Spatially organized striatal neuromodulator release encodes trajectory errors.

Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional 'trajectory errors' reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.

Factorized visual representations in the primate visual system and deep neural networks.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether ('invariance'), represented in non-interfering subspaces of population activity ('factorization') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

When looking at a picture, we can quickly identify a recognizable object, such as an apple, applying a single word label to it. Although extensive neuroscience research has focused on how human and monkey brains achieve this recognition, our understanding of how the brain and brain-like computer models interpret other complex aspects of a visual scene ­ such as object position and environmental context ­ remains incomplete. In particular, it was not clear to what extent object recognition comes at the expense of other important scene details. For example, various aspects of the scene might be processed simultaneously. On the other hand, general object recognition may interfere with processing of such details. To investigate this, Lindsey and Issa analyzed 12 monkey and human brain datasets, as well as numerous computer models, to explore how different aspects of a scene are encoded in neurons and how these aspects are represented by computational models. The analysis revealed that preventing effective separation and retention of information about object pose and environmental context worsened object identification in monkey cortex neurons. In addition, the computer models that were the most brain-like could independently preserve the other scene details without interfering with object identification. The findings suggest that human and monkey high level ventral visual processing systems are capable of representing the environment in a more complex way than previously appreciated. In the future, studying more brain activity data could help to identify how rich the encoded information is and how it might support other functions like spatial navigation. This knowledge could help to build computational models that process the information in the same way, potentially improving their understanding of real-world scenes.

Selective consolidation of learning and memory via recall-gated plasticity.

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

Dynamics of striatal action selection and reinforcement learning.

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

Motor cortex is required for flexible but not automatic motor sequences.

How motor cortex contributes to motor sequence execution is much debated, with studies supporting disparate views. Here we probe the degree to which motor cortex's engagement depends on task demands, specifically whether its role differs for highly practiced, or 'automatic', sequences versus flexible sequences informed by external events. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex revealed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex-dependent, suggesting that subcortical consolidation of an automatic motor sequence is delayed or prevented when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and explained the underlying circuit mechanisms. Our results critically delineate the role of motor cortex in motor sequence execution, describing the condition under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex's role in motor sequence generation.

Dissociating the contributions of sensorimotor striatum to automatic and visually guided motor sequences.

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. However, such flexibility is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast and effortless, that is, 'automatic'. The basal ganglia are thought to underlie both types of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence instructed by cues and in a self-initiated overtrained, or 'automatic,' condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of the execution mode. Although lesions reduced the movement speed and affected detailed kinematics similarly, they disrupted high-level sequence structure for automatic, but not visually guided, behaviors. These results suggest that the basal ganglia are essential for 'automatic' motor skills that are defined in terms of continuous kinematics, but can be dispensable for discrete motor sequences guided by sensory cues.

Modularity and robustness of frontal cortical networks.

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

The connectome of the adult Drosophila mushroom body provides insights into function.

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory, and activity regulation. Here, we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.