Skip to main content
Indiana University Bloomington

Ehren Newman

Dr. Ehren Newman

Assistant Professor

 

ehnewman [at] indiana.edu | personal website

office: PY 347

lab: Newman Lab
   

 

Systems Neuroscience, Learning and Memory, Circuits, High-density electrophysiology, Optogenetics, Neural rhythms, Theta & Gamma Rhythms, Acetylcholine, Neuromodulation, Neural information processing, Modeling of neural circuits

Educational Background

  • 2008-2015 - Postdoc, Boston University
  • 2008 - PhD, Princeton University
  • 2004 - MA, Princeton University
  • 2002 - BS, Brandeis University

Areas of Interest

  • Neuroscience
  • Cognitive Science

Research Topics

  • Information processing by neural circuits
  • Neurophysiology of learning and memory
  • Modulation of circuit dynamics
  • Neuromodulation by acetylcholine
  • Alzheimer's disease

Research Summary:

How do neural circuits give rise to memory? To answer this question, we combine optogenetics, DREADDs, pharmacology, and behavioral manipulations with high-density tetrode and depth-probe recordings of neural activity in awake behaving rats. We are most interested in areas known as the hippocampus, medial septum, and entorhinal cortex, all of which have all been shown to have important roles in memory in humans and animals. We use computational modeling to bridge this experimental work and human memory processing. This work suggests that neural rhythms allow the brain to code, manipulate and store information and that these dynamics are regulated by acetylcholine.

We seek to characterize the functional circuit-level processes of memory with which specific disorders can be understood, diagnosed, treated and cured. Memory is well specified by cognitive principles such as encoding, mnemonic search, recall, proactive interference, and retrieval induced forgetting. In contrast, the processing dynamics behind these operations take place at the circuit level and remain woefully unspecified. Pathologies of memory associated with Alzheimer's Disease, post-traumatic stress disorder, schizophrenia, autism and normal aging place an enormous burden on individuals, relationships and societies. The development of treatments and cures for these pathologies require a functional understanding of the circuit-based mechanisms of memory.

Lab Goals:

We focus our energy to make progress on three fronts:

  1. Visualizing neural information processing
    Although memory is a well-defined concept in psychology, the biological basis of memory formation and retrieval is poorly understood. We seek to develop a systems-level understanding of how memories are encoded and retrieved. One outcome will be the development of a ‘functional lens’ through which psychological disorders can be understood and diagnosed, analogous to the microscope used by Alois Alzheimer to identify plaques and tangles in his famous patient. Toward this end, the lab seeks to develop new tools to observe and track neural activation.
  2. Understanding role of acetylcholine for memory formation
    Acetylcholine is a signaling molecule that is utilized by almost every neural circuit in mammals although the specific influences of this molecule differs substantially between circuits. Acetylcholine is well recognized to play a key role in allowing new memories to be formed but the mechanism by which it has this influence remains unknown. We seek to identify the circuits and mechanisms by which acetylcholine influences memory processing.
  3. Detecting Alzheimer's disease
    Alzheimer's disease is the 6th leaded cause of death in the United States and is projected to cost the nation $1,100,000,000,000 ($1.1 TRILLION!) annually by 2050*. While modern drugs can slow its progression diagnosing Alzheimer's disease remains difficult. An estimated 65% of those afflicted remain undiagnosed. Our goal is to identify new clinically relevant biomarkers of Alzheimer's disease that will facilitate diagnosing the disease and increase the rate of early detection.
    http://www.alz.org/facts/overview.asp

Approaches

High-density electrophysiology generates a spatially and temporally precise record of neural activity. Arrays of tetrodes and depth-probes make it possible to track the spiking of tens to hundreds of isolated neurons and the aggregate synaptic input as reflected in the local field potential (LFP) simultaneously. In our research, we compare population activity to behavior and LFP to identify the principles of neural information processing that are central to memory.

Circuit manipulation helps identify the physiological bases of neural information processing by reversibly suppressing or facilitating specific circuit componentTs. A standard method is to introduce a receptor activated by light (optogenetics) or a designer drug (DREADDs) into select cells that can be used to temporarily stimulate or silence action potentials. Pharmacology can also manipulate specific signaling pathways. Our research uses these methods to identify the role of acetylcholine in governing mnemonic processing.

Computational models translate empirical observations into concrete principles, bridge disconnected literatures, and motivate new empirical studies. We use models to bridge neural physiology, as understood from in vitro studies, to memory, as understood from human behavior (Newman, Shay & Hasselmo, 2012). A recently developed model to account for intracellular recordings of grid cells that explains how networks of distinct neural populations cooperate to track animal behavior (Onslow, Hasselmo, & Newman, 2014). In another model, rhythmic fluctuations of inhibition have been shown to drive new learning and account for particularities of human memory (Norman*, Newman*, Detre & Polyn, 2006; Norman, Newman & Detre, 2007; Norman, Newman, & Perotte, 2005).

Representative Publications

An always-updated list can be found here: http://www.ncbi.nlm.nih.gov/pubmed?term=Newman+Ehren%5BAuthor%5D

Newman, E.L., Hasselmo, M.E. (2014) Grid cell properties vary as a function of theta phase locking preferences in the rat medial entorhinal cortex. Frontiers in Systems Neuroscience. 8:193. doi: 10.3389/fnsys.2014.00193

Climer, J.R., DiTullio, R., Newman, E.L., Hasselmo, M.E., Eden, U.T. (2015) Examination of rhythmicity of extracellularly recorded neurons in the entorhinal cortex. Hippocampus. 25(4), 460-473. doi: 10.1002/hipo.22383

Newman, E.L., Climer, J.R., Hasselmo, M.E. (2014) Grid cell spatial tuning reduced following systemic muscarinic receptor blockade. Hippocampus. 24(6), 643-655. doi: 10.1002/hipo.22253

Newman, E.L., Hasselmo, M.E. (2014) CA3 sees the big picture while dentate gyrus splits hairs. Neuron, 81(2).

Onslow, A.C.E., Hasselmo, M.E., Newman, E.L. (2014) DC-shifts in amplitude in-field generated by an oscillatory interference model of grid cell firing. Frontiers in Systems Neuroscience. 8(1).

Newman, E.L., Gillet, S.N., Climer, J.R., Hasselmo, M.E. (2013) Cholinergic blockade reduced theta-gamma phase amplitude coupling and speed modulation of theta frequency consistent with behavioral effects on encoding. Journal of Neuroscience, 33(50), 19635-19646.

Climer, J.R., Newman, E.L., Hasselmo, M.E. (2013) Phase coding by grid cells in unconstrained environments: Two-dimensional phase precession. European Journal of Neuroscience, 38(4): 2526-41.

Newman, E.L., Shay, C.F., Hasselmo, M.E. (2012) Malignant synaptic growth and Alzhiemer's disease. Future Neurology, 7(5), 557-571.

Newman, E.L., Gupta, K., Climer, J.R., Monaghan, C.K., Hasselmo, M.E. (2012) Cholinergic modulation of cognitive processing: insights drawn from computational models. Frontiers in Behavioral Neuroscience, 6(24).

Newman, E.L., Norman, K.A. (2010). Moderate excitation leads to weakening of perceptual representations. Cerebral Cortex 20(11) pp. 2760-70.

Hasselmo, M.E., Brandon, M.P., Yoshida, M., Fransen, E., Giocomo, L.M., Heys, J., Newman, E.L. (2009). A phase code for memory could arise from circuit mechanisms in entorhinal cortex. Neural Networks 22(8) pp. 1129-38.

Newman, E.L., Caplan, J.B., Kirschen, M.P., Korolev, I.O., Sekuler, R., Kahana, M.J. (2007) Learning your way around town: Virtual taxi drivers reveal secrets of navigational learning. Cognition 104(2), 231-253.

Norman, K.A., Newman, E.L. & Detre, G.J. (2007). A neural network model of retrieval-induced forgetting. Psychological Review 114 (4), 887-953.

Norman*, K.A., Newman*, E.L., Detre, G.J., Polyn, S.M. (2006) How inhibitory oscillations can train neural networks and punish competitors. Neural Computation 18:1577-1610.

Norman, K.A., Newman, E.L., Perotte, A.J. (2005) Methods for reducing interference in the complementary learning systems model: Oscillating inhibition and autonomous memory rehearsal. Neural Networks 18:1212-1228.

Ekstom, A.D., Kahana, M.J., Caplan, J.B., Fields T.A., Isham, E.A., Newman, E.L., Fried I. (2003). Cellular networks underlying human spatial navigation. Nature 425:184-188.

Caplan, J.B., Madsen, J.R., Schulze-Bonhage, A., Aschenbrenner-Scheibe, R., Newman, E.L., Kahana, M.J. (2003) Human theta oscillations related to sensorimotor integration and spatial learning. Journal of Neuroscience 23(11):4726-4736.