Biology subjects

Beiran, M.

Publications and source records attributed to Beiran, M..

9 recordsLinked to original sources

bioRxiv2026

Neural circuit mechanisms for multi-tasking

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.

neuroscience↗

bioRxiv2024

Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.

neuroscience↗

bioRxiv2023

Prefrontal Cortex Neural Correlates of the Contraction Bias in Frequency Discrimination

Perception is influenced by sensory stimulation, prior knowledge, and contextual cues, which collectively contribute to the emergence of perceptual biases. However, the precise neural mechanisms underlying these biases remain poorly understood. This study aims to address this gap by analyzing neural recordings from the prefrontal cortex (PFC) of monkeys performing a vibrotactile frequency discrimination task. Our findings provide empirical evidence supporting the hypothesis that perceptual biases can be reflected in the neural activity of the PFC. We found that the state-space trajectories of PFC neuronal activity encoded a warped representation of the first frequency presented during the task. Remarkably, this distorted representation of the frequency aligned with the predictions of its Bayesian estimator. The identification of these neural correlates expands our understanding of the neural basis of perceptual biases and highlights the involvement of the PFC in shaping perceptual experiences. Similar analyses could be employed in other delayed comparison tasks and in various brain regions to explore where and how neural activity reflects perceptual biases during different stages of the trial.

neuroscience↗

bioRxiv2022

Emergent perceptual biases from state-space geometry in spiking recurrent neural networks trained to discriminate time intervals

A stimulus held in working memory is perceived as contracted towards the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a novel normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.

neuroscience↗

bioRxiv2022

Neural dynamics and geometry for transitive inference

Relational cognition -- the ability to infer relationships that generalize to novel combinations of objects -- is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) adopted different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs expressing an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

neuroscience↗

bioRxiv2021

Parametric control of flexible timing through low-dimensional neural manifolds

Biological brains possess an unparalleled ability to adapt behavioral responses to changing stimuli and environments. How neural processes enable this capacity is a fundamental open question. Previous works have identified two candidate mechanisms: a low-dimensional organization of neural activity and a modulation by contextual inputs. We hypothesized that combining the two might facilitate generalization and adaptation in complex tasks. We tested this hypothesis in the framework of flexible timing tasks where dynamics play a key role. Examining trained recurrent neural networks, we found that confining the dynamics to a low-dimensional subspace allowed tonic inputs to parametrically control the overall input-output transform, enabling generalization to novel inputs and adaptation to changing conditions. Reverse-engineering and theoretical analyses demonstrated that this parametric control relies on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds in activity space while preserving their geometry. Comparisons with data from behaving monkeys confirmed the behavioral and neural signatures of this mechanism.

neuroscience↗

bioRxiv2020

Complementary roles of dimensionality and population structure in neural computations

Neural computations are currently investigated using two separate approaches: sorting neurons into functional populations, or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and cell-class structure play fundamentally complementary roles. While various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input-output mappings instead required a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a population structure enabled flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the dynamical landscape of collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, inactivation experiments, and for the implication of different neurons in multi-tasking.

neuroscience↗

bioRxiv2020

Midbrain dopamine firing activity codes reward expectation and motivation in a parametric working memory task

Little is known about how dopamine (DA) neuron firing rates behave in cognitively demanding decision-making tasks. Here we investigated midbrain DA activity in monkeys performing a discrimination task in which the animal had to use working memory (WM) to report which of two sequentially applied vibrotactile stimuli had the higher frequency. We found that perception was altered by an internal bias, likely generated by deterioration of the representation of the first frequency during the WM period. This bias greatly controlled the DA phasic response during the two stimulation periods, confirming that DA reward prediction errors reflected subjective stimulus perception. Contrastingly, tonic dopamine activity during WM was not affected by the bias and did not encode the stored frequency. More interestingly, both WM activity and phasic responses before the second stimulus negatively correlated with reaction times of the animal after the trial start cue and thus represented motivated behavior on a trial-by-trial basis. During WM, this motivation signal underwent a ramp-like increase. At the same time, motivation reduced noise in perception and, by decreasing the effect of the bias, improved performance, especially in difficult trials. Overall, our results show that DA activity was simultaneously involved in reward prediction, motivation and WM. Also, the ramping activity during the WM period suggests a possible DA role in stabilizing sustained cortical activity, hypothetically by increasing the gain communicated to prefrontal neurons in a motivation-dependent way.

neuroscience↗

bioRxiv2018

Internal bias controls dopamine perceptual decision-related responses

Dopamine neurons produce reward-related signals that regulate learning and guide behavior. Prior expectations about forthcoming stimuli and internal biases can alter perception and choices and thus could influence dopamine signaling. We tested this hypothesis studying dopamine neurons recorded in monkeys trained to discriminate between two tactile frequencies separated by a delay period, a task affected by the contraction bias. The bias greatly controlled the animals choices and confidence on their decisions. During decision formation the phasic activity reflected bias-induced modulations and simultaneously coded reward prediction errors. In contrast, the activity during the delay period was not affected by the bias, was not tuned to the value of the stimuli but was temporally modulated, pointing to a role different from that of the phasic activity.

neuroscience↗