Topic: spiking activity

While anatomical connectivity changes slowly through synaptic learning, the functional connectivity of 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

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

Timescales characterize how fast the observables change in time. In neuroscience, they 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

Despite the increasing recognition of predictive processing in circuit neuroscience, little is known about how it may be implemented in cortical circuits. We set out to develop and characterise a biological model system with layer 5 pyramidal cells in the centre. We aim to gain access to prediction and internal model generating processes by controlling, understanding or monitoring everything else: the sensory environment, feed-forward and feed-back inputs, integrative properties, their spiking activity and output. I’ll show recent work from the lab establishing such a model system both in term

Inter-areal coherence between cortical field-potentials is a widespread phenomenon and depends on numerous behavioral and cognitive factors. It has been hypothesized that inter-areal coherence reflects phase-synchronization between local oscillations and flexibly gates communication. We reveal an alternative mechanism, where coherence results from and is not the cause of communication, and naturally emerges 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

Animals control motor actions at multiple timescales. We use larval zebrafish and advanced optical microscopy to understand the underlying neural mechanisms. First, we examined the mechanisms of short-term motor 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

Starting with the work of Hodgkin and Huxley in the 1950s, we now have 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

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

It is appealing to disclose the state of an animal as well as the functional connectivity of its neuronal circuit by calculating pairwise and higher-order 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