About

Kishore Kuchibhotla, PhD, is the Principal Investigator of the Kuchibhotla Lab. He grew up in Connecticut and pursued his undergraduate studies at MIT, where he majored in Physics and Brain & Cognitive Sciences, with a minor in Political Science. He earned his PhD in Biophysics at Harvard University under the mentorship of Brian Bacskai and Bradley Hyman. Following his doctoral training, he completed postdoctoral research with Robert Froemke at New York University. In 2018, he established his own laboratory at Johns Hopkins University.

Research topics

• Neuroscience
• Psychology
• Computer Science
• Cognitive psychology
• Artificial Intelligence
• Biology
• Chemistry
• Cognitive science

Selected publications

• A shared predictive architecture in the sensory cortex for statistical and reward-based learning

  Current Opinion in Neurobiology · 2026-02-27

  articleSenior authorCorresponding
  PublisherDOI

• Rapid emergence of latent knowledge in the sensory cortex drives learning

  Nature · 2025-03-19 · 23 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Spatially clustered neurons in the bat midbrain encode vocalization categories

  Nature Neuroscience · 2025-04-14 · 3 citations

  articleSenior author
  PublisherDOI

• Revealing hidden knowledge in amnestic mice

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-09

  preprintOpen accessSenior authorCorresponding

  Alzheimer's disease (AD) is a form of dementia in which memory and cognitive decline is thought to arise from underlying neurodegeneration. These cognitive impairments, however, are transient when they first appear and can fluctuate across disease progression. Here, we investigate the neural mechanisms underlying fluctuations of performance in amnestic mice. We trained APP/PS1+ mice on an auditory go/no-go task that dissociated learning of task contingencies (knowledge) from its more variable expression under reinforcement (performance). APP/PS1+ exhibited significant performance deficits compared to control mice. Using large-scale two-photon imaging of 6,216 excitatory neurons in 8 mice, we found that auditory cortical networks were more suppressed, less selective to the sensory cues, and exhibited aberrant higher-order encoding of reward prediction compared to control mice. A small sub-population of neurons, however, displayed the opposite phenotype, reflecting a potential compensatory mechanism. Volumetric analysis demonstrated that deficits were concentrated near Aβ plaques. Strikingly, we found that these cortical deficits were reversed almost instantaneously on probe (non-reinforced) trials when APP/PS1+ performed as well as control mice, providing neural evidence for intact stimulus-action knowledge despite variable ongoing performance. A biologically-plausible reinforcement learning model recapitulated these results and showed that synaptic weights from sensory-to-decision neurons were preserved (i.e. intact stimulus-action knowledge) despite poor performance that was due to inadequate contextual scaling (i.e. impaired performance). Our results suggest that the amnestic phenotype is transient, contextual, and endogenously reversible, with the underlying neural circuits retaining the underlying stimulus-action associations. Thus, memory deficits commonly observed in amnestic mouse models, and potentially at early stages of dementia in humans, relate more to contextual drivers of performance rather than degeneration of the underlying memory traces.

  PublisherOA PDFDOI

• Fast and accessible morphology-free functional fluorescence imaging analysis

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-20

  preprintOpen access

  Abstract Optical calcium imaging is a powerful tool for recording neural activity across a wide range of spatial scales, from dendrites and spines to whole-brain imaging through two-photon and widefield microscopy. Traditional methods for analyzing functional calcium imaging data rely heavily on spatial features, such as the compact shapes of somas, to extract regions of interest and their associated temporal traces. This spatial dependency can introduce biases in time trace estimation and limit the applicability of these methods across different neuronal morphologies and imaging scales. To address these limitations, the Graph Filtered Temporal Dictionary Learning (GraFT) uses a graph-based approach to identify neural components based on shared temporal activity rather than spatial proximity, enhancing generalizability across diverse datasets. Here we present significant advancements to the GraFT algorithm, including the integration of a more efficient solver for the L1 least absolute shrinkage and selection operator (LASSO) problem and the application of compressive sensing techniques to reduce computational complexity. By employing random projections to reduce data dimensionality, we achieve substantial speedups while maintaining analytical accuracy. These advancements significantly accelerate the GraFT algorithm, making it more scalable for larger and more complex datasets. Moreover, to increase accessibility, we developed a graphical user interface to facilitate running and analyzing the outputs of GraFT. Finally, we demonstrate the utility of GraFT to imaging data beyond meso-scale imaging, including vascular and axonal imaging.

  PublisherOA PDFDOI

• Rapid sensorimotor adaptation to auditory midbrain silencing in free-flying bats

  Current Biology · 2024-11-15 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Uncovering hidden sensorimotor memories in mice with Alzheimer’s disease relevant pathology

  Alzheimer s & Dementia · 2024-12-01

  articleOpen accessSenior author

  BACKGROUND: Alzheimer's disease is a progressive form of dementia where cognitive capacities deteriorate due to neurodegeneration. Interestingly, Alzheimer's patients exhibit cognitive fluctuations during all stages of the disease. Though it is thought that contextual factors are critical for unlocking these hidden memories, understanding the neural basis of cognitive fluctuations has been hampered due to the lack of behavioral approaches to dissociate memories from contextual-performance. Our previous work demonstrated that interleaving reinforced with non-reinforced ('probe') trials in an auditory go/no-go discrimination task, allows us to distinguish between acquired sensorimotor memories and their contextual expression. METHOD: Here, we used this approach, together with two-photon calcium imaging on behaving APP/PS1+ mice, to determine whether amyloid accumulation impacts underlying sensorimotor memories and/or contextual performance in an age dependent manner. RESULT: Importantly, peripheral auditory function, measured using auditory brainstem responses, was similar between WT and APP/PS1+ mice. We found that while contextual-performance is significantly impaired in 6-8mo APP/PS1+ mice compared to age-matched controls, these animals have preserved sensorimotor memories. However, 12mo APP/PS1+ mice show deficits in both domains. The impairment found in the young adults was accompanied by overall network suppression, compensatory disinhibition, reduced stimulus selectivity, and aberrant behavioral encoding in neurons of the primary auditory cortex that was partially restored in probe trials. These impairments were concentrated near Aβ plaques and were recapitulated with a reinforcement learning model that accounts for contextual signal changes. Model parameters affected were those governing contextual scaling and behavioral inhibition. CONCLUSION: These results suggest that Aβ deposition impacts circuits involved in contextual computations before those involved in acquiring knowledge and that neural circuit interventions (modulating inhibition) may hold promise to reveal hidden memories.

  PublisherOA PDFDOI

• Rapid emergence of latent knowledge in the sensory cortex drives learning

  bioRxiv (Cold Spring Harbor Laboratory) · 2024 · 8 citations

  Senior authorCorresponding
  • Neuroscience
  • Psychology
  • Cognitive psychology

  , two-photon calcium imaging of AC excitatory neurons throughout learning revealed two higher-order signals that were causal to learning and performance. First, a reward prediction (RP) signal emerged rapidly within tens of trials, was present after action-related errors only early in training, and faded at expert levels. Strikingly, silencing at the time of the RP signal impaired rapid learning, suggesting it serves an associative and teaching role. Second, a distinct cell ensemble encoded and controlled licking suppression that drove the slower performance improvements. These two ensembles were spatially clustered but uncoupled from underlying sensory representations, indicating a higher-order functional segregation within AC. Our results reveal that the sensory cortex manifests higher-order computations that separably drive rapid learning and slower performance improvements, reshaping our understanding of the fundamental role of the sensory cortex.

  PublisherOA PDFDOI

• Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance

  Nature Communications · 2024-07-17 · 21 citations

  articleOpen access

  Neuronal responses during behavior are diverse, ranging from highly reliable 'classical' responses to irregular 'non-classically responsive' firing. While a continuum of response properties is observed across neural systems, little is known about the synaptic origins and contributions of diverse responses to network function, perception, and behavior. To capture the heterogeneous responses measured from auditory cortex of rodents performing a frequency recognition task, we use a novel task-performing spiking recurrent neural network incorporating spike-timing-dependent plasticity. Reliable and irregular units contribute differentially to task performance via output and recurrent connections, respectively. Excitatory plasticity shifts the response distribution while inhibition constrains its diversity. Together both improve task performance with full network engagement. The same local patterns of synaptic inputs predict spiking response properties of network units and auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus, diverse neural responses contribute to network function and emerge from synaptic plasticity rules.

  PublisherDOI

• Performance errors during rodent learning reflect a dynamic choice strategy

  Current Biology · 2024-04-26 · 18 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• Neural circuits for flexible audiomotor learning

  NIH · $2.9M · 2020–2025

• Neural circuitry for flexible control of auditory perception and behavior

  NIH · $241k · 2015–2017

• CAREER: The control of learning rate through multi-timescale cholinergic neuromodulation

  NSF · $900k · 2022–2027

• Neural circuitry for flexible control of auditory perception and behavior

  NIH · $986k · 2018–2020

Frequent coauthors

• Ziyi Zhu

  Discovery Institute

  24 shared

• Robert C. Froemke

  18 shared

• Srdjan Ostojic

  École Normale Supérieure

  15 shared

• Cynthia F. Moss

  15 shared

• Jennifer Lawlor

  Johns Hopkins University

  13 shared

• Rupesh Kumar

  10 shared

• Yves Boubenec

  Laboratoire des Systèmes Perceptifs

  10 shared

• Tara L. Spires‐Jones

  University of Edinburgh

  9 shared

Labs

• Kuchibhotla LabPI

  Neuroscience research laboratory at Johns Hopkins University