Welcome to the Goldman Lab


Animal behavior typically involves interactions among networks of large numbers of interconnected neurons, but experimental techniques in most systems are limited to the direct measurement of single or small numbers of neurons. My laboratory uses computational modeling to bridge the gap between single-neuron measurements and hypothesized network function. We study a wide variety of systems and seek to address questions ranging from cellular and network dynamics to sensory coding to memory and plasticity. These include the accumulation and storage of information in working memory, and the circuit basis of reinforcement learning in motor and cognitive tasks.

One major area of research is understanding the mechanisms by which networks accumulate and store information in working memory.  Recent projects seek to build cellular resolution models of how multiple brain regions work together to store working memories.  In one set of projects, we are combining data from cellular resolution recordings and perturbations of activity with detailed anatomy from connectomics to determine the mechanisms by which signals controlling the movements of the eye are mathematically integrated and stored.  In a second set of projects, we are modeling how multiple cortical and subcortical structures work together to govern the accumulation of evidence in a decision making task.  Other projects in the lab include: the circuit basis of reinforcement learning in cerebellar control of eye movements, striatal control of decision making, cortical circuitry underlying binocular vision, and circuit dynamics of the songbird system.

Select Publications:

  • Mackevicius EL, Bahle AH, Williams AH, Gu S, Denissenko NI, Goldman MS [co-corresponding author], Fee MS (2019) Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience, eLife 8:e38471.
  • Goldman MS, Fee MS (2017) Computational training for the next generation of neuroscientists, Current Opinion in Neurobiology 46:25-30.
  • Daie K, Goldman MS [co-corresponding author], Aksay ER (2015) Spatial patterns of persistent neural activity vary with the behavioral context of short-term memory, Neuron 85:847-860.
  • Fisher D, Olasagasti I, Tank DW, Aksay E, Goldman MS (2013) A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit, Neuron 79:987-1000.
  • Lim S, Goldman MS (2013) Balanced cortical microcircuitry for maintaining information in working memory, Nature Neuroscience 16:1306-1314.
  • Sanders H, Berends M, Major G, Goldman MS [co-corresponding author], Lisman JE (2013) NMDA and GABAB (Kir) conductances: the “perfect couple” for bistability, Journal of Neuroscience 33:424-429.
  • Lim S, Goldman MS (2012) Noise tolerance of attractor and feedforward memory models, Neural Computation 24:332-390.
  • Goldman MS (2009) Memory without feedback in a neural network, Neuron 61:621-634.
  • Aksay E, Olasagasti I, Mensh BD, Baker R, Goldman MS [co-corresponding author], Tank DW (2007) Functional dissection of circuitry in a neural integrator, Nature Neuroscience 10:494-504.
  • Butts DA, Goldman MS (2006) Tuning curves, neuronal variability, and sensory coding, PLoS Biology 4:e92.
  • Goldman MS (2004) Enhancement of information transmission efficiency with unreliable synapses, Neural Computation 16:1137-1162.
  • Goldman MS, Golowasch J, Marder E, Abbott LF (2001) Global structure, robustness, and modulation of neuronal models, Journal of Neuroscience 21:5229-5238.


  • Aksay laboratory, Weill Medical College of Cornell University, Website
  • BrainCOGS consortium (Brody, Pillow, Seung, Tank, Witten, Wang laboratories, Princeton University), Website
  • Raymond laboratory, Stanford University, Website
  • Fee laboratory, Massachusetts Institute of Technology, Website