Main Research Questions

Working Memory


Memory is a key building block of cognition. Whether it’s short as remembering a phone number before dialing it, or long as rules of playing chess, the brain is constantly engaged in storing new memories and executing actions while integrating sensory information with past memories and subject’s internal model of the task. Working memory (WM), the ability to store and manipulate information for short periods of time, is an example where contextual information becomes an integral part of perception and memory. Despite extensive research, mechanisms underlying WM have remained obscure. A particular type of WM task, called Parametric Working Memory (PWM), is delayed comparison, the sequential comparison of two graded stimuli separated by a delay period of a few seconds, which forces the subject to maintain an analog value in memory. An identifying feature of PWM is that it is adaptive to various factors in the task, e.g the saliency of the stimuli, the delay duration, and task history. PWM is a unique WM behavioral paradigm in which different stages --sensory processing, memory maintenance, and decision making-- can be precisely parsed out and deconfounded. In addition, using memory items that lie on a quantitative, graded, continuum greatly facilitates both analysis and modeling of the data. Over the past couple of decades, primates have been the focus of research on neural correlates of PWM. However, PWM paradigms were assumed impossible in rodents due to the difficulty of training protocols. Our recent comparative human/rodent psychophysics experiments showed that rats’ PWM capacities are remarkably similar to humans’ (1, 2). The discovery of higher cognitive functions in rodents allows mental processes that once could be studied only in humans and monkeys to be addressed in the rodent brain. This makes it possible to harness the rapidly expanding toolkit of cutting-edge technical advantages uniquely offered by rodent preparations, in order to identify, manipulate, and functionally dissect neuronal populations at the circuit level.

At LIM Lab, by employing variants of PWM paradigm and designing novel memory tasks in rodents, we will address fundamental, unanswered questions about the neural basis of sensory memory formation, its maintenance, update and recall: 1) Which brain regions are required for WM? 2) What is the content of WM? (feature extraction from sensory to memory) 3) What neural mechanisms underlie maintenance and update of WM? 4) How are different brain regions recruited to support various timescales of WM? 5) Directly testing various theoretical models of WM maintenance like reverberating neural activities (Wang 1999, Brunel and Wang 2001, Leowenstein and Sompolinsky 2003), or short-term synaptic facilitations (Mongillo et al 2008).



There have been decades of research on how prior stimulus history affects perception and memory. This research has been psychophysical, theoretical (e.g. substantial work on Bayesian influences on perception), and neurophysiological, using human subjects and fMRI or EEG. Some of the theoretical proposals (e.g., the work of Ma, Pouget and colleagues) have involved models of networks of single neurons.  But, little is known about how priors are formed, represented, stored in memory, or used in combination with incoming sensory information. At LimLab we exploit rodent behavioral and physiological approaches, together with computational modeling, to study the neural underpinnings of the formation, storage, and utilization of experience-dependent priors. Specifically, we study experience-dependent priors in two different contexts: a) in conjunction with sensory WM, and b) as perceptual biases independent of their interaction with sensory WM.

Our findings in PWM tasks identify the Posterior Parietal Cortex (PPC) as a critical node supporting priors of medium length (i.e. several previous trials). However, it remains unclear whether priors are computed and represented by the PPC itself, or in conjunction with other areas heavily interconnected with PPC. Coupling activity measurement and (projection specific) perturbation here will allow us to further investigate the circuitry that sends input to PPC or reads from PPC. 

Theoretical proposals of how probabilities can be represented in the brain fall into two main categories: 1) Parameter-based schemes, in which neural activity represents parameters of a probability distribution describing statistics of the sensory variables, and 2) Sample-based schemes, in which neural activity represents sensory variables themselves. These hypotheses can be distinguished by fitting different models to recordings from PPC (and other relevant areas), in order to determine which type of information is better reflected in their activity. We use experimental results to develop and constrain new circuit-level models of the computations that underlie formation of priors.

Inferring abstract relations

Image from  Johnson et al 2009

To recognize and generalize abstract relations is a fundamental component of cognition. Both human and nonhuman organisms are sensitive to statistical regularities in sensory inputs that support functions including communication, and sequence learning. Statistical learning has been proposed as a key mechanism for extracting regularities distributed over the stream of inputs across sensory modalities and cognitive domains. Over the past 20 years, it has been shown that human neonates, rhesus monkeys, cotton-top tamarins, ratsjuvenile Bengalese finches, zebra finches, and chicks, once presented with simple rule-governed patterned sequences of inputs (e.g. ABB, AAB, and ABA), were able to distinguish patterns on the basis of the repetition.  This learning capability, despite sounding trivial, can be the foundation of further cognitive abilities. What's the neural basis of statistical learning of abstract relations? 

At LIM Lab, we recruit novel behavioral designs to probe common ability to learn various scales of temporal patterns and relations across different species.