Biology subjects

Renart, A.

Publications and source records attributed to Renart, A..

9 recordsLinked to original sources

bioRxiv2024

Optimal control of spiking neural networks

Control theory provides a natural language to describe multi-areal interactions and flexible cognitive tasks such as covert attention or brain-machine interface (BMI) experiments, which require finding adequate inputs to a local circuit in order to steer its dynamics in a context-dependent manner. In optimal control, the target dynamics should maximize a notion of long-term value along trajectories, possibly subject to control costs. Because this problem is, in general, not tractable, current approaches to the control of networks mostly consider simplified settings (e.g., variations of the Linear-Quadratic Regulator). Here, we present a mathematical framework for optimal control of recurrent networks of stochastic spiking neurons with low-rank connectivity. An essential ingredient is a control-cost that penalizes deviations from the default dynamics of the network (specified by its recurrent connections), which motivates the controller to use the default dynamics as much as possible. We derive a Bellman Equation that specifies a Value function over the low-dimensional network state (LDS), and a corresponding optimal control input. The optimal control law takes the form of a feedback controller that provides external excitatory (inhibitory) synaptic input to neurons in the recurrent network if their spiking activity tends to move the LDS towards regions of higher (lower) Value. We use our theory to study the problem of steering the state of the network towards particular terminal regions which can lie either in or out of regions in the LDS with slow dynamics, in analogy to standard BMI experiments. Our results provide the foundation of a novel approach with broad applicability that unifies bottom-up and top-down perspectives on neural computation.

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bioRxiv2024

Facial expressions in mice reveal latent cognitive variables and their neural correlates

Brain activity controls adaptive behavior but also drives unintentional incidental movements. Such movements could thus potentially be used to read out internal cognitive variables also neurally computed. Establishing this, however, would require ruling out that incidental movements reflect cognition only because they are coupled with task-related responses through the biomechanics of the body. We addressed this issue in a foraging task for mice where multiple decision variables are simultaneously encoded even if, at any given time, only one of them is used. We found that characteristic features of the face simultaneously encode not only the currently used decision variables, but also independent and unexpressed ones, and we show that these features partially originate from neural activity in the secondary motor cortex. Our results suggest that the face reflects ongoing computations above and beyond those related to task demands, demonstrating the ability of noninvasive monitoring to expose otherwise latent cognitive states.

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bioRxiv2022

Control Limited Perceptual Decision Making

Periods of disengagement are generally observed during perceptual decision-making tasks, but a normative understanding of engagement is lacking. Here, we develop a theory that frames disengagement as a problem in cognitive control. Good performance through task engagement requires control, but control is costly, and this establishes a performance-control tradeoff. We derive decision policies that optimize this tradeoff as a function of the capacity of an agent for cognitive control. When their control ability is sufficiently low, agents lapse. For intermediate control limitations, a new decision-making regime appears where agents dont lapse, but their behavior is nevertheless shaped by control. We identify hidden signatures of control-limited behavior at the level of accuracy, reaction time and decision confidence which are often observed experimentally, but had not been normatively explained. Our findings provide a path to the study of normative decision strategies in real biological agents.

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bioRxiv2021

Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions

Sensory responses of cortical neurons are more discriminable when evoked on a base-line of desynchronized spontaneous activity, but cortical desynchronization has not generally been associated with more accurate perceptual decisions. Here we show that mice perform more accurate auditory judgements when activity in the auditory cortex is elevated and desynchronized before stimulus onset, but only if the previous trial was an error, and that this relationship is occluded if previous outcome is ignored. We confirmed that the outcome-dependent effect of brain state on performance is neither due to idiosyncratic associations between the slow components of either signal, nor to the existence of specific cortical states evident only after errors. Instead, errors appear to gate the effect of cortical state fluctuations on discrimination accuracy. Neither facial movements nor pupil size during the baseline were associated with accuracy, but they were predictive of measures of responsivity, such as the probability of not responding to the stimulus or of responding prematurely. These results suggest that the functional role of cortical state on behavior is dynamic and constantly regulated by performance monitoring systems.

neuroscience↗

bioRxiv2021

Reservoir of decision strategies in the mouse brain

In any given situation, the environment can be parsed in different ways to define useful decision variables (DVs) for any task, but the way in which this manifold of potential decision strategies is processed to shape behavioral policies is not known. We recorded neural ensembles in the frontal cortex of mice performing a foraging task admitting multiple DVs. Methods developed to uncover the currently employed DV revealed the use of multiple strategies and latent changes in strategy within sessions. Optogenetic manipulations showed that the secondary motor cortex (M2) is needed for mice to use the different DVs in the task. Surprisingly, we found that, regardless of the DV best explaining the behavior of each mouse, M2 activity reflected a full basis set of computations spanning a repertoire of DVs extending beyond those useful for the present task. This form of multiplexing may confer considerable advantages for learning and adaptive behavior.

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bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

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bioRxiv2020

Phasic activation of dorsal raphe serotonergic neurons increases pupil-linked arousal

Variations in pupil size under constant luminance are closely coupled to changes in arousal state [1-5]. It is assumed that such fluctuations are primarily controlled by the noradrenergic system [6-9]. Phasic activity of noradrenergic axons precedes pupil dilations associated with rapid changes in arousal [7,9], and is believed to be driven by unexpected uncertainty [1,10-16]. However, the role of other modulatory pathways in the control of pupil-linked arousal has not been as thoroughly investigated, but evidence suggests that noradrenaline may not be alone [7,17,18]. Administration of serotonergic drugs seems to affect pupil size [19-23], but these effects have not been investigated in detail. Here, we show that transient serotonin (5-HT) activation, like noradrenaline, causes pupil-size changes. We used phasic optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DRN) in head-fixed mice locomoting in a foraging task. 5-HT-driven modulations of pupil size were maintained throughout the photostimulation period and sustained for several seconds after the end of the stimulation. The activation of 5-HT neurons increased pupil size additively with locomotor speed, suggesting that 5-HT transients affect pupil-linked arousal independently from locomotor states. We found that the effect of 5-HT on pupil size depended on the level of environmental uncertainty, consistent with the idea that 5-HT may report a salience or surprise signal [24]. Together, these results challenge the classic view of the neuromodulatory control of pupil-linked arousal, revealing a tight relationship between the activation of 5-HT neurons and changes in pupil size.

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bioRxiv2018

State-dependent geometry of population activity in rat auditory cortex

The accuracy of the sensory neural code depends on the relationship between variability in population activity caused by the stimuli versus variability caused by noise. Whereas a large body of work has addressed this issue at a theoretical level, there have been few systematic empirical investigations using large-scale simultaneous recordings. We studied the geometry of the population code in the rat auditory cortex using sound stimuli varying across two orthogonal dimensions: the difference in sound level between the two ears, and the average level across the ears. We quantified the structure of the code across recordings spanning a wide range of brain states along the activation/inactivation continuum. We found that the nature of the code, both in terms of the stimulus and the noise subspaces, depends strongly on the degree of cortical activation. During the inactive (or synchronized) state, the code is characterized by a prevalence of contra-lateral tuning, a preference for loud sounds, gain-modulated tuning curves and coherent global noise fluctuations overlapping with the signal subspace. In contrast, in the most active (or desynchronized) recordings, there is an approximately symmetric preference between contra-, ipsi-, quiet and loud sounds, no systematic changes in the gain of lateralization tuning curves by sound intensity, and noise fluctuations span dimensions orthogonal to those evoked by the stimuli. Logistic regression could predict the sound lateralization with accuracy similar to the behaving rats, but only during cortical activation. Furthermore, also exclusively during the active state, the lateralization of sounds of different overall level could be decoded with the same accuracy, even by the same decoder, due to an orthogonal representation of level-differences and overall level across the population. Decoding accuracy from surrogate datasets with independent noise across neurons still displays strong state-dependence, suggesting that tuning curve reorganization plays a critical role in the overall effect of brain state on the population code. Our results provide an empirical foundation for the geometry and state dependence of cortical population codes.

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bioRxiv2018

Weber’s law is the result of exact temporal accumulation of evidence

Webers law states that the discriminability between two stimulus intensities depends only on their ratio. Despite its status as the cornerstone of psychophysics, the mecha-nisms underlying Webers law are still debated, as no principled way exists to choose between its many proposed alternative explanations. We studied this problem training rats to discriminate the lateralization of sounds of different overall level. We found that the rats discrimination accuracy in this task is level-invariant, consistent with Webers law. Surprisingly, the shape of the reaction time distributions is also level-invariant, implying that the only behavioral effect of changes in the overall level of the sounds is a uniform scaling of time. Furthermore, we demonstrate that Webers law breaks down if the stimulus duration is capped at values shorter than the typical reaction time. Together, these facts suggest that Webers law is associated to a process of bounded evidence accumulation. Consistent with this hypothesis, we show that, among a broad class of sequential sampling models, the only robust mechanism consistent with reaction time scale-invariance is based on perfect accumulation of evidence up to a constant bound, Poisson-like statistics, and a power-law encoding of stimulus intensity. Fits of a minimal diffusion model with these characteristics describe the rats performance and reaction time distributions with virtually no error. Various manipulations of motivation were unable to alter the rats psychometric function, demonstrating the stability of the just-noticeable-difference and suggesting that, at least under some conditions, the bound for evidence accumulation can set a hard limit on discrimination accuracy. Our results establish the mechanistic foundation of the process of intensity discrimination and clarify the factors that limit the precision of sensory systems.

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