neurodudes

Postdoctoral/research scientist positions are available in the inter-disciplinary group of Dmitri Chklovskii at the new Janelia Farm Research Campus of the Howard Hughes Medical Institute located in the suburbs of Washington, D.C. Candidates are expected to have a PhD in neuroscience, physics, computer science or electrical engineering. Most of the work is theoretical or computational and is done in collaboration with several experimental laboratories. Successful applicants will work on projects centered on neuronal circuits such as high-throughput reconstruction of wiring diagrams as well as combining structural and physiological data to infer circuit function. Salary will be commensurate with qualifications. For more information about research directions in the group please see: http://www.hhmi.org/research/groupleaders/chklovskii.html
Interested applicants should send their CV and a statement of research interests to mitya (at) janelia.hhmi.org, and arrange for three recommendation letters to be emailed to me.

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.

A very nice neuroecon expt. in the newest Nature:

Daw et al. find that humans choose between multiple slot machines (with different payoff probabilities) based on expected value (versus just going with the highest probability one most of the time and then randomly choosing another one every so often). Then, with fMRI, they find brain areas correlated with different value predictions.

News & Views (Daeyol Lee)

Cortical substrates for exploratory decisions in humans (Daw, Dayan)

Abstract:

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this ‘exploration-exploitation’ dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects’ choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

PLoS Biology: Attenuation of Self-Generated Tactile Sensations Is Predictive, not Postdictive [open access]

I haven’t gotten a chance to fully digest this article (What is the attenuation phenomena that happens when the taps are delayed?), but it seems like a deep result from a relatively simple haptics experiment. Just thought I’d share it with the crowd.

Also, Happy Birthday to fellow Neurodude Bayle! Congrats, man.

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (”potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (”depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.

For those with theoretical interests with respect to machine learning flavored AI, the ML Theory blog run by John Langford is highly recommended. Though recently started, Langford and others have so far been doing an excellent job of commenting on both the science and culture of theoretical learning research.

Neuroimaging data of different brain areas fit to a Rescorla-Wagner model show that different cortical areas integrate stimulus changes over different time intervals. The result itself probably isn’t that shocking but I liked the nice combination of theory and experiment.

From the July 21 Neuron:

Formal Learning Theory Dissociates Brain Regions with Different Temporal Integration

Jan Gläscher and Christian Büchel

Learning can be characterized as the extraction of reliable predictions about stimulus occurrences from past experience. In two experiments, we investigated the interval of temporal integration of previous learning trials in different brain regions using implicit and explicit Pavlovian fear conditioning with a dynamically changing reinforcement regime in an experimental setting. With formal learning theory (the Rescorla-Wagner model), temporal integration is characterized by the learning rate. Using fMRI and this theoretical framework, we are able to distinguish between learning-related brain regions that show long temporal integration (e.g., amygdala) and higher perceptual regions that integrate only over a short period of time (e.g., fusiform face area, parahippocampal place area). This approach allows for the investigation of learning-related changes in brain activation, as it can dissociate brain areas that differ with respect to their integration of past learning experiences by either computing long-term outcome predictions or instantaneous reinforcement expectancies.

How does this relate to Hawkins’s idea that all cortex implements the same underlying “algorithm”? Is the integration time constant (or, in RW terms, the learning rate) tuned differently by different inputs?