Population dynamics of choice representation in dorsal premotor and primary motor cortex

Abstract

SummaryStudies in multiple species have revealed the existence of neural signals that lawfully co-vary with different aspects of the decision-making process, including choice, sensory evidence that supports the choice, and reaction time. These signals, often interpreted as the representation of a decision variable (DV), have been identified in several motor preparation circuits and provide insight about mechanisms underlying the decision-making process. However, single-trial dynamics of this process or its representation at the neural population level remain poorly understood. Here, we examine the representation of the DV in simultaneously recorded neural populations of dorsal premotor (PMd) and primary motor (M1) cortices of monkeys performing a random dots direction discrimination task with arm movements as the behavioral report. We show that single-trial DVs covary with stimulus difficulty in both areas but are stronger and appear earlier in PMd compared to M1 when the stimulus duration is fixed and predictable. When temporal uncertainty is introduced by making the stimulus duration variable, single-trial DV dynamics are accelerated across the board and the two areas become largely indistinguishable throughout the entire trial. These effects are not trivially explained by the faster emergence of motor kinematic signals in PMd and M1. All key aspects of the data were replicated by a computational model that relies on progressive recruitment of units with stable choice-related modulation of neural population activity. In contrast with several recent results in rodents, decision signals in PMd and M1 are not carried by short sequences of activity in non-overlapping groups of neurons but are instead distributed across many neurons, which once recruited, represent the decision stably during individual behavioral epochs of the trial.

Non‐invasive assessment of superficial and deep layer circuits in human motor cortex

“…However, when we see no consistent changes in the early part of I1 (EFD 0 ms) but changes in later parts (EFD +0.6 ms), we believe that this provides good evidence that distinct cortical circuits have modulated their excitability. In support of this conclusion, our results were as expected from direct recordings of neurons in monkeys performing a discrimination task (Chandrasekaran et al 2017;Peixoto et al 2018).…”

“…We used the same algorithm and parameters as in a previous study in monkeys (Peixoto et al . ). The subjects used a robotic manipulandum, which tracked the wrist position and plotted it as a cursor on a computer screen, and made their choices by placing the cursor in a left or right target that was displayed on the screen.…”

“…The visual stimuli were generated using MATLAB and presented with 75 Hz frame rate on a 21-inch LCD monitor with 800 × 600 pixels resolution positioned 40 cm away from the subject. We used the same algorithm and parameters as in a previous study in monkeys (Peixoto et al 2018). The subjects used a robotic manipulandum, which tracked the wrist position and plotted it as a cursor on a computer screen, and made their choices by placing the cursor in a left or right target that was displayed on the screen.…”

Economic Choice as an Untangling of Options into Actions

“…As the decision-maker deliberates and begins to favor of one the options, the representation of that action grows stronger until a decision threshold is reached (Cisek, 2006; Cisek and Kalaska, 2005; Thura and Cisek, 2014). These observations suggest that these regions are not simply waiting around for the results of the comparison to occur, but that the preparation and comparison take place—in part—within motor regions (Cisek, 2012; Cisek and Pastor-Bemier, 2014; Hunt and Hayden, 2017; Kaufman et al, 2014; see also Peixoto et al, 2018). …”

Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex

“…The idea of complicated encoding systems yielding easily decodable codes has been previously suggested [56,57]. Although simple decoders have been successfully used to read out neural population responses in diverse cortical circuits [13,15,[58][59][60][61][62], the complexity of the neural encoding and decoding has been rarely quantified in the same dataset. We provide such a direct comparison in large populations of simultaneously recorded cortical neurons for the first time, demonstrating strong influence of history-dependent spatial and temporal correlations on encoding in conjunction with a simple decoding scheme.…”