Generalized linear model

The brain has a remarkable ability to maintain a flexible behavioral repertoire which must continuously expand to accommodate new knowledge, a form of ‘continual learning’. Unlike mainstream artificial networks, which require complex and computationally expensive systems to achieve similar versatility, biological brains efficiently generalize prior learning, making inferences to acquire new tasks and perform multiple tasks based on context. Despite being an ideal species for neural investigation, mice have typically been thought to exhibit limited cognitive capacity for such complex learning p

Information in sensory neurons is conveyed by spiking activity varying in time. This is quantified by the mutual-information rate (MIR), given by $\mathrm{MIR}:=\underset{\Delta t\rightarrow\infty}{\lim}\frac{{\cal I}\left(\Delta t\right)}{\Delta t}$, where ${\cal I}\left(\Delta t\right)$ is the mutual information between the activity of a spiking neuron and a stimulus over a given time window $\Delta t$. To compute it for spiking data, the spike train is typically discretized with a certain bin-size $\mathrm{dt}$. Because $\Delta t/\mathrm{dt}$ is typically well bigger than $1$, there are man

…fNIRS signal was deconvoluted using Generalized Linear Model (GLM) in which orthogonalized SC signal (first analysis), n

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. We expect neural responses to be functionally highly similar within a species (since we expect findings to generalize across animals). What kind of transform will make such similarity most evident? That is, under what kind of transform are conspecifics’ neural responses highly similar to each other? Researchers often default to linear regression as a reasonable transform class for measuring neural

Perceptual decision making is fraught with uncertainty that comes (at least) in two distinct forms: there can be uncertainty about how a stimulus is perceived, and even for a known stimulus there can be uncertainty about the decision that it licenses. Although putative neural correlates of uncertainty have been described in early sensory areas, these two forms of uncertainty have not been distinguished. Thus, it remains unknown whether and how perceptual uncertainty (presumably computed locally), as opposed to decision uncertainty (presumably computed downstream and conveyed by feedback connec

Communication is an inherently interactive behavior involving the coordinated exchange of social signals between two or more individuals. Studies exploring the neural basis of communication in the primate brain have traditionally focused on how these signals are processed or produced, leaving many facets of vocal behavior largely unexplored. One challenge has been limitations of traditional analyses for quantifying the often variable, dynamic nature of natural communication behaviors, such as conversational exchanges. Here we recorded the activity of single neurons in prefrontal and premotor c

Learning is not only the acquisition of knowledge, but also the ability to express that knowledge when needed. Previous work using non-reinforced probe trials showed that animals exhibit complete acquisition of stimulus-action associations far before they express them under reinforcement. Why do animals exhibit this gap in performance between acquisition and expression despite already acquiring the stimulus-action associations? Early in learning, animals may (1) exhibit motor biases that they slowly learn to suppress, (2) continue to explore different choice alternatives, or (3) base decisions

…address this challenge by performing a model-based analysis of decision-making data acquired during multi-region laser-scanning inactivation of dorsal cortex. We analyzed optogenetic inactivation data from mice pe

Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is