the National Academy of Sciences

Some cool silicon biology to add to the toolbox. Now you can tag proteins by using a nonsense codon that codes for a fluorescent amino acid-tRNA. This technique and similar ones could easily revolutionize cellular tracking of protein trafficking.

{alpha}-Synuclein Blocks ER-Golgi Traffic and Rab1 Rescues Neuron Loss in Parkinson’s Models — Cooper et al., Science

Fascinating evidence pointing toward a treatment for parkinson’s. Basically, Lindquist’s group finds that overexpression of a trafficking protein Rab1 that moves folded proteins from the ER to the Golgi can prevent alpha-synuclein accumulation-triggered death of rat neurons.

Of course, in vitro is not in vivo. And, for all we know, Parkinson’s could be a complex, multi-mechanism disease. But this looks promising!

Abstract:

Alpha-synuclein misfolding is associated with several devastating neurodegenerative disorders including Parkinson’s Disease (PD). In yeast cells and in neurons {alpha}Syn accumulation is cytotoxic, but little is known about its normal function or pathobiology. The earliest defect following {alpha}Syn expression in yeast was a block in endoplasmic reticulum (ER) to Golgi vesicular trafficking. In a genome-wide screen, the largest class of toxicity modifiers were proteins functioning at this same step, including the Rab GTPase Ypt1p, which associated with cytoplasmic {alpha}Syn inclusions. Elevated expression of Rab1, the mammalian YPT1 homolog, protected against {alpha}Syn-induced dopaminergic neuron loss in animal models of PD. Thus synucleinopathies may result from disruptions in basic cellular functions that interface with the unique biology of particular neurons to make them especially vulnerable.

An Anti-Addiction Pill? - New York Times

Lots of interesting stuff here on new treatments for addiction, including: A methadone (heroin-substitute) replacement called buprenorphine with less dependency and less of a high; an injectible version of alcoholism treatment naltrexone called Vivitrol, which is injectable and lasts one month; some medications that increase GABA production; and, perhaps most innovative is a vaccine against nicotine that allows antibodies to bind nicotine and prevent crossing through the blood-brain barrier.

Excerpts with some of the neat experiments involving dopamine receptors and environmental factors in addiction are after the jump.
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Modulation of intracortical synaptic potentials by presynaptic somatic membrane potential : Nature

Very interesting work. Modulation of the somatic potential seems to influence the EPSP, as measured by paired patch recordings of two layer 5 cells in cortical slice. Somatic depolarization from resting potential to near threshold results in an increase in evoked EPSPs.

In synaptic physiology, we often make a point of distinguishing intrinsic changes (eg. membrane potential) from synaptic conductance changes. Now it looks like the line between those might be a bit blurry!

Here’s a N&V by Eve Marder too.

Cell : Astrocytes Put down the Broom and Pick up the Baton [N&V summary]

Some beautiful work [original article] by Oliet’s lab in a recent issue of Cell demonstrates the importance of glia in synaptic plasticity. The show a system where D-serine and not glycine controls the NMDA receptor in a coagonist role (or perhaps glutamate is really the coagonist…) and show how similar pairing protocols can have opposite effects (LTD vs. LTP) depending on D-serine modulation by astrocytes. Yet more hidden factors in plasticity are being revealed!

Here’s the key figure:

More details from the News & Views summary after the jump. (more…)