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Memories of the images that we have seen are thought to be reflected in the reduction of neural responses in high-level visual areas such as inferotemporal (IT) cortex, a phenomenon known as repetition suppression (RS). We challenged this hypothesis with a task that required rhesus monkeys to report whether images were novel or repeated while ignoring variations in contrast, a stimulus attribute that is also known to modulate the overall IT response. The monkeys’ behavior was largely contrast invariant, contrary to the predictions of an RS-inspired decoder, which could not distinguish responses to images that are repeated from those that are of lower contrast. However, the monkeys’ behavioral patterns were well predicted by a linearly decodable variant in which the total spike count was corrected for contrast modulation. These results suggest that the IT neural activity pattern that best aligns with single-exposure visual recognition memory behavior is not RS but rather sensory referenced suppression: reductions in IT population response magnitude, corrected for sensory modulation.

Recognizing traffic signs is a fundamental perception task for automated driving systems and requires high accuracy under strict latency and energy constraints. Convolutional neural networks (CNNs) achieve strong performance but can be computationally demanding for embedded platforms. Spiking convolutional neural networks (SCNNs) offer an event-driven alternative that can reduce computation through sparse activity, yet their accuracy often degrades under very low-latency settings with few time steps. To improve spike-based inference under strict runtime constraints, we integrate a neural population decoding layer at the output stage and evaluate directly trained SCNNs with and without population decoding against a CNN baseline on the German Traffic Sign Recognition Benchmark (GTSRB). The best SCNN without population decoding achieved 98.85% test accuracy at 30 time steps, exceeding the CNN baseline of 98.38%. Population decoding improved performance in the low-latency regime, reaching 98.31% accuracy at a single time step, corresponding to an improvement of 0.56% over the SCNN without population decoding at the same temporal setting. Using an operation-based energy estimation, the SCNNs achieved over 14 times higher energy efficiency than the CNN at one time step. Overall, the results demonstrate that directly trained SCNNs can surpass a comparable CNN while enabling flexible trade-offs between accuracy, inference time, and energy efficiency. In particular, population decoding proves beneficial when operating under strict latency constraints.

Understanding how the brain computes eye position is essential to unraveling high-level visual functions such as eye movement planning, coordinate transformations and stability of spatial awareness. The lateral intraparietal area (LIP) is essential for this process. However, despite decades of research, its contribution to the eye position signal remains controversial. LIP neurons have recently been reported to inaccurately represent eye position during a saccadic eye movement, and to be too slow to support a role in high-level visual functions. We addressed this issue by predicting eye position and saccade direction from the responses of populations of LIP neurons. We found that both signals were accurately predicted before, during and after a saccade. Also, the dynamics of these signals support their contribution to visual functions. These findings provide a principled understanding of the coding of information in populations of neurons within an important node of the cortical network for visual-motor behaviors. Whenever we reach towards an object, we automatically use visual information to guide our movements and make any adjustments required. Visual feedback helps us to learn new motor skills, and ensures that our physical view of the world remains stable despite the fact that every eye movement causes the image on the retina to shift dramatically. However, such visual feedback is only useful because it can be compared with information on the position of the eyes, which is stored by the brain at all times. It is thought that one important structure where information on eye position is stored is an area towards the back of the brain called the lateral intraparietal cortex, but the exact contribution of this region has long been controversial. Graf and Andersen have now clarified the role of this area by studying monkeys as they performed an eye-movement task. Rhesus monkeys were trained to fixate on a particular location on a grid. A visual target was then flashed up briefly in another location and, after a short delay, the monkeys moved their eyes to the new location to earn a reward. As the monkeys performed the task, a group of electrodes recorded signals from multiple neurons within the lateral intraparietal cortex. This meant that Graf and Andersen could compare the neuronal responses of populations of neurons before, during, and after the movement. By studying neural populations, it was possible to accurately predict the direction in which a monkey was about to move his eyes, and also the initial and final eye positions. After a movement had occurred, the neurons also signaled the direction in which the monkey's eyes had been facing beforehand. Thus, the lateral intraparietal area stores both retrospective and forward-looking information about eye position and movement. The work of Graf and Andersen confirms that the LIP has a central role in eye movement functions, and also contributes more generally to our understanding of how behaviors are encoded at the level of populations of neurons. Such information could ultimately aid the development of neural prostheses to help patients with paralysis resulting from injury or neurodegeneration.

Mammals rely on vision and self-motion information in nature to distinguish directions and navigate accurately and stably. Inspired by the mammalian brain neurons to represent the spatial environment, the brain-inspired positioning method based on multi-sensors’ input is proposed to solve the problem of accurate navigation in the absence of satellite signals. In the research related to the application of brain-inspired engineering, it is not common to fuse various sensor information to improve positioning accuracy and decode navigation parameters from the encoded information of the brain-inspired model. Therefore, this paper establishes the head-direction cell model and the place cell model with application potential based on continuous attractor neural networks (CANNs) to encode visual and inertial input information, and then decodes the direction and position according to the population neuron firing response. The experimental results confirm that the brain-inspired navigation model integrates a variety of information, outputs more accurate and stable navigation parameters, and generates motion paths. The proposed model promotes the effective development of brain-inspired navigation research.

Primary motor cortex (MI) and parietal area PE both participate in cortical control of reaching actions, but few studies have been able to directly compare the form of kinematic encoding in the two areas simultaneously during hand tracking movements. To directly compare kinematic coding properties in these two areas under identical behavioral conditions, we recorded simultaneously from two chronically implanted multielectrode arrays in areas MI and PE (or areas 2/5) during performance of a continuous manual tracking task. Monkeys manually pursued a continuously moving target that followed a series of straight-line movement segments, arranged in a sequence where the direction (but not length) of the upcoming segment varied unpredictably as each new segment appeared. Based on recordings from populations of MI (31–143 units) and PE (22–87 units), we compared hand position and velocity reconstructions based on linear filters. We successfully reconstructed hand position and velocity from area PE (mean r 2  = 0.751 for position reconstruction, r 2  = 0.614 for velocity), demonstrating trajectory reconstruction from each area. Combing these populations provided no reconstruction improvements, suggesting that kinematic representations in MI and PE encode overlapping hand movement information, rather than complementary or unique representations. These overlapping representations may reflect the areas’ common engagement in a sensorimotor feedback loop for error signals and movement goals, as required by a task with continuous, time-evolving demands and feedback. The similarity of information in both areas suggests that either area might provide a suitable target to obtain control signals for brain computer interface applications.

Eye tracking experiments show that neurons respond rapidly to eye movements, allowing our view of the world to remain stable.Eye tracking experiments show that neurons respond rapidly to eye movements, allowing our view of the world to remain stable.

We discuss the ability of dynamic neural fields to track noisy population codes in an online fashion when signals are constantly applied to the recurrent network. To report on the quantitative performance of such networks we perform population decoding of the ‘orientation’ embedded in the noisy signal and determine which inhibition strength in the network provides the best decoding performance. We also study the performance of decoding on time-varying signals. Simulations of the system show good performance even in the very noisy case and also show that noise is beneficial to decoding time-varying signals.

Animals readily execute learned behaviors in a consistent manner over long periods of time, and yet no equally stable neural correlate has been demonstrated. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which captures the dominant co-variation patterns within the neural population, must be preserved across time. We recorded from populations of neurons in premotor, primary motor and somatosensory cortices as monkeys performed a reaching task, for up to 2 years. Intriguingly, despite a steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. The stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based directly on the recorded neural activity degraded substantially. We posit that stable latent cortical dynamics within the manifold are the fundamental building blocks underlying consistent behavioral execution.Gallego, Perich et al. report that latent dynamics in the neural manifold across three cortical areas are stable throughout years of consistent behavior. The authors posit that these dynamics are fundamental building blocks of learned behavior.

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans .

Neural circuits construct distributed representations of key variables—external stimuli or internal constructs of quantities relevant for survival, such as an estimate of one’s location in the world—as vectors of population activity. Although population activity vectors may have thousands of entries (dimensions), we consider that they trace out a low-dimensional manifold whose dimension and topology match the represented variable. This manifold perspective enables blind discovery and decoding of the represented variable using only neural population activity (without knowledge of the input, output, behavior or topography). We characterize and directly visualize manifold structure in the mammalian head direction circuit, revealing that the states form a topologically nontrivial one-dimensional ring. The ring exhibits isometry and is invariant across waking and rapid eye movement sleep. This result directly demonstrates that there are continuous attractor dynamics and enables powerful inference about mechanism. Finally, external rather than internal noise limits memory fidelity, and the manifold approach reveals new dynamical trajectories during sleep.