Biology subjects

Tiesinga, P.

Publications and source records attributed to Tiesinga, P..

15 recordsLinked to original sources

bioRxiv2026

Planning in Nonhuman Primates Emerges from Structure Knowledge and is Distinct from Attention, Working Memory, Effort Control, and Learning

Forward planning involves mentally simulating future choices. Such mental simulation may constitute an independent cognitive operation, or it may be intricately linked to canonical cognitive operations supporting working memory of choice options, covert attentional information sampling, forming memory models of problem structure and effort control for pursuing future goals. Here, we test the relative independence of planning in non-human primates with an object-sequence planning task assessed together with an assay of simpler cognitive tasks. The sequence task unexpectedly masked objects at future serial positions probing NHPs to plan ahead serial future choices. We found that five of six subjects planned ahead 2-3 ordinal positions. Planning depth was predicted by knowledge of the serial structure but constituted a unique cognitive factor that was distinct from sequence memory, object memory, and learning efficacy. Similarly, planning was segregated from working memory capacity, attention, inhibitory control and effort control. These findings suggest that planning ahead constitutes a separate construct not reducible to other domain-general cognitive operations, and emerges in NHPs that successfully form an internal model of the problem structure.

animal behavior and cognition↗

bioRxiv2026

Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights

Stroke is a leading cause of long-term disability, often resulting in persistent motor impairments reflecting disruptions in large-scale brain networks. While electroencephalography (EEG) has long been used to monitor neurophysiological changes following stroke, an integrated framework capturing spatiotemporal dynamics would help understand changes over time. In this study, we analysed resting-state EEG from stroke patients at one week (Session 1) and three months (Session 2) post-stroke to investigate electrophysiological biomarkers of motor recovery, indexed by changes in the Fugl-Meyer scale ({Delta}FM ). We quantified spectral properties, focusing on the relative alpha band power, microstate metrics such as mean duration, complexity, and transition probabilities, and measures of metastability and synchrony derived from the Kuramoto Order Parameter. Among all the EEG measures, the longitudinal change in relative alpha power emerged as the strongest single correlate of motor improvement, accounting for the largest proportion of variance among the examined EEG measures. Although metastability and synchrony alone did not reach statistical significance, they showed moderate positive correlations with {Delta}FM, particularly in the alpha and theta ranges, and once combined with alpha power, added 26% in explaining the variance in {Delta}FM . Microstate parameters did not explain additional variance in recovery once alpha power and network-level dynamics were considered. A hierarchical model combining alpha power, metastability/synchrony, and microstates explained over 78% of the variance in {Delta}FM, indicating that stroke recovery involves restoring balanced alpha oscillations and flexible large-scale brain coordination. Future research with larger samples and more frequent longitudinal assessments is required to confirm the prognostic utility of integrated EEG biomarkers for guiding personalised stroke rehabilitation strategies.

neuroscience↗

bioRxiv2025

Two Distinct Attentional Priorities Guide Exploratory and Exploitative Gaze in Parallel

Gaze is directed to visual objects that are informative, reward-predictive, or novel. These gaze preferences may reflect the parallel influence of two separable attention systems: Exploratory attention prioritizing uncertainty and exploitative attention prioritizing learned information about reward. We tested this hypothesis in nonhuman primates learning feature-based attention to objects that had either previously learned reward associations or were novel. The reward history of features slowed down learning by attracting fixations of non-rewarded distractors that were previously targets. This reward history bias persisted in fixations used to choose objects even after choice accuracy stabilized. In contrast, fixational sampling that preceded a choice showed negligible history biases that were overcome quickly in favor of wider exploratory sampling. Quantifying the exploratory value object features with a Parallel Belief States model of attention confirmed that exploratory fixational sampling was unaffected by reward history, while exploitative fixations that committed to a decision showed persistent target history biases. These findings suggest that gaze is guided by two separable attentional priorities in parallel. Exploratory attention prioritizes uncertain items and instantiates information sampling, while exploitative attentional priority guides gaze to current and previously goal-relevant features.

animal behavior and cognition↗

bioRxiv2025

Parallel Belief States account for Learning and Updating of Attentional Priorities in Multidimensional Environments

Inferring the behavioral relevance of visual features is difficult in multidimensional environments as many features could be important. One solution could involve tracking the experience with multiple features and using attentional control to decide which subset of features to explore and chose. Here, we characterize this attentional control process with a model of parallel belief states and test it with a task requiring the learning and updating of attention to features with varying selection histories and motivational costs. We found that exploring and exploiting features was accounted for by a model that tracks the latent beliefs about the relevance of multiple features in parallel. These parallel belief states accounted for the fast learning of feature-based attention, for perseverative selection history effects for features that were previously relevant, and for enhanced learning performance when the motivational costs of making errors increased. Taken together, these results quantify how multiple parallel belief states guide exploration and exploitation of feature-based attention during learning. We suggest parallel belief states represent attentional priorities that are read out by a competitive attentional control process to explore and exploit those visual objects in multidimensional environments that are believed to be relevant. Significance statementDuring goal-directed behavior attention is allocated to features relevant for the behavioral goal. But in real-world settings with multidimensional objects, it is often unknown which features are maximally relevant and should be attentionally prioritized. We found a solution to this problem by quantifying the hidden beliefs about the relevance of multiple features in parallel. By tracking belief states about feature relevance we found that subjects consider multiple features in parallel during the learning of feature-based attention. These belief states correspond to attentional priorities and explained when attention is biased towards previously relevant but now irrelevant features, and when learning about relevant features is enhanced by motivational incentives. These findings quantify the parallel hidden beliefs that guide attention in complex environments.

neuroscience↗

bioRxiv2024

Anterior Cingulate Cortex Causally Supports Meta-Learning

Learning the reward structure of complex environments can be achieved using reinforcement learning processes augmented with cognitive strategies. Among these strategies is the adjustment of the exploration-exploitation trade-off to increase exploration when behavior gets stuck and increase exploitation when reward contingencies remain stable. Here we tested how the anterior cingulate cortex (ACC) and the striatum causally support adaptive cognitive strategies augmenting reinforcement learning. We electrically microstimulated the ACC or the striatum in nonhuman primates at the time they chose multidimensional objects to learn about their reward values, while varying target feature uncertainty and the motivational saliency of the chosen objects. We found that stimulation of the ACC and the striatum affected adaptive strategies and reinforcement learning when target feature uncertainty was high, but in opposite ways. ACC-stimulation impaired learning and sustaining correct responses, while striatum-stimulation on average improved learning from rewarding outcomes. Behavioral modeling showed that stimulation affected the same mechanisms but in opposite ways. ACC stimulation impaired the monitoring of outcome uncertainty for adapting exploration-exploitation and reduced the ability to lower prediction errors during learning, while striatum stimulation enhanced the monitoring of outcome uncertainty and prediction-error based updating of object values. Stimulation did not alter the use of working memory or attentional filtering as alternative learning strategy. These opposing behavioral stimulation effects were associated with the ACC having populations of neurons that fired stronger during choices that were more uncertain, had a lower value, and that tracked error history, while striatum neurons more likely encoded higher values and more certain choices. In summary, microstimulation during object choices suggest that ACC and the striatum causally adapt exploration-exploitation levels to guide exploration towards reward-relevant objects during periods of uncertainty. Short SummaryThe anterior cingulate cortex (ACC) and the striatum are core nodes of a network supporting reinforcement learning, but how they augment reinforcement learning with adaptive cognitive strategies has remained unresolved. This study uses electrical microstimulation to show that ACC and the striatum have causal roles adjusting the exploration-exploitation balance and optimize reinforcement learning in multidimensional environments, while not altering attentional filtering or working memory strategies.

neuroscience↗

bioRxiv2024

The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice

Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability and long-term stability. SUMMARYIntroducing a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents, optimized for ease of use, probe recovery, experimental versatility and compatibility with behavior. This protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple re-use of probes, significantly reducing experimental costs. The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7mm; (2) a 3D-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 hours), probe safety and animal welfare. The resulting implants had minimal impact on animals behavioral repertoire, were easily applicable in freely moving and head-fixed contexts and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of data collection post-implant. Infections and other surgery complications were extremely rare. As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.

neuroscience↗

bioRxiv2021

Does the brain care about averages? A simple test.

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: O_LIReliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions. C_LIO_LIBehavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. C_LI We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of data set 1, both assumptions were largely fulfilled. Moreover, better-matched single-trial responses predicted correct behaviour. In contrast, the less restrictive paradigm of data set 2 met neither assumption, with the match between single-trial and average responses being neither reliable nor predictive of behaviour. Simulations confirmed these results. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

neuroscience↗

bioRxiv2021

Geodesic-based distance reveals non-linear topological features in neural activity from mouse visual cortex

An increasingly popular approach to the analysis of neural data is to treat activity patterns as being constrained to and sampled from a manifold, which can be characterized by its topology. The persistent homology method identifies the type and number of holes in the manifold thereby yielding functional information about the coding and dynamic properties of the underlying neural network. In this work we give examples of highly non-linear manifolds in which the persistent homology algorithm fails when it uses the Euclidean distance which does not always yield a good approximation of the true distance distribution of a point cloud sampled from a manifold. To deal with this issue we propose a simple strategy for the estimation of the geodesic distance which is a better approximation of the true distance distribution and can be used to successfully identify highly non-linear features with persistent homology. To document the utility of our method we model a circular manifold, based on orthogonal sinusoidal basis functions and compare how the chosen metric determines the performance of the persistent homology algorithm. Furthermore we discuss the robustness of our method across different manifold properties and point out strategies for interpreting its results as well as some possible pitfalls of its application. Finally we apply this analysis to neural data coming from the Visual Coding - Neuropixels dataset recorded in mouse visual cortex after stimulation with drifting gratings at the Allen Institute. We find that different manifolds with a non-trivial topology can be seen across regions and stimulus properties. Finally, we discuss what these manifolds say about visual computation and how they depend on stimulus parameters.

neuroscience↗

bioRxiv2020

Learning at variable attentional load requires cooperation between working memory, meta-learning and attention-augmented reinforcement learning

Flexible learning of changing reward contingencies can be realized with different strategies. A fast learning strategy involves using working memory of recently rewarded objects to guide choices. A slower learning strategy uses prediction errors to gradually update value expectations to improve choices. How the fast and slow strategies work together in scenarios with real-world stimulus complexity is not well known. Here, we disentangle their relative contributions in rhesus monkeys while they learned the relevance of object features at variable attentional load. We found that learning behavior across six subjects is consistently best predicted with a model combining (i) fast working memory (ii) slower reinforcement learning from differently weighted positive and negative prediction errors, as well as (iii) selective suppression of non-chosen feature values and (iv) a meta-learning mechanism adjusting exploration rates based on a memory trace of recent errors. These mechanisms cooperate differently at low and high attentional loads. While working memory was essential for efficient learning at lower attentional loads, enhanced weighting of negative prediction errors and meta-learning were essential for efficient learning at higher attentional loads. Together, these findings pinpoint a canonical set of learning mechanisms and demonstrate how they cooperate when subjects flexibly adjust to environments with variable real-world attentional demands. Significance statementLearning which visual features are relevant for achieving our goals is challenging in real-world scenarios with multiple distracting features and feature dimensions. It is known that in such scenarios learning benefits significantly from attentional prioritization. Here we show that beyond attention, flexible learning uses a working memory system, a separate learning gain for avoiding negative outcomes, and a meta-learning process that adaptively increases exploration rates whenever errors accumulate. These subcomponent processes of cognitive flexibility depend on distinct learning signals that operate at varying timescales, including the most recent reward outcome (for working memory), memories of recent outcomes (for adjusting exploration), and reward prediction errors (for attention augmented reinforcement learning). These results illustrate the specific mechanisms that cooperate during cognitive flexibility.

animal behavior and cognition↗

bioRxiv2020

Interneuron Specific Gamma Synchronization Encodes Uncertain Cues and Prediction Errors in Lateral Prefrontal and Anterior Cingulate Cortex

Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35-45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and in anterior cingulate cortex (ACC). In LPFC this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically-characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.

neuroscience↗

bioRxiv2019

Adaptive Spike-Artifact Removal from Local Field Potentials Uncovers Prominent Beta and Gamma Band Neuronal Synchronization

BackgroundMany neurons synchronize their action potentials to the phase of local field potential (LFP) fluctuations in one or more frequency bands. Analyzing this spike-to-LFP synchronization is challenging, however, when neural spikes and LFP are generated in the same local circuit, because the spikes action potential waveform leak into the LFP and distort phase synchrony estimates. Existing approaches to address this spike bleed-through artifact relied on removing the average action potential waveforms of neurons, but this leaves artifacts in the LFP and distorts synchrony estimates.\n\nNew MethodWe describe a spike-removal method that surpasses these limitations by decomposing individual action potentials into their frequency components before their removal from the LFP. The adaptively estimated frequency components allow for variable spread, strength and temporal variation of the spike artifact.\n\nResultsThis adaptive approach effectively removes spike bleed-through artifacts in synthetic data with known ground truth, and in single neuron and LFP recordings in nonhuman primate striatum. For a large population of neurons with both narrow and broad action potential waveforms, the use of adaptive artifact removal uncovered 20-35 Hz beta and 35-45 Hz gamma band spike-LFP synchronization that would have remained contaminated otherwise.\n\nComparison with Existing MethodsWe demonstrate that adaptive spike-artifact removal cleans LFP data that remained contaminated when applying existing Bayesian and non-Bayesian methods of average spike-artifact removal.\n\nConclusionsApplying adaptive spike-removal from field potentials allows to estimate the phase at which neurons synchronize and the consistency of their phase-locked firing for both beta and low gamma frequencies. These metrics may prove essential to understand cell-to-circuit neuronal interactions in multiple brain systems.

neuroscience↗

bioRxiv2019

Prediction of a cell-type specific mouse mesoconnectome using gene expression data

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-type specificity would be a major step forward. We analysed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns and analyzed the biological context of the most predictive groups of genes. To achieve our goal, we used publicly available volumetric gene expression and connectivity data and pre-processed it for prediction by averaging across brain regions, imputing missing values and rescaling. Afterwards, we predicted the strength of axonal projections and their binary form using expression patterns of individual genes and co-expression patterns of spatial gene modules.\n\nFor predicting projection strength, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using the dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function.\n\nTaken together, we have demonstrated a prediction pipeline that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

neuroscience↗

bioRxiv2018

Why not record from every channel with a CMOS scanning probe?

It is an uninformative truism to state that the brain operates at multiple spatial and temporal scales, each with each own set of emergent phenomena. More worthy of attention is the point that our current understanding of it cannot clearly indicate which of these phenomenological scales are the significant contributors to the brains function and primary output (i.e. behaviour). Apart from the sheer complexity of the problem, a major contributing factor to this state of affairs is the lack of instrumentation that can simultaneously address these multiple scales without causing function altering damages to the underlying tissue. One important facet of this problem is that standard neural recording devices normally require one output connection per electrode. This limits the number of electrodes that can fit along the thin shafts of implantable probes generating a limiting balance between density and spread. Sharing a single output connection between multiple electrodes relaxes this constraint and permits designs of ultra-high density probes. Here we report the design and in-vivo validation of such a device, a complementary metal-oxide-semiconductor (CMOS) scanning probe with 1344 electrodes; the outcome of the European research project NeuroSeeker. We show that this design targets both local and global spatial scales by allowing the simultaneous recording of more than 1000 neurons spanning 7 functional regions with a single shaft. The neurons show similar recording longevity and signal to noise ratio to passive probes of comparable size and no adverse effects in awake or anesthetized animals. Addressing the data management of this device we also present novel visualization and monitoring methods. Using the probe with freely moving animals we show how accessing a number of cortical and subcortical brain regions offers a novel perspective on how the brain operates around salient behavioural events. Finally, we compare this probe with lower density, non CMOS designs (which have to adhere to the one electrode per output line rule). We show that an increase in density results in capturing neural firing patterns, undetectable by lower density devices, which correlate to self-similar structures inherent in complex naturalistic behaviour. To help design electrode configurations for future, even higher density, CMOS probes, recordings from many different brain regions were obtained with an ultra-dense passive probe.

neuroscience↗

bioRxiv2018

Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Prediction errors signal unexpected outcomes indicating that expectations need to be adjusted. For adjusting expectations efficiently prediction errors need to be associated with the precise features that gave rise to the unexpected outcome. For many visual tasks this credit assignment proceeds in a multidimensional feature space that makes it ambiguous which object defining features are relevant. Here, we report of a potential solution by showing that neurons in all areas of the medial and lateral fronto-striatal networks encode prediction errors that are specific to separate features of attended multidimensional stimuli, with the most ubiquitous prediction error occurring for the reward relevant features. These feature specific prediction error signals (1) are different from a non-specific prediction error signal, (2) arise earliest in the anterior cingulate cortex and later in lateral prefrontal cortex, caudate and ventral striatum, and (3) contribute to feature-based stimulus selection after learning. These findings provide strong evidence for a widely-distributed feature-based eligibility trace that can be used to update synaptic weights for improved feature-based attention.\n\nHighlightsO_LINeural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network.\nC_LIO_LIFeature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex.\nC_LIO_LIVentral striatum neurons encode feature specific surprise strongest for the goal-relevant feature.\nC_LIO_LINeurons encoding feature-specific prediction errors contribute to attentional selection after learning.\nC_LI

neuroscience↗

bioRxiv2017

Molecules of map plasticity in the somatosensory cortex

Sensory maps are representations of the sensory epithelia in the brain. Despite the intuitive explanatory power behind sensory maps as being neuronal precursors to sensory perception, and sensory cortical plasticity as a neural correlate of perceptual learning, molecular mechanisms that regulate map plasticity are not well understood. Here we perform a meta-analysis of transcriptional and translational changes during altered whisker use to nominate the major molecular correlates of experience-dependent map plasticity in the barrel cortex. We argue that brain plasticity is a systems level response, involving all cell classes, from neuron and glia to non-neuronal cells including endothelia. Using molecular pathway analysis, we further propose a gene regulatory network that could couple activity dependent changes in neurons to adaptive changes in neurovasculature, and finally we show that transcriptional regulations observed in major brain disorders target genes that are modulated by altered sensory experience. Thus understanding the molecular mechanisms of experience-dependent plasticity of sensory maps might help to unravel the cellular events that shape brain plasticity in health and disease.\n\nHighlightsO_LIExperience alters gene transcription in all major cell types of the brain\nC_LIO_LIGene expression profile during brain plasticity is cell-type specific\nC_LIO_LITemporal profile of gene expression is dynamic, regulated by recent experience\nC_LIO_LINeural activity-dependent gene regulation might cause neurovascular reorganization\nC_LIO_LIGenes that are regulated by experience are commonly dysregulated in brain disorders\nC_LI

neuroscience↗