Biology subjects

LaFosse, P. K.

Publications and source records attributed to LaFosse, P. K..

5 recordsLinked to original sources

bioRxiv2024

Active filtering of sequences of neural activity by recurrent circuits of sensory cortex

Recurrent neural networks can generate dynamics, but in sensory cortex it has been unclear if any dynamic processing is supported by the dense recurrent excitatory-excitatory network. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but by filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure neural responses to natural images and play them back, finding responses are boosted when inputs are played back during the correct movie dynamic context-- when the preceding sequence corresponds to natural vision. This sequence selectivity depends on a network mechanism: earlier input patterns produce responses in other local neurons, which interact with later input patterns. We confirm this mechanism by designing sequences of inputs that are boosted or attenuated by the network. These data suggest recurrent cortical connections perform predictive processing, encoding the statistics of the natural world in input-output transformations.

neuroscience↗

bioRxiv2023

Single cell optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cells input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons activity varies with sensory stimuli. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons IO functions can filter sensory inputs -- not only do sensory stimuli change neurons spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged. Significance statementHow neurons transform their inputs into outputs is a fundamental building block of brain computation. Past studies have measured neurons input-output (IO) functions in vitro or in anesthetized states. Here, we measure neurons IO functions in the awake and intact brain, where ongoing network activity can influence neurons responses to input. Using state-of-the-art optogenetic methods to deliver precise inputs to neurons near the cell body, or soma, we discover neurons have a supralinear-to-linear IO function, contrary to previous findings of threshold-linear, strongly saturating, or power law IO functions. This supralinear-to-linear somatic IO function shape allows neurons to decrease their responses to, or filter, inputs while they are suppressed below their resting firing rates, a computation we term attenuation-by-suppression.

neuroscience↗

bioRxiv2022

Bicistronic expression of a high-performance calcium indicator and opsin yields stable, robust cortical expression for holographic two-photon stimulation

Patterns of activity across many neurons are fundamental units of neural computation. Two-photon holographic photostimulation allows both delivering input to, and imaging responses from, patterns or populations of neurons. However, to make this method an easily-deployable tool, simple methods are needed to robustly and stably express opsins and indicators in the same cells. Here we describe a bicistronic adeno-associated virus (AAV) that in transfected cells expresses both the fast and bright calcium indicator GCaMP8s, and a soma-targeted (st) and two-photon-activatable opsin, ChrimsonR. With this method, in the visual cortex of mice, stChrimsonR stimulation with two-photon holography drives robust spiking in targeted cells, and neural responses to visual sensory stimuli and spontaneous activity are strong and easy to measure. stChrimsonR is a good choice of opsin when a balance is needed between stimulation-laser activatability and avoidance of imaging laser activation. This approach is a simple and robust way to prepare neurons in vivo for two-photon holography and imaging. Significance statementThe recent advent of holographic photostimulation methods in conjunction with standard two-photon calcium imaging promises unprecedented levels of control in manipulating and dissecting brain circuitry in vivo while reading out neural activity. These all-optical methods rely on a working synergy between optogenetic strategies to both measure calcium activity through genetically-encoded calcium indicators and modulate cell activity through light-activated opsins. Genetic strategies to achieve reliable and stable co-expression of opsin and indicator remain sparse and often challenging to execute. Here, we present a genetic tool to achieve robust co-expression of jGCaMP8s indicator and stChrimsonR opsin via a single injected virus to help facilitate experiments aiming to use holography to investigate the circuit principles underlying brain activity.

neuroscience↗

bioRxiv2022

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons responses is unclear, with some studies reporting weak recurrent effects, some amplification, and others showing instead local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neurons direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations, by shaping and changing neurons responses to input.

neuroscience↗

bioRxiv2022

Amplified cortical neural responses as animals learn to use novel activity patterns

Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task, and therefore these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.

neuroscience↗