Biology subjects

Engelhard, B.

Publications and source records attributed to Engelhard, B..

9 recordsLinked to original sources

bioRxiv2024

Small animal brain surgery with neither a brain atlas nor a stereotaxic frame

Stereotaxic surgery is a cornerstone in brain research for the precise positioning of electrodes and probes, but its application is limited to species with available brain atlases and tailored stereotaxic frames. Addressing this limitation, we introduce an alternative technique for small animal brain surgery that requires neither an aligned brain atlas nor a standard stereotaxic frame. This method requires a high-contrast MRI scan of a specimen and access to a microCT scanner. The process involves attaching miniature markers to the skull, followed by CT scanning of the head. Subsequently, MRI and CT images are co-registered using standard image processing software and the targets for recordings in the brain are marked in the MRI image. During surgery, the animals head is stabilized in any convenient orientation, and the probes 3D position and angle are tracked using a multi-camera system. We have developed a software that utilizes the on-skull markers as fiducial points to align the CT/MRI 3D model with the surgical positioning system, and in turn instructs the surgeon how to move the probe to reach the targets within the brain. Our technique allows the execution of insertion tracks connecting two points in the brain. We successfully applied this method for neuropixels probe positioning in owls, quails, and mice, demonstrating its versatility and its potential to open new avenues for research in non-standard and novel animal models.

neuroscience↗

bioRxiv2023

Overlapping representations of food and social stimuli in VTA dopamine neurons

Dopamine neurons of the ventral tegmental area (VTADA) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear if the same or different VTADA neurons encode these different stimuli. To address this question, we performed 2-photon calcium imaging in mice presented with food and conspecifics, and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that modifying motivation for one stimulus affects responses to both stimuli. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone related genes in individual VTADA neurons. Taken together, our functional and transcriptional data suggest overlapping VTADA populations underlie food and social motivation.

neuroscience↗

bioRxiv2022

A vector reward prediction error model explains dopaminergic heterogeneity

The hypothesis that midbrain dopamine (DA) neurons broadcast an error for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1-3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. For instance, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward4. We argue that the previously predominant family of extensions to the RPE model, which replicate the classic model in multiple parallel circuits, are ill-suited to explaining these and other results concerning DA heterogeneity within the VTA. Instead, we introduce a complementary "feature-specific RPE" model positing that DA neurons within VTA report individual RPEs for different elements of a population vector code for an animals state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task and compare the feature-specific RPE derived from the network to population recordings from DA neurons during the same task. The model recapitulates key aspects of VTA DA neuron heterogeneity. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among SNc DA neurons5. Thus, our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.

neuroscience↗

bioRxiv2021

Strong and opponent contributions of dorsomedial striatal pathways to behavior depends on cognitive demands and task strategy

A classic view of the striatum holds that activity in direct and indirect pathways oppositely modulates motor output. Whether this involves direct control of movement, or reflects a cognitive process underlying movement, remains unresolved. Here we find that strong, opponent control of behavior by the two pathways of the dorsomedial striatum (DMS) depends on the cognitive requirements of a task. Furthermore, a latent state model (a hidden markov model with generalized linear model observations) reveals that--even within a single task--the contribution of the two pathways to behavior is state-dependent. Specifically, the two pathways have large contributions in one of two states associated with a strategy of evidence accumulation, compared to a state associated with a strategy of repeating previous choices. Thus, both the demands imposed by a task, as well as the internal state of mice when performing a task, determine whether DMS pathways provide strong and opponent control of behavior.

neuroscience↗

bioRxiv2020

Inferring spikes from calcium imaging in dopamine neurons

Calcium imaging has led to discoveries about neural correlates of behavior in subcortical neurons, including dopamine (DA) neurons. However, spike inference methods have not been tested in most populations of subcortical neurons. To address this gap, we simultaneously performed calcium imaging and electrophysiology in DA neurons in brain slices, and applied a recently developed spike inference algorithm to the GCaMP fluorescence. This revealed that individual spikes can be inferred accurately in this population. Next, we inferred spikes in vivo from calcium imaging from these neurons during Pavlovian conditioning, as well as during navigation in virtual reality. In both cases, we quantitatively recapitulated previous in vivo electrophysiological observations. Our work provides a validated approach to infer spikes from calcium imaging in DA neurons, and implies that aspects of both tonic and phasic spike patterns can be recovered.

neuroscience↗

bioRxiv2019

Choice-selective sequences in cortical inputs to the NAc provide a potential substrate for credit assignment

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement learning algorithms, both in a circuit model based on synaptic plasticity, and one based on neural dynamics. Finally, we test and confirm predictions of our circuit models by direct manipulation of nucleus accumbens input neurons. Thus, we integrate experiment and modeling to suggest neural solutions for credit assignment.

neuroscience↗

bioRxiv2019

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

neuroscience↗

bioRxiv2018

Specialized and spatially organized coding of sensory, motor, and cognitive variables in midbrain dopamine neurons

There is increased appreciation that dopamine (DA) neurons in the midbrain respond not only to reward 1,2 and reward-predicting cues 1,3,4, but also to other variables such as distance to reward 5, movements 6-11 and behavioral choices 12-15. Based on these findings, a major open question is how the responses to these diverse variables are organized across the population of DA neurons. In other words, do individual DA neurons multiplex multiple variables, or are subsets of neurons specialized in encoding specific behavioral variables? The reason that this fundamental question has been difficult to resolve is that recordings from large populations of individual DA neurons have not been performed in a behavioral task with sufficient complexity to examine these diverse variables simultaneously. To address this gap, we used 2-photon calcium imaging through an implanted lens to record activity of >300 midbrain DA neurons in the VTA during a complex decision-making task. As mice navigated in a virtual reality (VR) environment, DA neurons encoded an array of sensory, motor, and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioral variables, in addition to encoding reward. These functional clusters were spatially organized, such that neighboring neurons were more likely to be part of the same cluster. Taken together with the topography between DA neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by DA neurons.

neuroscience↗

bioRxiv2017

An accumulation-of-evidence task using visual pulses for mice navigating in virtual reality

The gradual accumulation of sensory evidence is a crucial component of perceptual decision making, but its neural mechanisms are still poorly understood. Given the wide availability of genetic and optical tools for mice, they can be useful model organisms for the study of these phenomena; however, behavioral tools are largely lacking. Here, we describe a new evidence-accumulation task for head-fixed mice navigating in a virtual reality environment. As they navigate down the stem of a virtual T-maze, they see brief pulses of visual evidence on either side, and retrieve a reward on the arm with the highest number of pulses. The pulses occur randomly with Poisson statistics, yielding a diverse yet well-controlled stimulus set, making the data conducive to a variety of computational approaches. A large number of mice of different genotypes were able to learn and consistently perform the task, at levels similar to rats in analogous tasks. They are sensitive to side differences of a single pulse, and their memory of the cues is stable over time. Moreover, using non-parametric as well as modeling approaches, we show that the mice indeed accumulate evidence: they use multiple pulses of evidence from throughout the cue region of the maze to make their decision, albeit with a small overweighting of earlier cues, and their performance is affected by the magnitude but not the duration of evidence. Additionally, analysis of the mice's running patterns revealed that trajectories are fairly stereotyped yet modulated by the amount of sensory evidence, suggesting that the navigational component of this task may provide a continuous readout correlated to the underlying cognitive variables. Our task, which can be readily integrated with state-of-the-art techniques, is thus a valuable tool to study the circuit mechanisms and dynamics underlying perceptual decision making, particularly under more complex behavioral contexts.

neuroscience↗