Circuit Diagrams

Abstract A general principle of brain function is the ability to store information about the past to better predict and prepare for the future. Working memory and timing are two computational features that evolved to allow the brain to use recent information about the past to accomplish short-term goals. Working memory refers to the ability to transiently store information in a flexible manner, while timing refers to the ability to generate well timed motor responses, modulate attention in time, and predict when external events will occur.

Project Summary/Abstract In order to control specific behavioral responses, transcriptionally distinct cell types assembled into dynamic brain circuits integrate environmental information with internal states and generate purposeful motor actions. While tools have been developed to independently measure the activity dynamics, connectivity and transcriptional profiles of individual neurons, it remains challenging to integrate this diverse information into a coherent model of behavior.

PROJECT SUMMARY Rhythmic fluctuations of electrical activity in the brain are frequently observed during cognitive tasks. In many cases these oscillations are synchronized across brain regions. Synchronization in the gamma-frequency (~30- 100 Hz) range has been hypothesized to promote communication between brain regions, thereby facilitating cognitive functions. Conversely, deficits in gamma synchrony have been hypothesized to contribute to cognitive deficits at the heart of schizophrenia, Alzheimer’s disease, and related disorders.

PROJECT SUMMARY A principle aim of the NINDS is to determine how motor control is successfully implemented by the nervous system. Locomotion and balance are complex motor functions that are largely controlled by complex microcircuits that reside outside the brain. Understanding how such microcircuits function is critical to being able to treat diseases related to age, congenital disorders, and trauma in which these circuits are impaired.

Project Summary Interactions between the auditory and visual systems are among the most well-established cases of crossmodal interplay, yet the overwhelming bulk of the sensory physiology literature reflects studies examining processing confined to a single modality and we understand comparatively little about the circuitry mediating crossmodal interplay, or under what conditions such circuits are active. Audiovisual interactions exist even in primary auditory cortex (AC), with latencies too low to be mediated through purely top-down connections.

Prefrontal cortex (PFC) is critical for a range of high-level cognitive functions, such as attention and decision- making. Studying these processes is difficult, since they are covert, dynamic and under the control of the subject, rather than the experimenter. Their measurement traditionally relies on inferring their presence via behavior.

Abstract Behavior is motivated by reward, and the most powerful rewards are those that satisfy a physiologic need. For decades, neuroscientists have studied the midbrain dopamine system to understand reward and hypothalamic circuits to understand sensing of internal needs. But how these two neural systems are interact to give rise to behaviors like eating and drinking remains poorly understood. Recently, we have used approaches for simultaneous neural recording and manipulation to observe directly the communication between these two systems.

Project Summary The field of artificial intelligence (AI) has recently made remarkable advances that resulted in new and improved algorithms and network architectures that proved efficient empirically in silico. These advances raise new questions in neurobiology: are these new algorithms used in the brain? The present study focuses on a new algorithm developed in the field of reinforcement learning (RL), called distributional RL, which outperforms other state-of-the-art RL algorithms and is regarded as a major advancement in RL.

ABSTRACT Our long-term goal is to generate a complete understanding of how the cerebellum learns to improve movement in response to motor errors. Climbing fibers are thought to play an essential role in this process because they fire during erroneous movement. Their activity reliably excites Purkinje cells, eliciting calcium spikes in their dendrites that can trigger long-term synaptic plasticity at coactive parallel fiber inputs. Plasticity induction ultimately leads to corrective behavior by altering the cerebellum’s response to sensorimotor stimuli that predict mistakes.

Project Abstract Understanding how neuronal computations build up a perception of the external world is fundamental to our understanding of how the brain works. This is particularly relevant to sensory systems, where heterogenous inputs representing distinct sensory features must be re-assembled to generate a perception. How individual neurons in early stages of sensory circuits process parallel inputs, and how these circuit elements later contribute to cortical computations that bind the inputs together is completely unknown.