Data Science & Informatics

When learning a new task, both rats and humans exhibit suboptimal behaviors plagued with superstitious ticks and idiosyncratic biases. One prominent example of such suboptimality are sequential effects: animals tend to bias their choices based on previous decisions and outcomes, hindering performance in common laboratory tasks using independent trials. Recurrent neural networks (RNN) have become a common tool to study potential neural mechanisms of cognition. Yet, RNNs typically behave much closer to optimality in laboratory tasks than real subjects.

What operations are performed by the mammalian central nervous systems to coordinate and conduct voluntary movement? Motor systems neuroscience seeks to understand these neural mechanisms. The last two decades have witnessed a transformation in this field with the use of multielectrode recordings and statistical estimation and modeling techniques. These technological advances have yielded rich, low-dimensional neural dynamics that are suggestive of the mechanisms underlying behavior.

Neuroimaging methods such as functional MRI and magneto- / electroencephalography (MEG/EEG) cannot directly reveal causal relationships between regional brain activity and behavior. To allow causal inference, transcranial magnetic stimulation (TMS) has been used to perturb local cortical activity to create temporary "virtual lesions”.

Motivation and Objectives Why are ion channels localized in subcellular dendritic compartments and is there a tight coupling of the observed localization with neuron function? We argue that this fundamental question [55, 44] can be addressed by studying the biophysical mechanisms of single neuron computation in two model systems where a large amount of electrophysiological and anatomical data is available and has been tied to the functional roles of key neurons.

Sleep is critical to memory and learning. During rapid eye movement (REM) or non-REM (NREM) sleep, subgroups of cell assemblies in hippocampal and sensory cortical circuits are reactivated in a temporally coordinated manner, forming a cortical-hippocampal-cortical loop of information processing during memory consolidation. Deciphering neural codes of hippocampal-neocortical memories during sleep would reveal important circuit mechanisms of memory consolidation.

There have been remarkable advances in imaging technology, used routinely and pervasively in many human studies, that non-invasively measures human brain structure and function. Diffusion magnetic resonance imaging (dMRI) and structural MRI (sMRI) are used to infer locations of millions of interconnected white matter fiber tracts-known as the brain connectome-that act as highways for neural activity and communication across the brain.

Learning from feedback in the real w'orld is limited by constant fluctuations in reward outcomes associated with choosing certain options or actions. Some of these fluctuations are caused by fundamental changes in the reward values of those options/actions that necessitate dramatic adjustments to the current learning strategies, like in epiphany learning or one-shot learning [Chen & Krajbich, 2017; Lee et al. 2015]. Other changes represent inherent stochasticity in an otherwise stable environment and should be tolerated and ignored to maintain stable choice preferences.

Dendritic spines mediate essentially all excitatory connections and are thus critical elements in the brain but their function is still poorly understood. In particular, a key question is whether or not they are electrical compartments. To explore this, researchers have used cable theory and Goldman-Hodgkin-Huxley-Katz models, which form a theoretical foundation responsible for many cornerstone advances in neuroscience. However, these theories break down when applied to small neuronal compartments, such as dendritic spines, because they assume spatial and ionic homogeneity.