About

Michael Hasselmo is a Professor in the Department of Psychological & Brain Sciences at Boston University. He serves as the Director of the Computational Neurophysiology Laboratory and the Center for Systems Neuroscience. His research focuses on the cortical dynamics of memory-guided behavior, including the effects of neuromodulatory receptors and the role of theta rhythm oscillations in cortical function. Hasselmo's work employs neurophysiological techniques to analyze intrinsic and synaptic properties of cortical circuits in rats and explores how modulators influence these properties. He also utilizes computational modeling to connect physiological data to memory-guided behavior, designing experiments with multiple single-unit recordings in behavioral tasks to test model predictions. His areas of focused research include episodic memory function and theta rhythm dynamics in the entorhinal cortex, prefrontal cortex, and hippocampal formation. His research addresses physiological effects relevant to Alzheimer’s disease, schizophrenia, and depression.

Research topics

• Computer Science
• Neuroscience
• Psychology
• Biology
• Mathematics
• Artificial Intelligence
• Mathematical analysis
• Optics
• Cognitive science
• Chemistry
• Medicine
• Physics
• Statistics
• Cognitive psychology

Selected publications

• Exploring Human Behavior During Abstract Rule Inference and Problem Solving with the Cognitive Abstraction and Reasoning Corpus

  Open MIND · 2026-02-25

  preprint

  Humans exhibit remarkable flexibility in abstract reasoning, and can rapidly learn and apply rules from sparse examples. To investigate the cognitive strategies underlying this ability, we introduce the Cognitive Abstraction and Reasoning Corpus (CogARC), a diverse human-adapted subset of the Abstraction and Reasoning Corpus (ARC) which was originally developed to benchmark abstract reasoning in artificial intelligence. Across two experiments, CogARC was administered to a total of 260 human participants who freely generated solutions to 75 abstract visual reasoning problems. Success required inferring input-output rules from a small number of examples to transform the test input into one correct test output. Participants' behavior was recorded at high temporal resolution, including example viewing, edit sequences, and multi-attempt submissions. Participants were generally successful (mean accuracy ~90% for experiment 1 (n=40), ~80% for experiment 2 (n=220) across problems), but performance varied widely across problems and participants. Harder problems elicited longer deliberation times and greater divergence in solution strategies. Over the course of the task, participants initiated responses more quickly but showed a slight decline in accuracy, suggesting increased familiarity with the task structure rather than improved rule-learning ability. Importantly, even incorrect solutions were often highly convergent, even when the problem-solving trajectories differed in length and smoothness. Some trajectories progressed directly and efficiently toward a stable outcome, whereas others involved extended exploration or partial restarts before converging. Together, these findings highlight CogARC as a rich behavioral environment for studying human abstract reasoning, providing insight into how people generalize, misgeneralize, and adapt their strategies under uncertainty.

  DOI

• Computational Structure of Egocentric Boundary Cell Responses in Retrosplenial Cortex

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The neurovascular impulse response function differentially reflects intrinsic neuromodulation across cortical regions

  Nature Neuroscience · 2026-03-26 · 1 citations

  article
  PublisherDOI

• Bifurcation of spiking oscillations from a center in resonate-and-fire neurons

  Biological Cybernetics · 2026-02-28

  articleSenior author
  PublisherDOI

• Scientific Histories of Hippocampal Research: Introduction to the Special Issue Part 2

  Hippocampus · 2026-02-13

  article1st authorCorresponding

  This introduction is for the second installment of the special issue on Scientific Histories of Hippocampal Research, with 13 new articles. Part 2 adds to the 24 articles in Part 1, and we expect a third installment in the future. The different parts of this special issue contain articles from authors directly involved in pioneering research on the hippocampus ranging from electrophysiological recordings of neuronal activity to analyses of cellular mechanisms for synaptic plasticity, to behavioral studies of the effects of hippocampal lesions. The authors were specifically invited to provide first person historical descriptions of important research and discoveries concerning hippocampal function, and were encouraged to include information about how their background and training influenced their research. In this introduction to Part 2, we will briefly review some of the main themes discussed in Part 2, which builds on many of the same themes as the articles in Part 1.

  PublisherDOI

• Exploring Human Behavior During Abstract Rule Inference and Problem Solving with the Cognitive Abstraction and Reasoning Corpus

  arXiv (Cornell University) · 2026-02-25

  articleOpen access

  Humans exhibit remarkable flexibility in abstract reasoning, and can rapidly learn and apply rules from sparse examples. To investigate the cognitive strategies underlying this ability, we introduce the Cognitive Abstraction and Reasoning Corpus (CogARC), a diverse human-adapted subset of the Abstraction and Reasoning Corpus (ARC) which was originally developed to benchmark abstract reasoning in artificial intelligence. Across two experiments, CogARC was administered to a total of 260 human participants who freely generated solutions to 75 abstract visual reasoning problems. Success required inferring input-output rules from a small number of examples to transform the test input into one correct test output. Participants' behavior was recorded at high temporal resolution, including example viewing, edit sequences, and multi-attempt submissions. Participants were generally successful (mean accuracy ~90% for experiment 1 (n=40), ~80% for experiment 2 (n=220) across problems), but performance varied widely across problems and participants. Harder problems elicited longer deliberation times and greater divergence in solution strategies. Over the course of the task, participants initiated responses more quickly but showed a slight decline in accuracy, suggesting increased familiarity with the task structure rather than improved rule-learning ability. Importantly, even incorrect solutions were often highly convergent, even when the problem-solving trajectories differed in length and smoothness. Some trajectories progressed directly and efficiently toward a stable outcome, whereas others involved extended exploration or partial restarts before converging. Together, these findings highlight CogARC as a rich behavioral environment for studying human abstract reasoning, providing insight into how people generalize, misgeneralize, and adapt their strategies under uncertainty.

  PublisherOA PDF

• Internal and external codes for location

  Nature Neuroscience · 2025-08-26

  article1st authorCorresponding
  PublisherDOI

• Modeling presynaptic inhibition by the amyloid precursor protein demonstrates one potential mechanism for preventing runaway synaptic modification in Alzheimer’s disease

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-15

  preprintOpen accessCorresponding

  Abstract INTRODUCTION Previous simulations of Hebbian associative memory models inspired the malignant synaptic growth hypothesis of Alzheimer’s disease (AD), which suggests that cognitive impairments arise due to runaway synaptic modification resulting from poor separation between encoding and retrieval. METHODS We computationally model presynaptic inhibition by the recently identified interaction of soluble amyloid precursor protein (sAPPα) with the γ-aminobutyric acid type B receptor (GABA B R) as one potential biological mechanism which can enhance separation between encoding and retrieval. RESULTS Simulations predict that the dual effect of sAPPα on long-term potentiation and presynaptic inhibition of glutamatergic synapses maintains effective associative memory function and prevents runaway synaptic modification. Moreover, computational modeling predicts that sAPPα, which interacts with the 1a isoform of GABA B R, is more effective at stabilizing associative memory than the GABA B R agonist Baclofen. DISCUSSION Molecular mechanisms that enhance presynaptic inhibition, such as sAPPα-GABA B R1a signaling, are potential therapeutic targets for preventing cognitive impairments in AD.

  PublisherOA PDFDOI

• Coding of Space and Time for Memory Function

  2025-05-29

  book-chapter1st authorCorresponding

  Abstract Physiological recordings in brain structures implicated in episodic memory reveal activity of neurons that code spatial location and time intervals, relevant to modelling of episodic memories as spatiotemporal trajectories. Neurons also disambiguate overlapping spatiotemporal trajectories, and change firing rate and rhythmicity based on running speed and head direction. These data support a model of episodic memory as a trajectory that includes speed and direction, in contrast to Tulving’s definition of episodic memory as a series of snapshots. Recent data also shows neural coding of environmental boundaries in egocentric coordinates, supporting a model of neural coding of transformations between egocentric input and memory mediated by neurons coding allocentric location. Egocentric input can be used both for coding the location along spatiotemporal trajectories and for retrieving specific viewpoints of the environment. The chapter will also include a brief discussion of the importance of mathematical models to move beyond verbal definitions.

  PublisherDOI

• Granularity of thalamic head direction cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-14

  preprintOpen access

  Abstract Head direction signaling is fundamental for spatial orientation and navigation. The anterodorsal nucleus of the thalamus (ADn) contains a high density of head direction (HD) cells that process sensorimotor inputs for subsequent synaptic integration in postsynaptic cortical areas. We tested the hypothesis that individual HD cells show differences in their firing patterns and connectivity by recording and juxtacellularly labeling single HD cells in subregions of the ADn in awake mice during passive rotation. We identified HD cells that exhibited different response profiles to light, sound, and movement. We also identified a mediolateral gradient of calretinin-expressing (CR+) ADn cells, with CR+ HD cells having narrower tuning widths, lower maximal firing rates, and different intrinsic properties compared to CR-cells. Axons of labeled HD cells could be followed to the retrosplenial cortex, with collaterals innervating the thalamic reticular nucleus (type I cells); others additionally innervated the dorsomedial striatum (type II cells). Most medial CR+ cells preferentially projected to ventral retrohippocampal regions. Surprisingly, we also identified a subpopulation of medial CR+ cells with twisted dendrites and descending axons that avoided the thalamic reticular nucleus, termed tortuosa HD cells (type III cells). We conclude that HD cells of the mouse ADn comprise distinct cell types, providing parallel head-direction-modulated sensorimotor messages to synaptic target neurons within the head direction network.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01DA016454

  NIH · $1.2M · 2008

• NIH Grant P50MH071702

  NIH · $17.6M · 2011

• NIH Grant R01MH051570

  NIH · $6.6M · 2020

• NIH Grant P50MH094263

  NIH · $17.3M · 2017

• NSF EAGER: Initiative for Physics and Mathematics of Neural Systems

  NSF · $300k · 2014–2017

Frequent coauthors

• Chantal E. Stern

  Boston University

  81 shared

• Lisa M. Giocomo

  Stanford University

  24 shared

• Howard Eichenbaum

  20 shared

• G. William Chapman

  Sandia National Laboratories

  20 shared

• Eric A. Zilli

  Meta (United States)

  19 shared

• Mark P. Brandon

  McGill University

  19 shared

• Randal A. Koene

  Voxiva (United States)

  17 shared

• H. Eugene Stanley

  Boston University

  17 shared

Labs

• Hasselmo LaboratoryPI

  The Hasselmo Laboratory focuses on the neural basis of memory and decision-making.

Education

• Other

  Oxford University