Temporally precise, noninvasive control of activity in well-defined neuronal populations is a long-sought goal of systems neuroscience. We adapted for this purpose the naturally occurring algal protein Channelrhodopsin-2, a rapidly gated light-sensitive cation channel, by using lentiviral gene delivery in combination with high-speed optical switching to photostimulate mammalian neurons. We demonstrate reliable, millisecond-timescale control of neuronal spiking, as well as control of excitatory and inhibitory synaptic transmission. This technology allows the use of light to alter neural processing at the level of single spikes and synaptic events, yielding a widely applicable tool for neuroscientists and biomedical engineers.

/pdf/millisecond-timescale-genetically-targeted-optical-control-382eyyrbof.pdf

Millisecond-timescale, genetically targeted optical control of neural activity.

Historically, the locus coeruleus-norepinephrine (LC-NE) system has been implicated in arousal, but recent findings suggest that this system plays a more complex and specific role in the control of behavior than investigators previously thought. We review neurophysiological and modeling studies in monkey that support a new theory of LC-NE function. LC neurons exhibit two modes of activity, phasic and tonic. Phasic LC activation is driven by the outcome of task-related decision processes and is proposed to facilitate ensuing behaviors and to help optimize task performance (exploitation). When utility in the task wanes, LC neurons exhibit a tonic activity mode, associated with disengagement from the current task and a search for alternative behaviors (exploration). Monkey LC receives prominent, direct inputs from the anterior cingulate (ACC) and orbitofrontal cortices (OFC), both of which are thought to monitor task-related utility. We propose that these frontal areas produce the above patterns of LC activity to optimize utility on both short and long timescales.

/pdf/an-integrative-theory-of-locus-coeruleus-norepinephrine-2ljsj0k79l.pdf

An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance.

The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

/pdf/the-neural-basis-of-decision-making-2p5agsty56.pdf

The Neural Basis of Decision Making

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.

The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality.

/pdf/the-physics-of-optimal-decision-making-a-formal-analysis-of-2ghxc7vpjh.pdf

The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

Decisions based on uncertain information may benefit from an accumulation of information over time. We asked whether such an accumulation process may underlie decisions about the direction of motion in a random dot kinetogram. To address this question we developed a computational model of the decision process using ensembles of neurons whose spiking activity mimics neurons recorded in the extrastriate visual cortex (area MT or V5) and a sensorimotor association area of the parietal lobe (area LIP). The model instantiates the hypothesis that neurons in sensorimotor association areas compute the time integral of sensory signals from the visual cortex, construed as evidence for or against a proposition, and that the decision is made when the integrated evidence reaches a threshold. The model explains a variety of behavioral and physiological measurements obtained from monkeys.

https://www.shadlenlab.columbia.edu/publications/publications/mike/mazurek_cerebralCtx2003.pdf

A Role for Neural Integrators in Perceptual Decision Making

A central goal of cognitive neuroscience is to elucidate the neural mechanisms underlying decision-making. Recent physiological studies suggest that neurons in association areas may be involved in this process. To test this, we measured the effects of electrical microstimulation in the lateral intraparietal area (LIP) while monkeys performed a reaction-time motion discrimination task with a saccadic response. In each experiment, we identified a cluster of LIP cells with overlapping response fields (RFs) and sustained activity during memory-guided saccades. Microstimulation of this cluster caused an increase in the proportion of choices toward the RF of the stimulated neurons. Choices toward the stimulated RF were faster with microstimulation, while choices in the opposite direction were slower. Microstimulation never directly evoked saccades, nor did it change reaction times in a simple saccade task. These results demonstrate that the discharge of LIP neurons is causally related to decision formation in the discrimination task.

/pdf/microstimulation-of-macaque-area-lip-affects-decision-making-4eeljdkgau.pdf

Microstimulation of macaque area LIP affects decision-making in a motion discrimination task

Direction-selective neurons in the middle temporal visual area (MT) are crucially involved in motion perception, although it is not known exactly how the activity of these neurons is interpreted by the rest of the brain. Here we report that in a two-alternative task, the activity of MT neurons is interpreted as evidence for one direction and against the other. We measured the speed and accuracy of decisions as rhesus monkeys performed a direction-discrimination task. On half of the trials, we stimulated direction-selective neurons in area MT, thereby causing the monkeys to choose the neurons' preferred direction more often. Microstimulation quickened decisions in favor of the preferred direction and slowed decisions in favor of the opposite direction. Even on trials in which microstimulation did not induce a preferred direction choice, it still affected response times. Our findings suggest that during the formation of a decision, sensory evidence for competing propositions is compared and accumulates to a decision-making threshold.

Microstimulation of visual cortex affects the speed of perceptual decisions

Computational models based on diffusion processes have been proposed to account for human decision-making behaviour in a variety of tasks. The basic idea is that the brain keeps accumulating noisy sensory evidence until a critical level is reached. This study explores whether such models account for the speed and accuracy of perceptual decisions in a reaction-time random dot motion direction discrimination task, and whether they explain the decision-related activity of neurons recorded from the parietal cortex (area LIP) of monkeys performing the task. While a simple diffusion model can explain the psychometric function and the mean response times of correct responses, it fails to account for the longer response times observed for errors and for the response time distributions. Here I demonstrate that a time-variant version of the diffusion model can explain the psychometric function, the mean response times and the shape of the response time distributions. Such a time-variant mechanism could be implemented in different ways, but the best match between the physiological data and model predictions is provided by a diffusion process with a gain of the sensory signals, which increases over time. It can be shown that such a time-variant decision process allows the monkey to perform optimally (in the sense of maximizing reward rate) given the risk of aborting a trial by breaking fixation before a choice can be reported. The results suggest that the brain trades off speed and accuracy not only by adjusting parameters between trials but also by dynamic adjustments during an ongoing decision.

Jochen Ditterich

Papers

A Role for Neural Integrators in Perceptual Decision Making

Microstimulation of macaque area LIP affects decision-making in a motion discrimination task

Microstimulation of visual cortex affects the speed of perceptual decisions

Evidence for time-variant decision making

Stochastic models of decisions about motion direction: behavior and physiology