Neural populations

Build scalable computational pipelines for biophysical models of neurons and neural populations. Apply machine learning and statistical inference to neural recordings, construct biologically realistic networks and help shape research directions at an early-stage company working at the intersection of neuroscience and computation. On-site engagement is preferred and international candidates may apply. The advertised salary range is USD 132,000–259,000; no fixed closing date is stated.

Michael Breakspear and Mac Shine examine how neural dynamics change from individual cells to whole-brain systems. Their work connects gain modulation with the balance between integration and segregation, relates it to arousal pathways, and uses coarse-graining to investigate both scale-dependent coding and shared dynamical properties across neural populations.

…to multiplexed communication between neural populations in different directions [1], respectively relaying, in a predictive coding interpretation, top-down expectations and bottom-up prediction error signals [2], which target and originate from distinct cortical layers with different dominant frequencies [3]. However, this oscillatory-multiplexed routing theory presents several challenges. Multiplexed routing might occur indeed without need for distinct frequencies of operation [4]. Furthermore, phasic enhancement

…of the structure and function of neural circuits. Despite this, the application of these ideas to neuroscience and machine learning has been hampered by the fact that the computation and maximization of mutual information is only analytically tractable for simple coding problems, under the assumption of small network sizes and low dimensional stimuli. Using tools from the theory of spin-glasses we present a novel approach to estimating the entropy of large neural populations in response to arbitrary s

…control relates to the behaviour of neural populations.

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to dete

…Functional networks of neurons and neural populations reflect how their interactions change with behaviors, stimulus types, and internal states. Therefore, the information propagation across a network can be analyzed through the varying topological properties of the functional networks. Our study investigates the functional networks of the visual cortex at both the single-cell and population levels. Ou

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new t

…drift causes fixed readouts from a neural population to gradually deteriorate, suggesting that downstream populations adapt to these tuning changes. Many statistical distributions of tuning curve changes can produce comparable population-wide drift. We argue that heavy-tailed drift statistics, in which populations exhibit sparse and sudden changes in neuronal tuning, are relatively easy to adapt to. We present preliminary evidence that neural populations favour suc

…are organized in specialized sub-populations to encode behaviorally relevant variables ("categorical" selectivity) or whether information on several variables is distributed across the neural population ("heterogeneous mixed" selectivity). With evidence being published in support of both views, it is unclear under which conditions the brain uses one or the other encoding strategy. We will p

Perhaps the most impressive aspect of the way the brain enables us to act on the sensory world is its flexibility. We can make a general inference about many sensory features (rating the ripeness of mangoes or avocados) and map a single stimulus onto many choices (slicing or blending mangoes). These can be thought of as flexibly mapping many (features) to one (inference) and one (feature) to many (choices) sensory inputs to actions. Both theoretical and experimental investigations of this sort of flexible sensorimotor mapping tend to treat sensory areas as relatively static. Models typically

Learning general principles from single examples is a hallmark of high cognitive functions. This type of learning is computationally challenging for artificial agents, yet many animals such as primates can perform it with ease. To study one-exemplar generalization in the lab, it would be ideal to develop appropriate behavioral tasks in more tractable model organisms, such as rodents. Here we report that mice can perform one-exemplar generalization with difficult texture-based visual stimuli such as “leaves” and “rocks”. We trained mice to discriminate between single images from two texture cat

…the activity of PMd and M1 neural populations show distinct features, suggestive of area-specific computations. We analyzed simultaneous recordings from populations of PMd and M1 neurons from monkeys performing both a stand

Humans and other animals often perform similar actions in different contexts. For instance, we use different criteria when judging whether an apple or avocado are ripe, but use very similar motor actions to move the fruit into our basket if we select it. We hypothesize that this ability to context switch is facilitated by functional modularity within brain regions, such as the specialization of a subpopulation of inferotemporal cortex neurons for face relative to non-face features. However, the link between this functional modularity and context-dependent behavior is not fully understood. Here

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension

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

…on noisy single neurons, rather than neural populations, the linearity hypothesis has not been rigorously tested. Here, we examined the representational geometry of value in populations of neurons recorded in the ventromedial prefrontal cortex (vmPFC

…model the computation carried out by neural populations. This perspective hypothesizes that certain dynamical features underlie the computations performed by the system, and characterizing them enables an intuitive understanding of the system. One central feature is the fixed (or slow) point: the fixed points of the system and the linearized dynamics around them can explain important aspects of the computation that is being performed. While successful in many cases, fixed-points analysis has limitations. Finding fixed points is ge

…and operations that are manipulated by neural populations in the human cortex. Several theoretical works suggest that probabilistic representations are present from low-level sensory areas to high-level areas. According to this view, the neural dynamics implements some forms of probabilistic message passing (i.e. neural sampling, probabilistic pop

In an ever-changing environment, uncertainty is omnipresent. To deal with this, organisms have evolved mechanisms that allow them to take advantage of environmental regularities in order to make decisions robustly and adjust their behavior efficiently, thus maximizing their chances of survival. In this talk, I will present behavioral evidence that animals perform model-based state inference to predict environmental state changes and adjust their behavior rapidly, rather than slowly updating choice values. This model-based inference process can be described using Bayesian change-point models. F

Social interactions such as mating and fighting are driven by internal emotional states. How can we study internal states of an animal when it cannot tell us its subjective feelings? Especially when the meaning of the animal’s behavior is not clear to us, can we understand the underlying internal states of the animal? In this talk, I will introduce our recent work in which we used male mounting behavior in mice as an example to understand the underlying internal state of the animals. In many animal species, males exhibit mounting behavior toward females as part of the mating behavior repertoi

…experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will

…variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher-order visual areas and measured stimulus discrimination thresholds of 0.35 degrees and 0.37 degrees respectively in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported

…a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experim