Representational similarity analysis

Auditory categorization is a crucial aspect of everyday communication, but we lack a clear understanding of the computations performed by the brain to define boundaries between groups of natural sounds. While modern machine-learning-based classifiers perform as well as humans in categorizing sounds, they can rely on idiosyncratic acoustic cues that diverge from biologically relevant cues. We hypothesized that, since auditory cortical neurons show robust category selectivity, training a classifier with an auditory cortical front end should more accurately reflect biological categorization. To t

Bayesian models have been successful in explaining sensorimotor behavior in humans and animals, yet it remains unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about which latent variable is represented by a neuron, or neural population (``generative model'') and (2) a hypothesis about how beliefs about a latent variable (``posterior'') are represented by neural responses. Here, we present two new methods that in conjunction allow us to, first, fa

Constructing well-motivated methodologies for quantifying similarity between high-dimensional neural representations is an active research in computational neuroscience. Such similarity measures have implications for understanding variability in neural computations across individuals and species [1], as well as for comparisons between biological systems and computational models [2]. One common approach to understand these systems is to build regression models or “decoders” that reconstruct features of the stimulus from neural responses. Here, we leverage this idea to quantify the similarity of

When we look at a dolphin we see grey skin, a long body, and fins – visual features that are typical for fish. Yet we know that dolphins are mammals. How does the brain learn these exceptions? To elucidate this question, we conducted a functional magnetic resonance imaging study (fMRI) using a categorization learning task. Across five blocks, participants learned to sort stimuli into two classes depending on their color compositions while undergoing 3 Tesla fMRI scanning. In the two sets, we also presented stimuli that shared most of their features with the respective other group (exceptions).

Is working memory (WM) a unitary construct or is it composed of separable sub-processes with distinct neuroanatomical bases? In line with this latter view, (i) psychiatric and neurological patients often display subtle deficits in specific aspects of WM, and (ii) neurophysiological evidence indicates that WM engages a wide network of regions throughout the brain.To address this question, we designed a WM battery that required participants to maintain the colors and locations of multiple discs over a brief memory delay and report the location of the cued disc. By varying the number of discs to

Abundant work has highlighted the functional role of the hippocampal formation in navigation behaviour across species. Recent studies highlight evidence that the same neural coding mechanisms found to underly the representation of physical spaces also apply to conceptual domains. Specifically, the representation of locations in both physical and conceptual spaces is reflected in distinct neural activity patterns in hippocampus and entorhinal cortex. Electrophysiological work from rodents has shown that single hippocampal place cells represent different locations in different spaces – a functio

The sensory content and temporal structure of stimuli consistently bias the percept of duration. Previous studies revealed that intervals filled with continuous sensory input are perceived as longer compared to stimuli only marked at their onset and offset, a phenomenon referred to as 'filled duration illusion'. It remains unclear whether these distinct stimulus formats are measured by a common timing mechanism. In our study, we aimed to examine potential differences in the EEG correlates of temporal processing for filled, flanked and empty stimuli. We tested participants in a temporal reprodu

…of how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in human participants listening to acoustic sequences with different levels of regularity. Representational similarity analysis revealed how,

…address this need, here we leverage a Representational Similarity Analysis framework to quantita

…are now many methods for quantifying representational dissimilarity including canonical correlations analysis (CCA), centered kernel alignment (CKA), representational similarity analysis (RSA), shape metrics, and so on. Intuitively, these measures quantify similarity in the geometry of neural responses while removing expected forms of invariance across networks (e.g. permutations over arbitrary neuron indices). However, these met

The ability to record large neural populations—hundreds to thousands of cells simultaneously—is a defining feature of modern systems neuroscience. Aside from improved experimental efficiency, what do these technologies fundamentally buy us? I'll argue that they provide an exciting opportunity to move beyond studying the "average" neural response. That is, by providing dense neural circuit measurements in individual subjects and moments in time, these recordings enable us to track changes across repeated behavioral trials and across experimental subjects. These two forms of variability are stil

…across the cortical hierarchy using representational similarity

…Time-resolved representational similarity analysis revealed that fami