A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions.

Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.

Correlations enhance the behavioral readout of neural population activity in association cortex.

Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.

Human-level performance in 3D multiplayer games with population-based reinforcement learning.

Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this remains elusive. The complexity of biological neural networks substantially exceeds the complexity of DNNs, making it even more challenging to understand the representations they learn. Thus, both machine learning and computational neuroscience are faced with a shared challenge: how can we analyze their representations in order to understand how they solve complex tasks? We review how data-analysis concepts and techniques developed by computational neuroscientists can be useful for analyzing representations in DNNs, and in turn, how recently developed techniques for analysis of DNNs can be useful for understanding representations in biological neural networks. We explore opportunities for synergy between the two fields, such as the use of DNNs as in silico model systems for neuroscience, and how this synergy can lead to new hypotheses about the operating principles of biological neural networks.

Neural scene representation and rendering.

Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.

History-dependent variability in population dynamics during evidence accumulation in cortex.

We studied how the posterior parietal cortex combines new information with ongoing activity dynamics as mice accumulate evidence during a virtual navigation task. Using new methods to analyze population activity on single trials, we found that activity transitioned rapidly between different sets of active neurons. Each event in a trial, whether an evidence cue or a behavioral choice, caused seconds-long modifications to the probabilities that govern how one activity pattern transitions to the next, forming a short-term memory. A sequence of evidence cues triggered a chain of these modifications resulting in a signal for accumulated evidence. Multiple distinguishable activity patterns were possible for the same accumulated evidence because representations of ongoing events were influenced by previous within- and across-trial events. Therefore, evidence accumulation need not require the explicit competition between groups of neurons, as in winner-take-all models, but could instead emerge implicitly from general dynamical properties that instantiate short-term memory.

REST Regulates Non-Cell-Autonomous Neuronal Differentiation and Maturation of Neural Progenitor Cells via Secretogranin II.

RE-1 silencing transcription factor (REST), a master negative regulator of neuronal differentiation, controls neurogenesis by preventing the differentiation of neural stem cells. Here we focused on the role of REST in the early steps of differentiation and maturation of adult hippocampal progenitors (AHPs). REST knockdown promoted differentiation and affected the maturation of rat AHPs. Surprisingly, REST knockdown cells enhanced the differentiation of neighboring wild-type AHPs, suggesting that REST may play a non-cell-autonomous role. Gene expression analysis identified Secretogranin II (Scg2) as the major secreted REST target responsible for the non-cell-autonomous phenotype. Loss-of-function of Scg2 inhibited differentiation in vitro, and exogenous SCG2 partially rescued this phenotype. Knockdown of REST in neural progenitors in mice led to precocious maturation into neurons at the expense of mushroom spines in vivo. In summary, we found that, in addition to its cell-autonomous function, REST regulates differentiation and maturation of AHPs non-cell-autonomously via SCG2. SIGNIFICANCE STATEMENT: Our results reveal that REST regulates differentiation and maturation of neural progenitor cells in vitro by orchestrating both cell-intrinsic and non-cell-autonomous factors and that Scg2 is a major secretory target of REST with a differentiation-enhancing activity in a paracrine manner. In vivo, REST depletion causes accelerated differentiation of newborn neurons at the expense of spine defects, suggesting a potential role for REST in the timing of the maturation of granule neurons.