Advanced Numerical Methods in Neuroscience Lecture is now online

Advanced Numerical Methods in Neuroscience is a lecture I am holding in the graduate school Neurodapt. You can reach the overview of the lecture from this link and download the course material from this link.

Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex

Freedman et al. generated pictures of complex objects using parametric combinations of 6 base images. These base images represented different kinds of felines or dogs, therefore their combinations gave rise to images that were graded in their category membership (e.g. whereas some combinations were clearly dog- or feline-like, others pictures were somewhere in between) while guaranteeing diversity within each category. These images were shown in a delayed-match-to-category task to monkeys. Solving this task requires a level of abstraction from the sheer appearances of the stimuli. Even at category boundaries where the discrimination is most difficult, the performance was high. They recorded activity of single neurons from prefrontal cortex, more precisely from the ventral part of the principal sulcus. Their results show evidence for neurons that are able to distinguish between these two, supposedly learnt categories. That is the responses are 1/ not gradual as the stimuli  and 2/ characterized by a sharp step-like function at the category boundary. The data is clear, the interpretation is inline with the data. I find it unfortunately that pictures which are at the category boundary were not presented. And the study would gain if the similarity measure was in the perceptual space rather than in the stimulus domain. The 6 base images could in principle be tested on humans using perceptual mapping techniques.

OHBM Hamburg 2014 Abstract: Precision of Neuronal Representations during Fear Generalization

Precision of Neuronal Representations during Fear Generalization

Onat S., Büchel C.
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany


Fear generalization is usually conceived as resulting from a lack of precision in the neuronal representations about the aversive stimulus. Therefore the lack of certainty is believed to be the source of generalization that is observed on the behavioral level. Alternatively, fear generalization might be the result of an active neuronal process, whereby the nervous system tries to optimally control behavior based on what is already known. This scenario predicts that hyper-precise neuronal representations about the aversive stimulus would co-exist with a broader behavioral tuning. Using an event-related fMRI paradigm, we analyzed the precision of neuronal signals in different brain regions and compared these with different behavioral measurements.


We created 8 computer generated faces that were organized along a circular similarity gradient (Fig. 1, dashed line). A maximum-likelihood based multi-dimensional scaling method (Maloney et al., 2003) was used to confirm the circularity of the perceptual organization of these stimuli (Fig. 1, solid line). This gradual change in stimulus similarity was translated to an aversiveness gradient using a classical Pavlovian conditioning paradigm. To this end, one randomly selected face (CS+) was partially associated with an aversive electric shock. The most dissimilar face was kept as neutral (CS-). BOLD responses were recorded before and after the conditioning phase together with changes in skin conductance, as well as aversiveness ratings (n = 29).


We identified a set of neuronal clusters that were significantly modulated as a function of increasing dissimilarity to the CS+ face. The average amplitude of evoked responses by the CS+, CS- and all intermediate faces is shown in Fig. 2 (mean ± SEM, red for CS+, cyan for CS-) for two clusters located in hippocampus and insula. The effect of conditioning is clearly seen as a modulation of responses centered on the CS+ face following the conditioning (bottom panels). These responses were fit with a Gaussian function, yielding parameterized fear-tuning profiles (Fig. 2, black curves), where alpha (𝛼) and sigma (𝜎) parameters characterized the strength and the width of the tuning profiles, respectively (Fig. 2).

Almost all clusters within this identified fear generalization network including a set of prefrontal, cingular, hippocampal, and face selective sensory sites exhibited a strong deactivation in response to CS+ face (Fig. 3, left panel). The right insula was the sole exception to this pattern (p < 0.001), showing a fear-tuning profile that was characterized by a net activation (Fig. 3, left panel, top bar). Among all the clusters investigated, the insula showed the sharpest fear tuning (Fig. 3, right panel, bottom bar). We next compared, the precision of insular aversive tuning to the fear tuning of skin conductance and aversiveness ratings. The width of insular tuning was even sharper than the tuning of any behavioral measure i.e. ratings (t(28)= -2.67, p = 0.0062) and skin conductance (t(28)= -2.23, p = 0.017) responses (Fig. 4).  



These results show that the representation of the aversive stimulus is present in a hyper-precise manner within the neuronal networks responsible for fear-generalization. The imprecision of the tuning that is observed in other neuronal sites and at the behavior level seems to be mediated by a mechanism that actively “blurs” the source of the aversive event, rather than resulting from a lack of precision in the neuronal representations. Our results therefore suggest that a controlled imprecision rather than an imprecision in the control, is responsible for the generalization of fear in the healthy humans.