Biology subjects

Reddy, L.

Publications and source records attributed to Reddy, L..

7 recordsLinked to original sources

bioRxiv2026

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe

Memory processing requires the coordinated engagement of neuronal populations across distributed brain networks and across time. How such coordination is organized in the human medial temporal lobe (MTL) remains unclear. Here, we show that MTL population activity is dynamically structured by a transient slow-oscillatory architecture that emerges during online learning to promote offline consolidation and later recall. Using intracranial recordings that combine single-neuron spiking activity and local field potentials in human participants, we find that mnemonic engagement evokes on-demand slow-oscillatory bursts in the hippocampus. These hippocampal bursts synchronize gamma-band patterns across MTL regions, defining discrete coordination events that pace cross-regional coactivity motifs during learning. These learning-time population motifs are then selectively reactivated during hippocampal ripples in post-learning rest, and the strength of their reactivation predicts subsequent recall accuracy. Together, these findings identify a multi-scale coordination mechanism that links distributed population activity across learning, consolidation, and recall in humans.

neuroscience↗

bioRxiv2025

Modality-Agnostic Decoding of Vision and Language from fMRI

Humans perform tasks involving the manipulation of inputs regardless of how these signals are perceived by the brain, thanks to representations that are invariant to the stimulus modality. In this paper, we present modality-agnostic decoders that leverage such modality-invariant representations to predict which stimulus a subject is seeing, irrespective of the modality in which the stimulus is presented. Training these modality-agnostic decoders is made possible thanks to our new large-scale fMRI dataset SemReps-8K, released publicly along with this paper. It comprises 6 subjects watching both images and short text descriptions of such images, as well as conditions during which the subjects were imagining visual scenes. We find that modality-agnostic decoders can perform as well as modality-specific decoders, and even outperform them when decoding captions and mental imagery. Further, a searchlight analysis revealed that large areas of the brain contain modality-invariant representations. Such areas are also particularly suitable for decoding visual scenes from the mental imagery condition.

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bioRxiv2022

mulEEG: A Multi-View Representation Learning on EEG Signals

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleepstaging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels, beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.{dagger}

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bioRxiv2020

Human hippocampal neurons track moments in a sequence of events.

An indispensable feature of episodic memory is our ability to temporally piece together different elements of an experience into a coherent memory. Hippocampal "time cells" - neurons that represent temporal information - may play a critical role in this process. While these cells have been repeatedly found in rodents, it is still unclear to what extent similar temporal selectivity exists in the human hippocampus. Here we show that temporal context modulates the firing activity of human hippocampal neurons during structured temporal experiences. We recorded neuronal activity in the human brain while patients learned predictable sequences of pictures. We report that human time cells fire at successive moments in this task. Furthermore, time cells also signaled inherently changing temporal contexts during empty 10-second gap periods between trials, while participants waited for the task to resume. Finally, population activity allowed for decoding temporal epoch identity, both during sequence learning and during the gap periods. These findings suggest that human hippocampal neurons could play an essential role in temporally organizing distinct moments of an experience in episodic memory. Significance StatementEpisodic memory refers to our ability to remember the "what, where, and when" of a past experience. Representing time is an important component of this form of memory. Here, we show that neurons in the human hippocampus represent temporal information. This temporal signature was observed both when participants were actively engaged in a memory task, as well as during 10s-long gaps when they were asked to wait before performing the task. Furthermore, the activity of the population of hippocampal cells allowed for decoding one temporal epoch from another. These results suggest a robust representation of time in the human hippocampus.

neuroscience↗

bioRxiv2020

Anticipatory reinstatement of expected perceptual events during visual sequence learning

Being able to predict future events in learned sequences is a fundamental cognitive ability. Successful behavior requires the brain to not only anticipate an upcoming event, but to also continue to keep track of the sequence in case of eventual disruptions, (e.g., when a predicted event does not occur). However, the precise neural mechanisms supporting such processes remain unknown. Here, using multivariate pattern classification based on electroencephalography (EEG) activity and time-frequency amplitude, we show that the visual system represents upcoming expected stimuli during a sequence-learning task. Stimulus-evoked neural representations were reinstated prior to expected stimulus onset, and when an anticipated stimulus was unexpectedly withheld, suggesting proactive reinstatement of sensory templates. Importantly, stimulus representation of the absent stimulus co-occurred with an emerging representation of the following stimulus in the sequence, showing that the brain actively maintained sequence order even when the sequence was perturbed. Finally, selective activity was evident in the alpha-beta band (9-20 Hz) amplitude topographies, confirming the role of alpha-beta oscillations in carrying information about the nature of sensory expectations. These results show that the brain dynamically implements anticipatory mechanisms that reinstate sensory representations, and that allow us to make predictions about events further in the future.

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bioRxiv2020

Representational content of oscillatory brain activity during object recognition

Numerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theories are often tested with whole-brain recording methods of low spatial resolution (EEG or MEG), or depth recordings that provide a local, incomplete view of the brain. Opportunities to bridge the gap between local neural populations and whole-brain signals are rare. Here, using representational similarity analysis we ask which MEG oscillatory components (power and phase, across various frequency bands) correspond to low or high-level visual object representations, using brain representations from fMRI, or layer-wise representations in Deep Neural Networks (DNNs) as a template for low/high-level object representations. The results showed that around stimulus onset and offset, most transient oscillatory signals correlated with low-level brain patterns (V1). During stimulus presentation, sustained beta ([~]20Hz) and gamma (>60Hz) power best correlated with V1, while oscillatory phase components correlated with IT representations. Surprisingly, this pattern of results did not always correspond to low- or high-level DNN layer activity. In particular, sustained beta-band oscillatory power reflected high-level DNN layers, suggestive of a feed-back component. These results begin to bridge the gap between whole-brain oscillatory signals and object representations supported by local neuronal activations.

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bioRxiv2019

Theta-phase dependent neuronal coding during sequence learning in human single neurons.

The ability to maintain a sequence of items in short-term memory (STM) is a fundamental cognitive function. In the rodent hippocampus, the representation of sequentially organized spatial locations is reflected by the phase of action potentials relative to the theta oscillation (phase precession). We investigated whether the timing of neuronal activity relative to the theta brain oscillation also reflects sequence order in the medial temporal lobe of humans. We used a task in which human subjects learned sequences of pictures and recorded single neuron and local field potential activity with implanted electrodes. We report that spikes for three consecutive items in the sequence (the preferred stimulus for each cell, as well as the stimuli immediately preceding and following it) were phase-locked at distinct phases of the theta oscillation. Consistent with phase precession, spikes were fired at progressively earlier phases as the sequence advanced. These findings generalize previous findings in the rodent hippocampus to the human temporal lobe and suggest that encoding stimulus information at distinct oscillatory phases may play a role in maintaining their sequential order in STM.

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