Spiking activity

Complex dexterous movements, such as reach-and-grasp, are driven by the coordinated activity of multiple brain regions. Each region contributes unique computations while interacting with others to control the movements. To understand the neural principles underlying this behavior across different brain regions, we recorded spiking activity in 10 brain regions while the mice performed a skilled reach-and-grasp task. We identified distinct behavioral phases, each with unique kinematic features. Single neuron analyses showed similar response percentages, with differing response strengths, across

…that mediate changes in correlated activity. One reason for this lack of understanding is the vast set of possible circuit and cellular realities that can support correlated spiking activity. To provide insight into how circuit and

…BCIs use ``decoders'‘ to relate neural activity to some desired action. Early approaches used highly specific and targeted statistical models, which required careful adaptation to different sessions or days for the same user, and lacked the ability to generalize across users or tasks. Recently, the availability of a large amount of n

…neurons changes rapidly with ongoing activity of neurons and their functional interactions. Functional networks of neurons and neural populations reflect how their interactions change with behaviors, stimulus types, and internal states. Therefore, the information propagation across a network can be analyzed through the varying topological properties of the functional networks. Our study investigates the functional networks of the visual cortex at both the single-cell and population levels. Ou

Information in sensory neurons is conveyed by spiking activity varying in time. This is quantified by the mutual-information rate (MIR), given by $\mathrm{MIR}:=\underset{\Delta t\rightarrow\infty}{\lim}\frac{{\cal I}\left(\Delta t\right)}{\Delta t}$, where ${\cal I}\left(\Delta t\right)$ is the mutual information between the activity of a spiking neuron and a stimulus over a given time window $\Delta t$. To compute it for spiking data, the spike train is typically discretized with a certain bin-size $\mathrm{dt}$. Because $\Delta t/\mathrm{dt}$ is typically well bigger than $1$, there are man

The analysis of neuronal ensembles often suffers from low spiking activity. As statistical analysis relies on sufficiently large samples, rare joint events in neuronal ensembles usually fail to reach statistical significance. We show that statistical power can be enhanced considerably by careful preselection of specific, separated temporal stripes distributed throughout the whole time series in which spiking is more likely to occur. In a data set of two-photon calcium imaging from the visual cortex of mice (Cleppien et al., 2024+), we identified short temporal stripes of interest from the un

The brain's ability to process information through temporal sequences of spiking activity is crucial for complex cognitive tasks. These sequences arise from both sensory inputs and inherent network dynamics, spanning multiple, dynamically changing timescales. For instance, the syllables in the word ‘com-pu-ter’ would produce spike pattern sequences with different timings when spoken slowly or quickly, yet the semantic meaning remains invariant. What matters is the rank order of the sequence elements. Recent biological findings[1] and computational models suggest that dendritic plateau potentia

…interactions (HOI) among neuronal activity. Here, we present a systematic approach to achieve these goals by analysing the spiking activity of simultaneously recorded neurons. To this end, we first developed a method to extract significant pairwise and triple-wise interactions among neurons. Using statistically significant data, we then analysed pairwise and HOI from large-scale spiking activities in visual areas of mice during natural movie

Social interactions in primates are possible through the ability to extract relevant information from voices, for example their identity. The anterior Temporal Voice Area (aTVA) is a region in the anterior temporal lobe of humans, macaques and marmosets specialized in the processing of voices, but the exact voice information represented by individual neurons in the aTVA remains obscure.Here we asked how aTVA neurons encode voice identity information, separating different identities and grouping together calls from a same identity. We implanted two rhesus macaques with high-density multi-electr

To date, our understanding of how the human brain retrieves, processes, and stores information remains limited. A specific challenge lies in unravelling how local plasticity mechanisms shape the transfer of information between neurons. Traditional approaches to studying local plasticity in vitro, such as patch clamp or calcium imaging, are often limited by low throughput or the ability to record only from a subset of neurons within intricate networks. In response to these challenges, we adopt a bottom-up approach to engineer low-density neuronal networks with defined architectures. Human induc

We investigate the ability of the pairwise maximum entropy model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability of multiple simultaneously active neurons. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while vary

…We inspected the ripple activity in the frequency range of 70-150 Hz in V1 and V4 of two macaque monkeys implanted with 16 Utah electrode arrays each [1]. The activity was recorded during resting state with occasional periods of drowsing. We observed prominent events with hi

Presentation of counter-phasing visual stimuli generates a steady-state-visually-evoked-potential (SSVEP), which has been widely used in electroencephalogram (EEG) studies of attention. When two stimuli are presented at different temporal frequencies (TFs), EEG shows two peaks that are modulated by attention. However, when the stimuli are spatially overlapping, they also interact in complex ways. Specifically, previous studies using invasive recordings from V1 have shown that the simultaneous presentation of full-screen gratings at different TFs leads to more attenuation when the TFs are nearb

The spiking activity of neurons is highly variable and is often described as a Poisson process. This variability comes from random fluctuations within the membrane potential (V) that leads to random spike times. Prior analysis has shown that the variability of V, in response to stimuli, became quenched, where the variance (σ2) decreased as the mean (μ) increased. However, recent in-vivo primate data has shown that σ2 also can increase with μ. Here we propose a new analytical and computational framework to show that conductance-based integrate-and-fire (C-IF) neurons can accurately capture μ an

In this talk, we propose to decipher the activity of neural networks via a “multiply and conquer” approach. This approach considers limit networks made of infinitely many replicas with the same basic neural structure. The key point is that these so-called replica-mean-field networks are in fact simplified, tractable versions of neural networks that retain important features of the finite network structure of interest. The finite size of neuronal populations and synaptic interactions is a core determinant of neural dynamics, being responsible for non-zero correlation in the spiking activity and

…can be estimated from the measured activity and can be used, for example, as a signature of the memory trace in the network. I will first discuss the inference of the timescales from the neuroscience data comprised of the short trials and introduce a new unbiased method. Then, I will apply the method to the data recorded from a local population of cortical neurons from the visual area V4. I will demonstrate that the ongoing spiking activity unfolds across at least two distinct timescales - fast and slow - an

…inputs, integrative properties, their spiking activity and output. I’ll show recent work from the lab establishing such a model system both in term

…as a consequence of the fact that spiking activity in a sending area causes post-synaptic inputs both in the same area and in other areas. Consequently, coherence depends in a lawful manner

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present machine learning frameworks that enable inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales, from diverse brain areas, and without regard to behavior. I will then demonstrate extensions that allow recovery of dynamics from two-photon calcium imaging data with surpris

…learning by using whole-brain neural activity imaging. We found that the 5-HT system integrates the sensory outcome of actions and determines future motor patterns. Second, we established a method for recording spiking activity and membrane potential from a population of neurons during behavior. We identified putative motor command signals and internal copy signals that encode millisec

…a fairly good understanding of how the spiking activity of neurons can be modelled mathematically. For cortical circuits the understanding is much more limited. Most network studies have considered stylized models with a single or a handful of neuronal populations consisting of identical neurons with statistically identical connection properties. However, real cortical networks have heterogeneous neural populations and much more structured synaptic connections. Unlike typical simplified cortical network models, real network