The Neuroscience and Behavior Program at UMass Amherst is an interdepartmental graduate degree-granting academic unit that brings together faculty members from various departments to provide cutting-edge research training.

Neuroscience research at UMass Amherst falls within five broadly defined areas: Neuroendocrinology; Molecular and Cellular Neuroscience; Animal Behavior and Learning; Neural and Behavioral Development; and Sensorimotor, Cognitive, and Computational Neuroscience.

Web Information

Neuroscience and Behavior Program: https://www.umass.edu/neuro/

Neuroendocrinology

This nationally recognized group of faculty has formed a Center for Neuroendocrine Studies, which emphasizes interdisciplinary and collaborative studies on the interactions between hormones, brain function, and behavior. Current research interests include hormones and neuronal development, regulation of neuroendocrine cells and behavior, circadian rhythms, environmental endocrine disruptors, stress, and neuronal integration of experiential, metabolic, and hormonal signals. There is particular emphasis on reproductive neuroendocrinology and the role of gonadal steroids in ovulation and female mating behavior and sexual differentiation

Molecular and Cellular Neuroscience

Faculty members within this area apply state-of-the-art molecular and genetic techniques to the analysis of neuronal function and development in a variety of model systems. Particularly noteworthy is a strong and highly interactive group using zebrafish as a model organism to investigate the molecular mechanisms of neural development and organization. Another major area of interest involves ...

Professor, Computer Science, University of Massachusetts at Amherst Director, BINDS Lab Core Member, Neuroscience and Behavior Program

Hava Siegelmann’s research focuses on the understanding of biologically inspired computational systems. In particular, she studies the computational and dynamical complexity of neural systems as well as genetic-networks.

Web Information

Department web page: https://www.cics.umass.edu/faculty/directory/siegelmann_hava

BINDS Lab website: http://binds.cs.umass.edu/index.html

Neuroscience and Behavior Program website:

Contact Information

Email: hava@cs.umass.edu

Phone: (413) 577-4282

Address: School of Computer Science BINDS Lab 140 Governors Drive University of Massachusetts Amherst Amherst, MA 01003-9264

Biosketch

Ph.D., Computer Science, Rutgers University (1993, Fellow of excellence),

M.Sc., Computer Science, Hebrew University (1992, Cum Laude),

B.A., Computer Science, the Technion (1988, Suma Cum Laude). Professor.

Before joining UMass, she was on the faculty of Industrial Engineering and Management at the Technion and served as the head of Information Systems Engineering. She has been a visiting professor at MIT and Harvard University.

Activites and Awards

Professor Siegelmann has been active in the International Neural Networks Society, serving on the Elected Board of Governors since 2012, she recently served as the Program Chair of the International Joint Conference on Neural Networks (IJCNN), 2011. She currently also serves as the Vice Chair on the Neural Network Technical Committee (NNTC) of the IEEE Computational Intelligence Society (CIS) as well as a Task Force member in “Towards Human-like ...

Principal Investigators: Hava Siegelmann, University of Massachusetts Amherst and Lilianne Mujica-Parodi, SUNY Stony Brook Title:   Individual variability in human brain connectivity, modeled using multi-scale dynamics under energy constraint BRAIN Category: Individuality and Variation

Clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns.  Our long-range goal is to develop these tools, focusing first upon energy constraints across synaptic-hemodynamic scales.

Abstract

Award Number: #1533693

Recent years have witnessed an explosion of interest in human brain connectivity and its relationship to brain-based disease. Functional connectivity analyses in neuroimaging have taken three general forms: cross-correlations, weighting of directed connections, and graph-theoretic approaches. Graph-theoretic measures, in particular, provide valuable insights into network features at the time of imaging. Yet, they cannot identify how the brain came to have those features, nor can they inform estimation of future network evolution. Neurological and psychiatric illnesses tend to have degenerative or oscillatory time-courses that range over decades; thus, network evolution will be critical to understanding why two individuals with the same diagnosis show markedly distinct developmental onsets and prognoses.

At the most fundamental level, clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns. These constraints include, among others: limited energy resources as per ...