Kenway Louie

Temporally Structured Replay of Awake Hippocampal Ensemble Activity during Rapid Eye Movement Sleep

Human dreaming occurs during rapid eye movement (REM) sleep. To investigate the structure of neural activity during REM sleep, we simultaneously recorded the activity of multiple neurons in the rat hippocampus during both sleep and awake behavior. We show that temporally sequenced ensemble firing rate patterns reflecting tens of seconds to minutes of behavioral experience are reproduced during REM episodes at an equivalent timescale. Furthermore, within such REM episodes behavior-dependent modulation of the subcortically driven theta rhythm is also reproduced. These results demonstrate that long temporal sequences of patterned multineuronal activity suggestive of episodic memory traces are reactivated during REM sleep. Such reactivation may be important for memory processing and provides a basis for the electrophysiological examination of the content of dream states.

Physical interaction of the retinoblastoma protein with human D cyclins

The retinoblastoma protein (pRb) functions as a regulator of cell proliferation and in turn is regulated by cyclin-dependent kinases. Cyclins D1 and D3 can form complexes with pRb that resemble those formed by several viral oncoproteins and are disrupted by the adenovirus E1A oncoprotein and derived peptides. These cyclins contain a sequence motif similar to the pRb-binding conserved region II motif of the viral oncoproteins. Alteration of this motif in cyclin D1 prevents formation of cyclin D1-pRb complexes while enhancing the biological activity of cyclin D1 assayed in vivo. We conclude that cyclins D1 and D3 interact with pRb in a fashion distinct from cyclins A and E, which can induce pRb hyperphosphorylation, and that cyclin D1 activity may be regulated by its association with pRb.

Reward Value-Based Gain Control: Divisive Normalization in Parietal Cortex

The representation of value is a critical component of decision making. Rational choice theory assumes that options are assigned absolute values, independent of the value or existence of other alternatives. However, context-dependent choice behavior in both animals and humans violates this assumption, suggesting that biological decision processes rely on comparative evaluation. Here we show that neurons in the monkey lateral intraparietal cortex encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives. Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates. This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation. Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.

Separating Value from Choice: Delay Discounting Activity in the Lateral Intraparietal Area

The mathematical formulations used to study the neurophysiological signals governing choice behavior fall under one of two major theoretical frameworks: “choice probability” or “subjective value.” These two formulations represent behavioral quantities closely tied to the decision process, but it is unknown whether one of these variables, or both, dominates the neural mechanisms that mediate choice. Value and choice probability are difficult to distinguish in practice, because higher-valued options are chosen more frequently in free-choice tasks. This distinction is particularly relevant for sensorimotor areas such as parietal cortex, where both value information and motor signals related to choice have been observed. We recorded the activity of neurons in the lateral intraparietal area while monkeys performed an intertemporal choice task for rewards differing in delay to reinforcement. Here we show that the activity of parietal neurons is precisely correlated with the individual-specific discounted value of delayed rewards, with peak subjective value modulation occurring early in task trials. In contrast, late in the decision process these same neurons transition to encode the selected action. When directly compared, the strong delay-related modulation early during decision making is driven by subjective value rather than the monkeys probability of choice. These findings show that in addition to information about gains, parietal cortex also incorporates information about delay into a precise physiological correlate of economic value functions, independent of the probability of choice.

Normalization is a general neural mechanism for context-dependent decision making

Understanding the neural code is critical to linking brain and behavior. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making.

Efficient coding and the neural representation of value.

To survive in a dynamic environment, an organism must be able to effectively learn, store, and recall the expected benefits and costs of potential actions. The nature of the valuation and decision processes is thus of fundamental interest to researchers at the intersection of psychology, neuroscience, and economics. Although normative theories of choice have outlined the theoretical structure of these valuations, recent experiments have begun to reveal how value is instantiated in the activity of neurons and neural circuits. Here, we review the various forms of value coding that have been observed in different brain systems and examine the implications of these value representations for both neural circuits and behavior. In particular, we focus on emerging evidence that value coding in a number of brain areas is context dependent, varying as a function of both the current choice set and previously experienced values. Similar contextual modulation occurs widely in the sensory system, and efficient coding principles derived in the sensory domain suggest a new framework for understanding the neural coding of value.

Thirst-dependent risk preferences in monkeys identify a primitive form of wealth

Significance We show that monkeys display similar risk preferences and rationality to those of humans, suggesting that despite concerns raised by earlier reports, they can serve as a model for human behavior. Standard experimental economic techniques have long allowed us to evaluate human risk attitudes, but we do not know how they relate to wealth levels, a critical variable in economic models. We find thirsty monkeys to be more risk averse and discuss implications for the role of wealth in human decision making. Experimental economic techniques have been widely used to evaluate human risk attitudes, but how these measured attitudes relate to overall individual wealth levels is unclear. Previous noneconomic work has addressed this uncertainty in animals by asking the following: (i) Do our close evolutionary relatives share both our risk attitudes and our degree of economic rationality? And (ii) how does the amount of food or water one holds (a nonpecuniary form of “wealth”) alter risk attitudes in these choosers? Unfortunately, existing noneconomic studies have provided conflicting insights from an economic point of view. We therefore used standard techniques from human experimental economics to measure monkey risk attitudes for water rewards as a function of blood osmolality (an objective measure of how much water the subjects possess). Early in training, monkeys behaved randomly, consistently violating first-order stochastic dominance and monotonicity. After training, they behaved like human choosers—technically consistent in their choices and weakly risk averse (i.e., risk averse or risk neutral on average)—suggesting that well-trained monkeys can serve as a model for human choice behavior. As with attitudes about money in humans, these risk attitudes were strongly wealth dependent; as the animals became “poorer,” risk aversion increased, a finding incompatible with some models of wealth and risk in human decision making.

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.

Adaptive neural coding: from biological to behavioral decision-making

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior.

The Temporal Dynamics of Cortical Normalization Models of Decision-making

Abstract Normalization is a widespread neural computation in both early sensory coding and higher-order processes such as attention and multisensory integration. It has been shown that during decision-making, normalization implements a context-dependent value code in parietal cortex. In this paper we develop a simple differential equations model based on presumed neural circuitry that implements normalization at equilibrium and predicts specific time-varying properties of value coding. Moreover, we show that when parameters representing value are changed, the solution curves change in a manner consistent with normalization theory and experiment. We show that these dynamic normalization models naturally implement a time-discounted normalization over past activity, implying an intrinsic reference-dependence in value coding of a kind seen experimentally. These results suggest that a single network mechanism can explain transient and sustained decision activity, reference dependence through time discounting, and hence emphasizes the importance of a dynamic rather than static view of divisive normalization in neural coding.