Biology subjects

Lakshminarasimhan, K.

Publications and source records attributed to Lakshminarasimhan, K..

5 recordsLinked to original sources

bioRxiv2026

Coordinated Drift of Image Representations in Human EEG

Sensory representations drift over time in many animal systems, but evidence in humans remains limited. Moreover, how perception remains stable despite representational drift remains unclear. Here, we used THINGS-EEG2, a dataset with multiple sessions of repeated presentations of natural images, to test whether image-evoked EEG responses drift across recording sessions and whether this drift is geometrically constrained. We found that image-specific EEG response patterns drifted in a manner that was coordinated across channels and concentrated along dimensions that encoded image identity. Although this impaired cross-session decoding accuracy on average, its effects were heterogeneous with some image pairs becoming harder to distinguish and others becoming more separable. Critically, global distance statistics and the dimensionality of the image response space were preserved across sessions. Simulations show that downstream Hebbian networks can adapt faster to structured drift that preserves dimensionality than when drift is unstructured. These findings suggest that human sensory representations can change over time while remaining amenable to rapid compensation via unsupervised learning mechanisms.

neuroscience↗

bioRxiv2022

Specific connectivity optimizes learning in thalamocortical loops

1Cortico-thalamo-cortical loops have a central role in cognition and motor control, but precisely how thalamus contributes to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for thalamus in shaping cortical dynamics - and thus behavior - through learning. Since corticothalamic projections compress cortical activity into a lower-dimensional thalamic activity space, we hypothesized that the computational role of thalamus would depend critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic structures specialized to carry an efference copy of the cortical output benefit motor control, while corticothalamic connections that communicate the directions of highest variance in cortical activity are optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination, and found corticothalamic interactions consistent with these predictions. These results suggest that thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

neuroscience↗

bioRxiv2021

Flexible neural coding in sensory, parietal, and frontal cortices during goal-directed virtual navigation

We do not understand how neural nodes operate and coordinate within the recurrent action-perception loops that characterize naturalistic self-environment interactions. Here, we record single-unit spiking activity and local field potentials (LFPs) simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and dorsolateral prefrontal cortex (dlPFC) as monkeys navigate in virtual reality to "catch fireflies." This task requires animals to actively sample from a closed-loop virtual environment while concurrently computing continuous latent variables: (i) the distance and angle travelled (i.e., path integration) and (ii) the distance and angle to a memorized firefly location (i.e., a hidden spatial goal). We observed a patterned mixed selectivity, with the prefrontal cortex most prominently coding for latent variables, parietal cortex coding for sensorimotor variables, and MSTd most often coding for eye-movements. However, even the traditionally considered sensory area (i.e., MSTd) tracked latent variables, demonstrating path integration and vector-coding of hidden spatial goals. Further, global encoding profiles and unit-to-unit coupling (i.e., noise correlations) suggested a functional subnetwork composed by MSTd and dlPFC, and not between these and 7a, as anatomy would suggest. We show that the greater the unit-to-unit coupling between MSTd and dlPFC, the more the animals gaze position was indicative of the ongoing location of the hidden spatial goal. We suggest this MSTd-dlPFC subnetwork reflects the monkeys natural and adaptive task strategy wherein they continuously gaze toward the location of the (invisible) target. Together, these results highlight the distributed nature of neural coding during closed action-perception loops and suggest that fine-grain functional subnetworks may be dynamically established to subserve (embodied) task strategies.

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bioRxiv2017

Local differences in neuronal activation level decorrelate spatially coherent global fluctuations in gamma rhythms

Neuronal coherence is thought to constitute a unique substrate for information transmission distinct from firing rate. However, since the spatial scale of extracellular oscillations typically exceeds that of firing rates, it is unclear whether coherence complements or compromises the rate code. We examined responses in the macaque primary visual cortex and found that fluctuations in gamma-band (~40Hz) neuronal coherence correlated more with firing rate than oscillations in the local-field-potential (LFP). Although the spatial extent of LFP rhythms was broader, that of neuronal coherence was indistinguishable from firing rates. To identify the mechanism, we developed a statistical technique to isolate the rhythmic component of the spiking process and found that above results are explained by an activation-dependent increase in neuronal sensitivity to gamma-rhythmic input. Such adaptive changes in sensitivity to rhythmic inputs might constitute a fundamental homeostatic mechanism that prevents globally coherent inputs from undermining spatial resolution of the neural code.

neuroscience↗

bioRxiv2017

Inferring decoding strategies for multiple correlated neural populations

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.\n\nAuthor SummaryThe neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animals percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models can be distinguished by an experiment that measures correlations while inactivating different brain areas.

neuroscience↗