Hussain Shuler Lab

The Hussain Shuler lab seeks to understand the learning algorithms and representational architectures the brain uses to produce adaptive behavior. In particular, we study the domain of time-investment problems, where time must be spent to gain future reward. Temporal difference reinforcement learning (TDRL) is an example of a learning algorithm commonly used to explain human and animal reward learning and decision-making, while the state space to which TDRL is applied is the representational architecture. We use quantitative behavioral tasks, in vivo electrophysiology, imaging of neuromodulator release, and causal manipulations to test and develop better normative models and understand their neural instantiations. We also address these questions in models of Alzheimer’s disease to understand how neurodegeneration, especially of the cholinergic system, impairs decision-making.

Outcome Prediction

Humans, like other animals, decide between courses of action based on their expected outcomes. Yet, how expectations of outcomes of given amounts, delays, and valence are learned and produced by the brain in response to predictive events is little understood. This ability of the brain to model how much and when to expect rewards or punishments (outcome prediction) features prominently in reinforcement learning theory. However, neither our understanding of how a neural circuit can produce, nor how it can learn to produce, outcome prediction signaling to inform decision-making behavior is satisfactory.

While it is commonly held that ‘higher-order’ brain regions are responsible for imparting behavioral significance on sensory input, Dr. Hussain Shuler demonstrated that pairing visual stimuli with subsequent reward leads to the emergence of reward-timing activity in the primary visual cortex (V1). Subsequent work in the lab has provided evidence that this activity is generated within V1 itself, informed by acetylcholine and produced by circuit motifs of inhibitory interneurons. Visual cortex projects directly to the dorsal striatum, motivating our study of outcome prediction in the visual corticostriatal circuit and its role in decision-making. 

Representational Architectures

A learning rule, like the one used in TDRL to assign value to states, is just one component of a learning agent. The specific form and connectivity of a state space, the representational architecture, has a profound impact on the behavior of the agent. We use modeling, quantitative behavior, and electrophysiology to study how representational architectures are implemented in the brain. 

Alzheimer’s Disease

Alzheimer’s disease (AD) degrades the ability to learn and make appropriate decisions. As the basal forebrain cholinergic system is highly susceptible to amyloidosis, one neuromodulator that is especially implicated in cognitive decline caused by AD is acetylcholine, which is essential in many forms of learning and memory. While higher-order brain areas associated with decision-making have been intensively investigated as they relate to AD, recently there has been a call for greater focus on the consequences of AD in sensory- and motor-related areas. Observation of neural activity from the visual cortex and the dorsal striatum reveal visually-cued timing activity to expected reward, providing a window into the process of transforming visual cues into reward-seeking motor action. By combining a mouse model of AD that develops amyloidosis with a line that affords a means to control and functionally image cholinergic axons, the effects of amyloidosis on cholinergic-dependent interval timing activity can be assessed, neurally and behaviorally, compared to that caused by the normal course of aging, and rescued by augmenting cholinergic signaling of reward using a novel optogenetic intervention. 

The Hussain Shuler Lab is a part of the Solomon H. Snyder Department of Neuroscience and a member of the Kavli Neuroscience Discovery Institute at the Johns Hopkins University School of Medicine.

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