Summary Decision-making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in pre-arcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey’s choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes-of-mind (CoM’s) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoM’s. Importantly, the properties of the candidate CoM’s also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice; they were more likely for weak and intermediate stimuli than for strong stimuli; and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable “target” dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.

Summary A fundamental challenge in studying the frontal lobe is to parcellate this cortex into ‘natural’ functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated sub-networks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated sub-networks that differentially represent behavioral choice and reaction time. Importantly, these sub-networks are detectable during different behavioral states, and surprisingly, are defined better by ‘common noise’ than task-evoked responses. Our parcellation process works well on ‘spontaneous’ neural activity, and thus bears strong resemblance to the identification of ‘resting state’ networks in fMRI datasets. Our results demonstrate a powerful new tool for identifying cortical sub-networks by objective classification of simultaneously recorded electrophysiological activity.

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding we analyze neural activity recorded during delays in four experiments on non-human primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data, and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low dimensional. These constraints rule out working memory models that rely on constant, sustained activity, and neural networks with high dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time encoding properties and the dimensionality observed in the data.