A database of spiking activity across the mouse visual system-including the cortex, thalamus, and midbrain-while mice perform an image change detection task is presented, providing a valuable substrate for understanding sensorimotor computations in neural networks.
Abstract
Visual behavior requires coordinated activity across hierarchically organized brain circuits. Understanding this complexity demands datasets that are both large-scale (sampling many areas) and dense (recording many neurons in each area). Here, we present a database of spiking activity across the mouse visual system-including the cortex, thalamus, and midbrain-while mice perform an image change detection task. Using Neuropixels probes, we record from >75,000 high-quality units in 54 mice, mapping area-, cortical-layer-, and cell-type-specific coding of sensory and motor information. Modulation by task engagement increased across the thalamocortical hierarchy but was strongest in the midbrain. Novel images recruited an expanded cortical population and modulated late cortical (but not thalamic) responses. Population decoding and optogenetics identified a critical time window for change detection and were consistent with mice using an adaptation-based rather than image-comparison strategy. This comprehensive resource provides a valuable substrate for understanding sensorimotor computations in neural networks.
Decision-making requires the coordinated integration of sensory information, internal states, and learned rules across time. While the medial prefrontal cortex (mPFC) is known to contribute to this process, how population-level neural activity in the mPFC is temporally organized during decision-making remains unclear. Here, we investigated neuronal population activity in the mPFC of male mice using in vivo calcium imaging while animals performed a touchscreen-based visual cue-dependent location–choice task. We found that mPFC population activity exhibited a stable, low-dimensional, and continuous temporal structure that emerged reproducibly across trials. Using circular phase decoding, we found that these internal population dynamics tracked trial progression across prestimulus, stimulus, and poststimulus epochs. At the single-neuron level, a large proportion of mPFC neurons exhibited reliable, time-locked activity changes around task events. Hierarchical clustering summarized these heterogeneous responses into temporally organized response profiles spanning different phases of the task. Choice-related decoding was observed across these mPFC response profiles, whereas decoding performance in the primary somatosensory cortex remained near chance. Together, these results suggest that mPFC population activity exhibits reproducible temporal organization during visual cue-dependent choice behavior, linking single-neuron heterogeneity with population-level progression dynamics.
Takeru Suzuki, Daisuke Joho, Hiroyuki Okuno et al.· Journal of Neuroscience· 0 citations
Feature binding - how the brain encodes which features are part of other features to form representations of the coherent objects we perceive - remains an unsolved problem in neuroscience. Despite progress towards a solution, major theories either lack detailed explanations at the neuronal level or rely on biologically unrealistic simplifications, and none adequately account for the representation of hierarchical information, which is crucial to our perception of the world. To address this, a solution termed binding by polychrony has been proposed to explain how hierarchical feature relationships may be encoded at the neuronal level in a biologically realistic system. This theory relies on a phenomenon known as polychronization, where groups of neurons fire in precisely coordinated, time-locked sequences, leading to the emergence of regularly repeating spatiotemporal patterns that might encode these relationships. In this study, we explore binding by polychrony through simulations of a spiking neural network that closely aligns with the structural organisation of the primate ventral visual pathway, incorporating bottom-up, top-down, and lateral synaptic connections. By exposing the network to collections of related 2D object shapes from ecologically realistic datasets and applying spike-timing-dependent plasticity, the network self-organises such that individual neurons respond selectively to specific shape features. Furthermore, the network exhibits polychronization, giving rise to repeating spatiotemporal patterns, some of which form circuits that encode hierarchical feature relationships. Notably, these circuits are robust, even with the randomised, Poisson-distributed spike timings that represent the visual stimuli in the input layer. These results provide evidence for binding by polychrony as a feasible solution to the feature binding problem, and characterise the mechanism by which it may function. This mechanism can guide experimentalists in identifying such circuits in vivo, and could also be utilised in computer vision systems to capture more information and improve robustness to adversarial inputs.
Brian Gardner, Patrick T. McCarthy, J. Chrol-Cannon et al.· PLoS Computational Biology· 0 citations
Neural populations in auditory cortex (AC) perform sensory computations that support stimulus category decoding and flexible behavior. The underlying geometry and generalizability of these computations for natural stimuli remain poorly understood, particularly at the single-neuron level, where neurons display wide-ranging tuning specificity and temporal acuity. To address this gap, we developed ACNet, a foundation model of cortical sound encoding, to predict the time-varying activity of >3000 neurons in AC of ferrets. Training data included 42 hours of natural sounds spanning over 100 categories and were collected from multiple recording sites across multiple animals. The model achieved state-of-the-art response prediction accuracy. Model activity was succinctly captured by a low-dimensional neural manifold, which generalized (>80% of variance) across animals. Analysis of ACNet activations revealed the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex. These transformations were concomitant with the emergence of more accurate and neurally aligned auditory category decoding, even though ACNet was not explicitly trained to categorize sounds. The model also revealed tuning differences between anatomically distinct cell types. Taken together, our results demonstrate that foundation models of sensory systems can reveal generalizable computations by large neural populations.
Satyabrata Parida, Jereme C. Wingert, Jonah D. Stickney et al.· bioRxiv· 0 citations
Categorization of sensory stimuli is a fundamental computation in cognition and is critical for many aspects of learning, perception, and prediction. Auditory categorization has been linked to the neuronal activity in the auditory cortex (AC), however, it remains poorly understood how categorical representations emerge in population neuronal activity with learning. We recently showed that during categorization learning, mice use multiple complementary behavioral strategies (Collina et al., 2025). To understand neural correlates of auditory categorization, we recorded activity from AC of mice implanted with neuropixel electrodes while training them to perform auditory categorization task using two-alternative forced choice task. Approximately 30% of the recorded neurons exhibited category preference. Additionally, population decoding accuracy to categories increased with training. Training increased alignment between neurometric and psychometric thresholds, suggesting that neural activity reflected behavior. Moreover, a subpopulation of neurons was choice-selective, as their firing rate reflected the choice of the mouse across trials. Additionally, we also identified components in the population responses that signaled reward prediction and reward response in AC. Our results show that neuronal activity in the AC during categorization reflects behavioral performance. We are currently analyzing how the neuronal population activity evolves throughout auditory categorization learning.
A. Sinha, M. Aizenberg, M. Geffen· Journal of the Acoustical So...· 0 citations
Primary visual cortex (V1) is a critical substrate for mammalian vision. Traditionally, visual inputs are thought to be the main drivers of V1 activity, with internal signals playing a modulatory role. Here we show that this relationship is inverted for a large fraction of V1 neurons. In rats completing a navigation task in darkness, these neurons encoded progress along physically distinct paths with a shared turn structure. Under illumination, visual stimuli gain- modulated this path-invariant activity rather than replacing it with stimulus-driven responses. Path-invariant V1 neurons were also preferentially coordinated with hippocampal ensembles during sharp-wave ripples, linking them to a brain-wide network involved in learning. These findings establish that an internal model of the world can serve as a primary driver of activity in sensory cortex.
Kyu Hyun Lee, Philip Adenekan, Fan Gao et al.· bioRxiv· 0 citations
Summary Temporal information processing is critical for brain function, supporting neural computations such as novelty detection, adaptation, and temporal normalization. Its disruption is implicated in schizophrenia. We present a protocol for analyzing ongoing neuronal network activity using binwise decoding, trial-to-trial variability analysis, and estimation of network-intrinsic timescales (INTs). We apply these techniques to identify slow dynamics that encode the memory of recent stimuli in neuronal populations in the mouse auditory cortex and in artificial neural networks trained on a novelty-detection task. For complete details on the use and execution of this protocol, please refer to Shymkiv et al.1
Yuriy Shymkiv, Rafael Yuste· STAR Protocols· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
