Biology subjects

Pals, M.

Publications and source records attributed to Pals, M..

5 recordsLinked to original sources

bioRxiv2026

Attractor dynamics underlie sequence working memory in data-constrained recurrent neural networks

Sequences of stimuli can be maintained in working memory by encoding each item, together with its position, as a distinct pattern of neural population activity. Competing hypotheses about the underlying dynamics have been proposed: patterns of activity might be supported either by stable attractor states that persist over time, or by transient sequential dynamics. To investigate the dynamics underlying sequence memory, we developed a framework for fitting low-rank recurrent neural networks (RNNs) to multi-session recordings of spiking activity, and applied it to 2,488 units recorded across 30 sessions in two macaques. Our data-constrained RNNs generated population spiking activity which closely resembled neural data and recovered single-unit tuning properties across sessions. Consistent with prior work, fitted networks represented items presented at different sequence positions in separate low-dimensional subspaces in the neural activity, alongside a dominant temporal component. The RNNs enabled direct investigation of the underlying dynamics: By isolating the temporal and stimulus subspaces, we found a manifold of fixed points emerged during the memory retention period. Targeted perturbation analyses confirmed that activity within this manifold is consistent with attractor-like dynamics, and predict that brief optogenetic perturbations delivered along this attractive manifold can selectively induce persistent shifts in neural activity and decoded behavior. Together, our results indicate that sequence working memory is organized by position-specific coding subspaces, within which attractor states maintain persistent representations, while activity evolves along time-varying dimensions.

neuroscience↗

bioRxiv2024

Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience--JO_SCPLOWAXLEYC_SCPLOW--which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JO_SCPLOWAXLEYC_SCPLOW opens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that JO_SCPLOWAXLEYC_SCPLOW can learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that JO_SCPLOWAXLEYC_SCPLOW makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that JO_SCPLOWAXLEYC_SCPLOW dramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.

neuroscience↗

bioRxiv2023

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or frame of reference. Information can be encoded by the timing of neural activity relative to the phase of such oscillations. In line with this hypothesis, there have been multiple empirical observations of such phase codes in the brain. Here we ask: What kind of neural dynamics support phase coding of information with neural oscillations? We tackled this question by analyzing recurrent neural networks (RNNs) that were trained on a working memory task. The networks were given access to an external reference oscillation and tasked to produce an oscillation, such that the phase difference between the reference and output oscillation maintains the identity of transient stimuli. We found that networks converged to stable oscillatory dynamics. Reverse engineering these networks revealed that each phase-coded memory corresponds to a separate limit cycle attractor. We characterized how the stability of the attractor dynamics depends on both reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the connectivity structures that underlie these dynamics, we showed that trained networks can be described as two phase-coupled oscillators. Using this insight, we condensed our trained networks to a reduced model consisting of two functional modules: One that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a mechanism by which neural networks can harness reference oscillations for working memory. Specifically, we propose that a phase-coding network generates autonomous oscillations which it couples to an external reference oscillation in a multi-stable fashion. Author summaryMany of our actions are rhythmic--walking, breathing, digesting and more. It is not surprising that neural activity can have a strong oscillatory component. Indeed, such brain waves are common, and can even be measured using EEG from the scalp. Perhaps less obvious is the presence of such oscillations during non-rhythmic behavior--such as memory maintenance and other cognitive functions. Reports of these cognitive oscillations have accumulated over the years, and various theories were raised regarding their origin and utilization. In particular, oscillations have been proposed to serve as a clock signal that can be used for temporal-, or phase-coding of information in working memory. Here, we studied the dynamical systems underlying this kind of coding, by using trained artificial neural networks as hypothesis generators. We trained recurrent neural networks to perform a working memory task, while giving them access to a reference oscillation. We were then able to reverse engineer the learned dynamics of the networks. Our analysis revealed that phase-coded memories correspond to stable attractors in the dynamical landscape of the model. These attractors arose from the coupling of the external reference oscillation with oscillations generated internally by the network.

neuroscience↗

bioRxiv2022

Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks

A prominent theory proposes that the temporal order of a sequence of items held in memory is reflected in ordered firing of neurons at different phases of theta oscillations 1. We probe this theory by directly measuring single neuron activity (1420 neurons) and local field potentials (LFP, 921 channels) in the medial temporal lobe of 16 epilepsy patients performing a working memory task for temporal order. We observe theta oscillations and preferential firing of single neurons at theta phase during memory maintenance. We find that - depending on memory performance - phase of firing is related to item position within a sequence. However, in contrast to the theory, phase order did not match item order. To investigate underlying mechanisms, we subsequently trained recurrent neural networks (RNNs) to perform an analogous task. Similar to recorded neural activity, we show that RNNs generate theta oscillations during memory maintenance. Importantly, model neurons exhibit theta phase-dependent firing related to item position, where phase of firing again did not match item order. Instead, we observed a mechanistic link between phase order, stimulus timing and oscillation frequency - a relationship we subsequently confirmed in our neural recordings. Taken together, in both biological and artificial neural networks we provide validating evidence for the role of phase-of-firing in memory processing while at the same time challenging a long-held theory about the functional role of spiking and oscillations in sequence memory.

neuroscience↗

bioRxiv2019

A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity

In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information in WM is maintained through persistent recurrent activity, recent studies have shown that information can be maintained without persistent firing; instead, information can be stored in activity-silent states. A candidate mechanism underlying this type of storage is short-term synaptic plasticity (STSP), by which the strength of connections between neurons rapidly changes to encode new information. To demonstrate that STSP can lead to functional behavior, we integrated STSP by means of calcium-mediated synaptic facilitation in a large-scale spiking-neuron model and added a decision mechanism. The model was used to simulate a recent study that measured behavior and EEG activity of participants in three delayed-response tasks. In these tasks, one or two visual gratings had to be maintained in WM, and compared to subsequent probes. The original study demonstrated that WM contents and its priority status could be decoded from neural activity elicited by a task-irrelevant stimulus displayed during the activity-silent maintenance period. In support of our model, we show that it can perform these tasks, and that both its behavior as well as its neural representations are in agreement with the human data. We conclude that information in WM can be effectively maintained in activity-silent states by means of calcium-mediated STSP. Author SummaryMentally maintaining information for short periods of time in working memory is crucial for human adaptive behavior. It was recently shown that the human brain does not only store information through neural firing - as was widely believed - but also maintains information in activity-silent states. Here, we present a detailed neural model of how this could happen in our brain through short-term synaptic plasticity: rapidly adapting the connection strengths between neurons in response to incoming information. By reactivating the adapted network, the stored information can be read out later. We show that our model can perform three working memory tasks as accurately as human participants can, while using similar mental representations. We conclude that our model is a plausible and effective neural implementation of human working memory.

neuroscience↗