Biology subjects

Montijn, J. S.

Publications and source records attributed to Montijn, J. S..

8 recordsLinked to original sources

bioRxiv2024

Structured neural fluctuations can generate noise invariance and inter-areal gating at distinct timescales

The brain processes, computes, and categorizes sensory input. But even in sensory brain areas, the relationship between input signals and neuronal spiking activity is complex and non-linear. Fast subsecond fluctuations in neuronal population responses dominate the temporal dynamics of neural circuits. Traditional approaches have treated this activity as "noise" that can be averaged away by taking the mean spiking rate over wide time bins or over multiple trial repetitions, but this ignores much of the temporal dynamics that naturally occur in neural systems. We find that subsecond flares of increased population activity are layer- and cell-type specific, and large-scale computational modelling suggests they may serve as an inter-areal gating mechanism. Moreover, we find that most of the neural variability is restricted to a population-gain axis. This observation explains why neural systems can function in the presence of excessive variability: population-level spiking dynamics generate invariance to the majority of neural noise.

neuroscience↗

bioRxiv2024

Experience-dependent predictions of feedforward and contextual information in mouse visual cortex

To navigate complex environments, the visual system must prioritize unexpected inputs or omissions over predictable sensory backgrounds. This saliency assignment is a hallmark of visual processing and depends on comparing visual feedforward inputs with learned contextual surrounds, a process often conceptualized as a difference computation. However, cortical circuitry poses fundamental challenges: visual contextual feedback is predominantly amplifying, and feedback-driven inhibitory neurons necessary for subtractive computations are relatively sparse. How then can complex contextual feedback be selectively compared with feedforward input at the level of individual neurons? Here, we show that divergent plasticity rules within single pyramidal neurons provide a solution to this problem. Using two-photon calcium imaging, we recorded layer 2/3 pyramidal cell responses in mouse primary visual cortex before and after visual familiarization. We dissociated feedforward and contextual signals by comparing responses to stimuli with and without an occluded sector. Repeated feedforward responses selectively weakened through adaptation and surround suppression, whereas contextual responses strengthened and generalized across scenes. These strengthened contextual inputs amplified non-adapted responses to novel images and drove neuronal activity when occlusion of feedforward input prevented surround suppression. This produced a conserved structure of feedforward and contextual responses across mice, monkeys, and humans. Ultimately, these results reveal a cellular mechanism for saliency assignment that does not require feedback-driven inhibition. Pyramidal neurons autonomously learn the generalized statistical structure of familiar contexts, allowing them to selectively amplify improbable inputs or omissions.

neuroscience↗

bioRxiv2023

A new family of statistical tests for responses in point-event and time-series data for one- and two-sample comparisons

Quantifying whether and when signals are modulated by autonomous or external events is ubiquitous in the field of neuroscience. Existing statistical approaches, however, are not ideally suited to do this, especially when the signals under scrutiny show temporal autocorrelations. For example, a standard approach in the analysis of calcium imaging data is to use a t-test on predetermined time-windows to quantify whether neurons respond (differently) to an event of interest. While this is attractive because of its simplicity, only average signal differences can be detected. In practice, neurons often show complex response dynamics which are missed by conventional statistical tests. More advanced methods, such as bin-wise ANOVAs, do not share this drawback, but can suffer from high false-positive rates or "ghost correlations" when applied to temporally autocorrelated data. To solve this issue, we have developed three novel statistical tests extending the original ZETA-test to other use cases: 1) a test for time-series data; 2) a two-sample test to detect differences in neural responses between two conditions; and 3) a two-sample test to detect differences in time-series data between two conditions. In addition, we have improved upon the original ZETA-test. We show that our methods have a statistical sensitivity superior to t-tests and ANOVAs and work well with temporally autocorrelated data where other approaches fail. Our methods are widely applicable and we present example applications to Neuropixels data, two-photon GCaMP imaging data, and human electrocorticogram data. Open-source code for implementations in MATLAB and Python is available on GitHub and PyPi. Significance StatementNeurophysiology involves the detection of weak signals in a noisy background. Often these signals, as well as the background, are temporally autocorrelated. This means that many statistical methods, such as the ANOVA, are inappropriate. In some cases, these statistical tests produce "ghost" signals that mislead researchers into thinking there is an effect of their experiment, when in reality there is none. We have therefore developed a new family of statistical tests, the ZETA-tests, that are not negatively impacted by temporal autocorrelations. They do not use arbitrary (hyper)parameters, like bin size, and show superior statistical sensitivity compared to other methods like t-tests and ANOVAs. We provide easy-to-use implementations and expect our methods to be useful for many neuroscientists from various disciplines.

neuroscience↗

bioRxiv2023

Visuomotor experience induces functional and structural plasticity of chandelier cells

Detailed characterization of interneuron types in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modelling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to events known to elicit arousal, including locomotion and visuomotor mismatch. Repeated exposure of the mice to the virtual tunnel was accompanied by reduced visual responses of ChCs and structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.

neuroscience↗

bioRxiv2022

A universal pipeline for the alignment of electrode tracks with slice histology and electrophysiological data

Modern electrophysiological experiments in neuroscience typically measure the activity of hundreds of neurons across multiple brain regions. Each recorded neuron has to be assigned its correct area of origin, which can be estimated using histological reconstructions of electrode tracks. However, this approach has a limited accuracy with which the probe position can be determined. While some tools exist to assist in this process in mice, such tools are scarce for other species, such as rats or macaques. Moreover, even in mice, a more reliable brain area assignment may be achieved by using spiking response properties to delineate area boundaries along the recording probe. Here we present the Universal Probe Finder, which provides multi-species support for reconstructing a probes brain area per spike-sorted cluster or recording site. It can use various data formats and can calculate neuronal responsiveness to experimental interventions with only event times and spike times to provide further neurophysiological markers that simplify and improve the reliability of assigning brain areas to neurons. The program can be downloaded here: https://github.com/JorritMontijn/UniversalProbeFinder.

neuroscience↗

bioRxiv2021

Task complexity temporally extends the causal requirement for visual cortex in perception

The transformation of sensory inputs into behavioral outputs is characterized by an interplay between feedforward and feedback operations in cortical hierarchies. Even in simple sensorimotor transformations, recurrent processing is often expressed in primary cortices in a late phase of the cortical response to sensory stimuli. This late phase is engaged by attention and stimulus complexity, and also encodes sensory-independent factors, including movement and report-related variables. However, despite its pervasiveness, the nature and function of late activity in perceptual decision-making remain unclear. We tested whether the function of late activity depends on the complexity of a sensory change-detection task. Complexity was based on increasing processing requirements for the same sensory stimuli. We found that the temporal window in which V1 is necessary for perceptual decision-making was extended when we increased task complexity, independently of the presented visual stimulus. This window overlapped with the emergence of report-related activity and decreased noise correlations in V1. The onset of these co-occurring activity patterns was time-locked to and preceded reaction time, and predicted the reduction in behavioral performance obtained by optogenetically silencing late V1 activity (>200 ms after stimulus onset), a result confirmed by a second multisensory task with different requirements. Thus, although early visual response components encode all sensory information necessary to solve the task, V1 is not simply relaying information to higher-order areas transforming it into behavioral responses. Rather, task complexity determines the temporal extension of a loop of recurrent activity, which overlaps with report-related activity and determines how perceptual decisions are built.

neuroscience↗

bioRxiv2020

A parameter-free statistical test that improves the detection of neuronal responsiveness

Neurophysiological studies depend on a reliable quantification of whether and when a neuron responds to stimulation. Simple methods to determine responsiveness require arbitrary parameter choices, such as binning size, while more advanced model-based methods require fitting and hyperparameter tuning. These parameter choices can change the results, which invites bad statistical practice and reduces the replicability. New recording techniques that yield increasingly large numbers of cells would benefit from a test for cell-inclusion that requires no manual curation. Here, we present the parameter-free ZETA-test, which outperforms t-tests, ANOVAs, and renewal-process-based methods by including more cells at a similar false-positive rate. We show that our procedure works across brain regions and recording techniques, including calcium imaging and Neuropixels data. Furthermore, in illustration of the method, we show in mouse visual cortex that 1) visuomotor-mismatch and spatial location are encoded by different neuronal subpopulations; and 2) optogenetic stimulation of VIP cells leads to early inhibition and subsequent disinhibition.

neuroscience↗

bioRxiv2019

Strong information-limiting correlations in early visual areas

If the brain processes incoming data efficiently, information should degrade little between early and later neural processing stages, and so information in early stages should match behavioral performance. For instance, if there is enough information in a visual cortical area to determine the orientation of a grating to within 1 degree, and the code is simple enough to be read out by downstream circuits, then animals should be able to achieve that performance behaviourally. Despite over 30 years of research, it is still not known how efficient the brain is. For tasks involving a large number of neurons, the amount of information encoded by neural circuits is limited by differential correlations. Therefore, determining how much information is encoded requires quantifying the strength of differential correlations. Detecting them, however, is difficult. We report here a new method, which requires on the order of 100s of neurons and trials. This method relies on computing the alignment of the neural stimulus encoding direction, f', with the eigenvectors of the noise covariance matrix,{Sigma} . In the presence of strong differential correlations, f' must be spanned by a small number of the eigenvectors with largest eigenvalues. Using simulations with a leaky-integrate-and-fire neuron model of the LGN-V1 circuit, we confirmed that this method can indeed detect differential correlations consistent with those that would limit orientation discrimination thresholds to 0.5-3 degrees. We applied this technique to V1 recordings in awake monkeys and found signatures of differential correlations, consistent with a discrimination threshold of 0.47-1.20 degrees, which is not far from typical discrimination thresholds (1-2 deg). These results suggest that, at least in macaque monkeys, V1 contains about as much information as is seen in behaviour, implying that downstream circuits are efficient at extracting the information available in V1.

neuroscience↗