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GlyT2-positive interneurons, Golgi and Lugaro cells, reside in the input layer of the cerebellar cortex in a key position to influence information processing. Here, we examine the contribution of GlyT2-positive interneurons to network dynamics in Crus 1 of mouse lateral cerebellar cortex during free whisking. We recorded neuronal population activity using Neuropixels probes before and after chemogenetic downregulation of GlyT2-positive interneurons in male and female mice. Under resting conditions, cerebellar population activity reliably encoded whisker movements. Reductions in the activity of GlyT2-positive cells produced mild increases in neural activity which did not significantly impair these sensorimotor representations. However, reduced Golgi and Lugaro cell inhibition did increase the temporal alignment of local population network activity at the initiation of movement. These network alterations had variable impacts on behavior, producing both increases and decreases in whisking velocity. Our results suggest that inhibition mediated by GlyT2-positive interneurons primarily governs the temporal patterning of population activity, which in turn is required to support downstream cerebellar dynamics and behavioral coordination., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Palacios et al.)

Internal states involve brain-wide changes that subserve coordinated behavioral and physiological responses for adaptation to changing environments and body states. Investigations of single neurons or small populations have yielded exciting discoveries for the field of neuroscience, but it has been increasingly clear that the encoding of internal states involves the simultaneous representation of multiple different variables in distributed neural ensembles. Thus, an understanding of the representation and regulation of internal states requires capturing large population activity and benefits from approaches that allow for parsing intermingled, genetically defined cell populations. We will explain imaging technologies that permit recording from large populations of single neurons in rodents and the unique capabilities of these technologies in comparison to electrophysiological methods. We will focus on findings for appetitive and aversive states given their high relevance to a wide range of psychiatric disorders and briefly explain how these approaches have been applied to models of psychiatric disease in rodents. We discuss challenges for studying internal states which must be addressed with future studies as well as the therapeutic implications of findings from rodents for improving treatments for psychiatric diseases., (© 2025 The Authors.)

The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, whose activation we refer to as ‘latent dynamics’. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFPs). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

Brain–computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent. Primary motor cortex (M1) is the typical target of brain–computer interfaces (BCIs), though many other brain regions are also critical for motor behavior. These brain regions could represent useful additional recording sites for improving BCI control. M1 activity is modulated by nonmotor variables, including attention, sensory input, and reward. Accordingly, M1's motor command signals may be better isolated by incorporating recordings from additional brain regions involved in these processes. Cognitive neuroscience research could be leveraged to further specify other useful BCI targets and to optimize training protocols. [ABSTRACT FROM AUTHOR]

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations.

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA., Competing Interests: Competing Interests V.G. holds shares in Neuralink, Corp., and Paradromics, Inc. and currently consults for Paradromics, Inc. These organizations had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

What would it mean to explain the mind in neural terms? Neural accounts of the mind are often sought in a reductionistic spirit in which neural mechanisms explain cognition. Because an individual's thoughts and behaviors are not reproducible without careful control of task, stimulus, and behavioral history, laws of the mind are the currency of psychology. Reduction may thus have to take the form familiar from physics: deriving macroscopic laws from microscopic laws. I argue that the metaphor of reduction from non‐equilibrium physics may be the most appropriate. Macroscopic patterns of neural activity, which cause behavior and thought, are slow dynamical variables that dominate the fast microscopic dynamics of individual neurons and synapses. I outline a theoretical framework in which strongly recurrent neural networks, described by neural dynamics, generate neural representations as attractor states that are embedded in low‐dimensional feature spaces. Instabilities of these states are instrumental in decision‐making and the generation of sequences of mental states that are the basis for higher cognition. Networks of such neural population dynamics form neural cognitive architectures that capture the laws of the mind. The dynamics of neural populations capture the laws of the mind This paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms' cognition and behaviour. [ABSTRACT FROM AUTHOR]

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference. [Display omitted] • Learning can change the neural activity that is used to perform familiar tasks • After learning, neural activity alters to be more appropriate for the learned task • These changes can occur without interfering with performance of the familiar task • This "memory trace" of new learning can be seen using a brain-computer interface How new tasks can be learned without interfering with old knowledge is unclear. Using a brain-computer interface, Losey et al. find that learning something new alters the neural activity used to perform a familiar task, such that neural activity remains appropriate for the new task but does not impede performance on the familiar task. [ABSTRACT FROM AUTHOR]

Partial differential equations with distributional sources—in particular, involving (derivatives of) delta distributions—have become increasingly ubiquitous in numerous areas of physics and applied mathematics. It is often of considerable interest to obtain numerical solutions for such equations, but any singular ("particle"-like) source modeling invariably introduces nontrivial computational obstacles. A common method to circumvent these is through some form of delta function approximation procedure on the computational grid; however, this often carries significant limitations on the efficiency of the numerical convergence rates, or sometimes even the resolvability of the problem at all. In this paper, we present an alternative technique for tackling such equations which avoids the singular behavior entirely: the "Particle-without-Particle" method. Previously introduced in the context of the self-force problem in gravitational physics, the idea is to discretize the computational domain into two (or more) disjoint pseudospectral (Chebyshev–Lobatto) grids such that the "particle" is always at the interface between them; thus, one only needs to solve homogeneous equations in each domain, with the source effectively replaced by jump (boundary) conditions thereon. We prove here that this method yields solutions to any linear PDE the source of which is any linear combination of delta distributions and derivatives thereof supported on a one-dimensional subspace of the problem domain. We then implement it to numerically solve a variety of relevant PDEs: hyperbolic (with applications to neuroscience and acoustics), parabolic (with applications to finance), and elliptic. We generically obtain improved convergence rates relative to typical past implementations relying on delta function approximations. [ABSTRACT FROM AUTHOR]

The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations. [ABSTRACT FROM AUTHOR]

Abstract: Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding. [Copyright &y& Elsevier]

Abstract: The phase synchronization of neuronal populations plays an important role for functional connectivity between neuronal networks. The paper introduces an efficient phase synchronization method based on the windowed harmonic wavelet transform. Three simulation models, including a simple oscillation wave contaminated with white noise, a coupled multi-component oscillator and a neural mass model were used to evaluate the performance of the windowed harmonic wavelet transform-based phase synchronization method. By comparing with the Gabor wavelet transform-based and frequently used zero-phase FIR band-pass filter/Hilbert transform-based methods, we found that the windowed harmonic wavelet transform-based phase synchronization method performed comparatively to the Gabor wavelet transform-based method and was superior to the band-pass filter/Hilbert transform-based method. The distinct advantages of the windowed harmonic wavelet transform-based method include simple implementation, high computational efficiency and free of parameter selection. Finally, the three phase synchronization methods were applied to analyze the neuronal populations between CA1 and CA3 regions of the hippocampus in the tetanus toxin model of rat epilepsy. Changes in the degree of phase coupling at different frequency bands during the pre-ictal, ictal and post-ictal states were revealed. The method based on the windowed harmonic wavelet transform can be applied to estimate the phase synchronization between neuronal populations, which is a time-effective tool for the analysis of the interaction dynamics between neuronal populations. [Copyright &y& Elsevier]

The capacity defines the ultimate fidelity limits of information transmission by any system. We derive the capacity of parallel Poisson process channels to judge the relative effectiveness of neural population structures. Because the Poisson process is equivalent to a Bernoulli process having small event probabilities, we infer the capacity of multi-channel Poisson models from their Bernoulli surrogates. For neural populations wherein each neuron has individual innervation, inter-neuron dependencies increase capacity, the opposite behavior of populations that share a single input. We use Shannon's rate-distortion theory to show that for Gaussian stimuli, the mean-squared error of the decoded stimulus decreases exponentially in both the population size and the maximal discharge rate. Detailed analysis shows that population coding is essential for accurate stimulus reconstruction. By modeling multi-neuron recordings as a sum of a neural population, we show that the resulting capacity is much less than the population's, reducing it to a level that can be less than provided with two separated neural responses. This result suggests that attempting neural control without spike sorting greatly reduces the achievable fidelity. In contrast, single-electrode neural stimulation does not incur any capacity deficit in comparison to stimulating individual neurons. [ABSTRACT FROM AUTHOR]

Abstract: The hierarchy of models of interacting neural populations with excitatory and inhibitory connections is described using second-order nonlinear ordinary differential equations. Systematic analytical and numerical studies are aimed at determining stability conditions of the coupled system. Generalized stability conditions are derived for a class of excitatory–inhibitory neural population models. Equilibrium continuation analysis is applied to interpret the obtained stability conditions. The results are discussed in the context of chaotic brain dynamics theory and chaotic itinerancy. Attractor switching in biologically relevant multi-stable systems is demonstrated. [Copyright &y& Elsevier]

A population formulation of neuronal activity is employed to s tudy an excitatory network of (spiking) neurons receiving external input as well as recurrent feedback. At relatively low levels of feedback, the network exhibits time stationary asynchronous behavior. A stability analysis of this time stationary state leads to an analytical criterion for the critical gain at which time asynchronous behavior becomes unstable. At instability the dynamics can undergo a supercritical Hopf bifurcation and the population passes to a synchronous state. Under different conditions it can pass to synchrony through a subcritical Hopf bifurcation. And at high gain a network can reach a runaway state, in finite time, after which the network no longer supports bounded solutions. The introduction of time delayed feedback leads to a rich range of phenomena. For example, for a given external input, increasing gain produces transition from asynchrony, to synchrony, to asynchrony and finally can lead to divergence. Time delay is also shown to strongly mollify the amplitude of synchronous oscillations. Perhaps, of general importance, is the result that synchronous behavior can exist only for a narrow range of time delays, which range is an order of magnitude smaller than periods of oscillation. [ABSTRACT FROM AUTHOR]

Objective . Understanding the function of brain cortices requires simultaneous investigation at multiple spatial and temporal scales and to link neural activity to an animal's behavior. A major challenge is to measure within- and across-layer information in actively behaving animals, in particular in mice that have become a major species in neuroscience due to an extensive genetic toolkit. Here we describe the Hybrid Drive, a new chronic implant for mice that combines tetrode arrays to record within-layer information with silicon probes to simultaneously measure across-layer information. Approach . The design of our device combines up to 14 tetrodes and 2 silicon probes, that can be arranged in custom arrays to generate unique areas-specific (and multi-area) layouts. Main results . We show that large numbers of neurons and layer-resolved local field potentials can be recorded from the same brain region across weeks without loss in electrophysiological signal quality. The drive's lightweight structure (≈3.5 g) leaves animal behavior largely unchanged, compared to other tetrode drives, during a variety of experimental paradigms. We demonstrate how the data collected with the Hybrid Drive allow state-of-the-art analysis in a series of experiments linking the spiking activity of CA1 pyramidal layer neurons to the oscillatory activity across hippocampal layers. Significance . Our new device fits a gap in the existing technology and increases the range and precision of questions that can be addressed about neural computations in freely behaving mice., (Creative Commons Attribution license.)

One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions. • Differences between neuronal interactions in macaque visual and prefrontal cortical areas • Pairwise interactions explain the entropy of the population response in visual cortex • Pairwise interactions capture spiking activity in visual cortex in wake and sleep states • Explaining the population dynamics in prefrontal cortex requires high-order interactions Chelaru et al. demonstrate that while pairwise interactions between visual cortical neurons capture the spiking patterns across multiple brain states, explaining the population dynamics in executive areas involves higher-order interactions. These results help elucidate the extent to which the multi-neuronal firing patterns in cortical populations can be predicted by interneuronal interactions. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) provide a powerful new tool for basic neuroscience investigators. This is because the BCI approach forges a tight link between the observation of neural activity and well-controlled manipulations of neural activity, driven by the BCI user's own volition. As in all branches of science, progress in neuroscience rests on observation and manipulation. In neuroscience, our observations are typically measurements of neural activity and behavior, and our manipulations are lesions and the addition of neural activity through direct neural stimulation, which cause changes in behavior and in the activity of other neurons. A BCI links observation and manipulation directly because the participant in the experiment observes a mapping of his or her own neural activity, and through volitional control, manipulates that activity. Researchers employing the BCI approach in a basic neuroscience context have made new progress toward understanding the neural basis of motor control, learning, and cognition. To date, most of the basic research using the BCI approach has been applied to understanding the motor system, but future basic science research objectives using the BCI approach include the neural basis of cognitive and emotional function, and explorations of the computational limits of neural circuitry., (© 2020 Elsevier B.V. All rights reserved.)