Biology subjects

Compte, A.

Publications and source records attributed to Compte, A..

14 recordsLinked to original sources

bioRxiv2025

Top-down feedback can explain the existence of working memory traces in early visual cortex

The temporary storage of visual memories is known to engage early visual cortex (EVC), where mnemonic content is presumed to be stored in weak or latent states. Two mechanisms have been suggested to underlie this storage: Top-down feedback from anterior cortical sites, and short-term synaptic plasticity (STSP) traces laid down by previous sensory inputs. Here, we tested if these mechanisms can account for two hallmarks of working memory, namely, the flexible selection of information from working memory, and the robustness of working memory against distraction. Simulating firing rates in a neural network model of working memory, we show that STSP cannot easily account for selection of information from within the working memory store, and is not robust to visual distraction. Rather, any input that passes through the visual system is briefly remembered, irrespective of its relevance. In contrast, top-down feedback can establish durable mnemonic signals in EVC that reflect currently task-relevant working memory contents and are robust to distraction. We argue that feedback is a parsimonious explanation for the existence of mnemonic signals in EVC, given that the storage of working memories is distributed across a hierarchy of cortical sites, which are known to have widespread feedback projections to visual cortex.

neuroscience↗

bioRxiv2025

Redundant, weakly connected prefrontal hemispheres balance precision and capacity in spatial working memory

The prefrontal hemispheres must coordinate dynamically to maintain a unified representation of visual space. Recently, two opposing theories using distinct storage strategies have been proposed: A high-capacity specialized architecture, where each hemisphere governs contralateral behavior, and a fail-safe redundant one, where both hemispheres jointly guide behavior across the visual space. Here, we analyzed simultaneous bilateral prefrontal cortex recordings from three macaque monkeys performing a visuo-spatial working memory task. Both hemispheres equally predicted behavioral imprecision, decoding errors were weakly correlated between hemispheres, and serial dependence remained local within hemispheres, suggesting a redundant, weakly coupled organization. Attractor network simulations showed that redundancy improved precision when task demands were below memory capacity, while weak interhemispheric coupling increased capacity in more demanding tasks by allowing hemispheric specialization. These predicted patterns were validated in human and monkey data, reconciling previous findings and revealing a versatile interhemispheric architecture that adapts to varying cognitive demands.

animal behavior and cognition↗

bioRxiv2024

Episodic recruitment of attractor dynamics in frontal cortex reveals distinct mechanisms for forgetting and lack of cognitive control in short-term memory

Short-term memory (STM) is prone to failure, especially during prolonged memory maintenance or under limited cognitive control. Despite predictive mechanistic frameworks based on persistent neural activity and attractor states, a direct assessment of network dynamics during multifactorial STM failure is still missing. We addressed this in a delayed-response task where mice maintained a prospective response during a long variable delay. Mice behavior episodically switched between a task-engaged state described by an attractor model, and a task-disengaged state purely determined by previous choices. During task engagement, the anterolateral motor cortex (ALM) showed delay persistent activity stably encoding correct choices, whereas the encoding reversed during the delay in error trials. In contrast, in task-disengaged phases ALM showed no clear traces of attractor dynamics and instead exhibited enhanced synchrony at [~] 4-5Hz. Thus, ALM switches between distinct error-generating dynamics: in control-capable trials, transitions between memory attractors cause forgetting errors, whereas non-memory errors are caused by the dissociation of ALM during the mnemonic period reflecting the lack of cognitive control.

neuroscience↗

bioRxiv2024

Alpha phase-coding supports feature binding during working memory maintenance

The ability to successfully retain and manipulate information in working memory (WM) requires that objects individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase coding dynamics that emerge from the competition between different memories. SignificanceWe investigate the neural basis of working memory, focusing on how feature binding is accomplished and how binding or swap errors arise. Using magnetoencephalography, we found that stable phase-locking of alpha oscillations supports correct feature binding, while swap errors correlate with reduced alpha phase preservation, localized to specific brain areas. These findings align with a biologically-plausible computational model predicting that temporal synchrony in neuronal firing underpins feature binding. This work advances our understanding of the neural mechanisms of working memory, providing empirical support for theories of time-based binding and demonstrating the utility of biophysically-realistic models in human neuroimaging studies.

neuroscience↗

bioRxiv2024

Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC. Significance statementAcetylcholine has been thought to improve cognitive performance by sharpening neuronal tuning in prefrontal cortex. Recent work has shown that electrical stimulation of the cholinergic forebrain in awake-behaving monkeys induces a reduction in prefrontal neural tuning under stimulation conditions that improve performance. To reconcile these divergent observations, we provide network simulations showing that these derive consistently from specific conditions in prefrontal attractor dynamics: firing rate saturation leads to increased storage precision and reduced neural tuning upon cholinergic activation via an increase in neural excitability, a reduction in neural correlations, and an increase in excitatory transmission. Our study integrates previously reported data into a consistent mechanistic view of how acetylcholine controls spatial working memory via attractor network dynamics in prefrontal cortex.

neuroscience↗

bioRxiv2021

Across-area synchronization supports feature integration in working memory

Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or "binding" between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks - one for color and one for location - simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the networks oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: "color bumps" abruptly changed their phase relationship with "location bumps". This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.

neuroscience↗

bioRxiv2019

Disrupted serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia

We report markedly reduced working memory-related serial dependence with preserved memory accuracy in anti-NMDAR encephalitis and schizophrenia. We argue that NMDAR-related changes in cortical excitation, while quickly destabilizing persistent neural activity, cannot fully account for a reduction of memory-dependent biases. Rather, our modeling results support a disruption of a memory mechanism operating on a longer timescale, such as short-term potentiation.

neuroscience↗

bioRxiv2019

Perceptual spatial suppression and sensitivity for motion are weakened in schizophrenia

Perceptual spatial suppression is a phenomenon in which the perceived strength of a stimulus in some region of the space is reduced when the stimulus is surrounded by other stimuli. For contrast perception, several studies suggest that spatial suppression is reduced in patients with schizophrenia. For motion perception, only one study has been conducted in a cohort of 16 patients, suggesting that spatial suppression is reduced. It is unknown, however, whether this reduction is related to the lower IQ that schizophrenic patients usually show; as there is evidence that spatial suppression for motion increases with IQ in healthy individuals. Here, we sought to determine the spatial suppression for motion in a larger cohort of 33 patients with schizophrenia controlling for IQ. We found a weakened spatial suppression in patients with schizophrenia, consistent with the previous study (g = 0.47, CI = [0.055, 0.88], combining the previous and our study). For comparison, we performed a meta-analysis on spatial suppression for contrast and found a similar effect size. We found that patients had a lower IQ than controls, but this difference did not explain their weaker spatial suppression. Further, we found that spatial suppression of patients, but not controls increased with their IQ and decreased with age in both groups. Finally, as we estimated lapses of attention, we could estimate motion sensitivity and found that it was decreased in patients. We speculate about possible alterations in neurotransmission that might explain the reduced spatial suppression and sensitivity that we found.

neuroscience↗

bioRxiv2019

Interplay between persistent activity and activity-silent dynamics in prefrontal cortex during working memory

Persistent neuronal spiking has long been considered the mechanism underlying working memory, but recent proposals argue for alternative, \"activity-silent\" substrates for memory. Using monkey and human electrophysiology, we show here that attractor dynamics that control neural spiking during mnemonic periods interact with activity-silent mechanisms in PFC. This interaction allows memory reactivation, which enhance serial biases in spatial working memory. Stimulus information was not decodable between trials, but remained present in activity-silent traces inferred from spiking synchrony in PFC. Just prior to the new stimulus, this latent trace was reignited into activity that recapitulated the previous stimulus representation. Importantly, the reactivation strength correlated with the strength of serial biases in both monkeys and humans, as predicted by a computational model integrating activity-based and activity-silent mechanisms. Finally, single-pulse TMS applied to human prefrontal cortex prior to trial start enhanced serial biases, demonstrating the causal role of prefrontal reactivations in determining working memory behavior.

neuroscience↗

bioRxiv2018

Build-up of serial dependence in color working memory

Serial dependence, how recent experiences bias our current estimations, has been described experimentally during delayed-estimation of many different visual features, with subjects tending to make estimates biased towards previous ones. It has been proposed that these attractive biases help perception stabilization in the face of correlated natural scene statistics as an adaptive mechanism, although this remains mostly theoretical. Color, which is strongly correlated in natural scenes, has never been studied with regard to its serial dependencies. Here, we found significant serial dependence in 6 out of 7 datasets with behavioral data of humans (total n=111) performing delayed-estimation of color with uncorrelated sequential stimuli. Consistent with a drifting memory model, serial dependence was stronger when referenced relative to previous report, rather than to previous stimulus. In addition, it built up through the experimental session, suggesting metaplastic mechanisms operating at a slower time scale than previously proposed (e.g. short-term synaptic facilitation). Because, in contrast with natural scenes, stimuli were temporally uncorrelated, this build-up casts doubt on serial dependencies being an ongoing adaptation to the stable statistics of the environment.

neuroscience↗

bioRxiv2018

Active information maintenance in working memory by a sensory cortex

Working memory is a critical function of the brain to maintain and manipulate information over delay periods of seconds. Sensory areas have been implicated in working memory; however, it is debated whether the delay-period activity of sensory regions is actively maintaining information or passively reflecting top-down inputs. We hereby examined the anterior piriform cortex, an olfactory cortex, in head-fixed mice performing a series of olfactory working memory tasks. Information maintenance is necessary in these tasks, especially in a dual-task paradigm in which mice are required to perform another distracting task while actively maintaining information during the delay period. Optogenetic suppression of the piriform cortex activity during the delay period impaired performance in all the tasks.Furthermore, electrophysiological recordings revealed that the delay-period activity of the anterior piriform cortex encoded odor information with or without the distracting task.Thus, this sensory cortex is critical for active information maintenance in working memory.

neuroscience↗

bioRxiv2017

Subgenual anterior cingulate cortex controls sadness-induced modulations of cognitive and emotional network hubs

The regulation of cognitive and emotional processes is critical for proper executive functions and social behavior, but its specific mechanisms remain unknown. Here, we addressed this issue by studying with functional magnetic resonance imaging the changes in network topology that underlie competitive interactions between emotional and cognitive networks in healthy participants. Our behavioral paradigm contrasted periods with high emotional and cognitive demands by including a sadness provocation task followed by a spatial working memory task. We hypothesized that this paradigm would enhance the modularity of emotional and cognitive networks and reveal the hub areas that regulate the flow of information between them. By applying graph analysis methods on functional connectivity between 20 regions of interest in 22 participants we identified two main brain network modules, one cognitive and one emotional, and their hub areas: the left dorsolateral prefrontal cortex (dlPFC) and the left medial frontal pole (mFP). These hub areas did not modulate their mutual functional connectivity following sadness but they did so through an interposed area, the subgenual anterior cingulate cortex (sACC). Our results identify dlPFC and mFP as areas regulating interactions between emotional and cognitive networks, and suggest that their modulation by sadness experience is mediated by sACC.

neuroscience↗

bioRxiv2017

Mean-Field Equations For Neuronal Networks With Arbitrary Degree Distributions

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections which are made randomly and independently with a fixed probability (Erdos-Renyi network) (ER), or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of pre-synaptic and post-synaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

neuroscience↗

bioRxiv2016

UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network

In the idling brain, neuronal circuits often exhibit transitions between periods of sustained firing (UP state) and quiescence (DOWN state). Although these dynamics occur across multiple areas and behavioral conditions, the underlying mechanisms remain unclear. Here we analyze spontaneous population activity from the somatosensory cortex of urethane-anesthetized rats. We find that UP and DOWN periods are variable (i.e. non-rhythmic) and that the population rate shows no significant decay during UP periods. We build a network model of excitatory (E) and inhibitory (I) neurons that exhibits a new bistability between a quiescent state and a balanced state of arbitrarily low rate. Fluctuating inputs trigger state transitions. Adaptation in E cells paradoxically causes a marginal decay of E-rate but a marked decay of I-rate, a signature of balanced bistability that we validate experimentally. Our findings provide evidence of a bistable balanced network that exhibits non-rhythmic state transitions when the brain rests.

neuroscience↗