Moving academic website and updating it

My main academic website had been hosted at Sussex University for the past 6+ years.  The design had not changed either, even though I had stop liking it long ago.  I’m changing the design entirely, all towards minimalism.

The main reason for choosing an online service is that I won’t have to maintain html files on my computer and I won’t have to upload them to a server.  It also means I can update the website from anywhere, as long as there is access to the internet.  The main reason for choosing tumblr (over wordpress) is that I like the aesthetics much better.

Even though the new website looks like a traditional website, with a few static pages that hold the long-term content, because of its design, it has the opportunity to serve also as a blog.  I’m not yet sure whether I will be posting things (news, what I’m working on, interesting articles I’m reading).  If I do, it will be strictly academic and relevant to my research.  And if so, it will be replacing this blog (I have yet to decide).  At any rate, that’s another reason for the change: to unite the static with the dynamic.

Feedback welcome.

Seminar #69: Evolution and analysis of minimal neural circuits for klinotaxis in C. elegans (via Life & Mind seminars)

Currently preparing for this.

Next Tuesday (31st Aug., 3:30pm in Arun-401) we will have a special seminar by one of our L&M alumni, Dr. Eduardo Izquierdo. He will give a presentation of his recent work on the topic of: Evolution and analysis of minimal neural circuits for klinotaxis in C. elegans Eduardo Izquierdo Chemotaxis during sinusoidal locomotion in nematodes captures in simplified form the general problem of how dynamical interactions between the nervous system, b … Read More

via Life & Mind seminars

Evolution and analysis of minimal neural circuits for klinotaxis in C. elegans

Paper accepted to the Journal of Neuroscience.

Abstract: Chemotaxis during sinusoidal locomotion in nematodes captures in simplified form the general problem of how dynamical interactions between the nervous system, body, and environment are exploited in the generation of adaptive behavior. We used an evolutionary algorithm to generate neural networks that exhibit klinotaxis, a common form of chemotaxis in which the direction of locomotion in a chemical gradient closely follows the line of steepest ascent. Sensory inputs and motor outputs of the model networks were constrained to match the inputs and outputs of the C. elegans klinotaxis network. We found that a minimalistic neural network, comprised of an ON- OFF pair of chemosensory neurons and a pair of neck muscle motor neurons, is sufficient to generate realistic klinotaxis behavior. Importantly, emergent properties of model networks reproduced two key experimental observations that they were not designed to fit, suggesting that the model may be operating according to principles similar to those of the biological network. A dynamical systems analysis of 77 evolved networks revealed a novel neural mechanism for spatial orientation behavior. This mechanism provides a testable hypothesis that is likely to accelerate the discovery and analysis of the biological circuitry for chemotaxis in C. elegans.

Preprint: IzquierdoLockery2010

How do you gauge the importance of (data-driven) models?

Imagine you are the editor of two journals, one with higher impact than the other.  You receive two modeling papers, one with more predictions than validations and vice-versa.  You can only accept one to each journal. Where would you send each?

What I’m asking is:  How do you gauge the importance of a data-driven* model?  By the number of predictions (including the proposal of novel experiments) that it makes or by the number of existing experiments that it agrees with? If both, then how would you weight each different types of contributions?

* I specify data-driven models to separate them from theory-driven models.

Kandel’s 11 open (and mostly vague) problems in the biology of memory

The Journal of Neuroscience published a series of articles commemorating the 40th anniversary of the society for neuroscience. In an attempt to follow the footsteps of mathematicians, nobel laureate Eric Kandel proposed 11 open problems for the future of understanding memory:
1. How does synaptic growth occur, and how is signaling across the synapse coordinated to induce and maintain growth?
2. What trans-synaptic signals coordinate the conversion of short- to intermediate- to long-term plasticity?
3. What can computational models contribute to understanding synaptic plasticity?
4. Will characterization of the molecular components of the presynaptic and postsynaptic cell compartments revolutionize our understanding of synaptic plasticity and growth?
5. What firing patterns do neurons actually use to initiate LTP at various synapses?
6. What is the function of neurogenesis in the hippocampus?
7. How does memory become stabilized outside the hippocampus?
8. How is memory recalled?
9. What are the role of small RNAs in synaptic plasticity and memory storage?
10. What is the molecular nature of the cognitive deficits in depression, schizophrenia, and non-Alzheimer’s age-related memory loss?
11. Does working memory in the prefrontal cortex involve reverbatory self-reexcitatory circuits or intrinsically sustained firing patterns?
Unlike the 23 problems confronting mathematics proposed by David Hilbert, the vagueness of most of Kandel’s open questions suggest an even grander question:  What is needed in the study of biological memory to allow us to propose more concrete and formal questions?
Eric R. Kandel (2009) The Biology of Memory: A Forty-Year Perspective. The Journal of Neuroscience 29(41):12748-12756.

The Journal of Neuroscience published a series of articles commemorating the 40th anniversary of the society for neuroscience.  In an attempt to follow the footsteps of mathematics, Eric Kandel proposed 11 open problems for the future of understanding the biology of memory:

1. How does synaptic growth occur, and how is signaling across the synapse coordinated to induce and maintain growth?

2. What trans-synaptic signals coordinate the conversion of short- to intermediate- to long-term plasticity?

3. What can computational models contribute to understanding synaptic plasticity?

4. Will characterization of the molecular components of the presynaptic and postsynaptic cell compartments revolutionize our understanding of synaptic plasticity and growth?

5. What firing patterns do neurons actually use to initiate LTP at various synapses?

6. What is the function of neurogenesis in the hippocampus?

7. How does memory become stabilized outside the hippocampus?

8. How is memory recalled?

9. What are the role of small RNAs in synaptic plasticity and memory storage?

10. What is the molecular nature of the cognitive deficits in depression, schizophrenia, and non-Alzheimer’s age-related memory loss?

11. Does working memory in the prefrontal cortex involve reverbatory self-reexcitatory circuits or intrinsically sustained firing patterns?

Unlike the 23 problems confronting mathematics proposed by David Hilbert, the vagueness of most of Kandel’s open questions suggest an even grander question:  What is needed in the study of biological memory to allow us to propose concrete, formal questions?