Representational drift

…Hippocampal place cells exhibit representational drift (Ziv et al., 2013), but this has only been characterized with somatic recordings. While dendritic activity is implicated in the formation of stable place fields and shaping hippocampal activity, in vivo studies of hippocampal dendritic activity have largely been confined to basal dendrites (Sheffield and Dombeck, 2015) or small cross-sections of apical den

Mechanisms of representational drift are still under investigation. Under one set of hypotheses, drift is a result of noisy continual learning in the brain. Previous computational studies have demonstrated that drift could occur under different types of noise such as synaptic noise, or sample stochasticity. However, exact contribution of various noises remains elusive. For example, it’s unclear what’s the contribution of stimuli that are relevant for the task, and the ones that are continually present in the environment but the agent learns to ignore in a context. In this study, using theory a

Our ability to form and retrieve memories relies on a network of connected brain regions. In particular, neuronal ensembles across regions form distributed memory engrams that have structural and functional connectivity. However, the computational principles underlying the distributed nature of memory remain unclear. Here, we show that distributed engrams enable memory generalization and discrimination in parallel across brain regions. Orthogonal and complementary, generalization and discrimination are computations that must be balanced for adaptive, memory-guided behavior. For instance, while

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. The hippocampus, a key brain region for these maps, exhibits neural population activity that often evolves along nonlinear, low-dimensional manifolds. While cognitive maps have been characterized at the level of cell tunings to spatial location and decorrelations over time, a precise, unsupervised, and quantitative model of how this map forms and morphs at the population level remains elusive due to the large dimensionality and nonlinearity of neural activity. Current dimensionality reduction t

Sex-steroid hormones powerfully influence internal states, mood, and social drives. In many species, including mice, females exhibit increased sexual receptivity during the peri-ovulatory phase following an estrogen surge. However, we lack understanding of how these hormones alter neural computations to regulate social behaviors, particularly their effects on neural dynamics in key regions for top-down control of social choice, such as the medial prefrontal cortex (mPFC). One potential, but unexplored role of the estrogen surge is to enable persistent neural states to facilitate costly behavio

…and learning, but recent findings on representational drift challenge our understanding of how individual neurons contribute to stable brain computations over time. While representational drift has been observed across multiple brain regions, its relationship to structural connectivity and homeostatic statistics remains unclear [1]. To address this, we explored how drift interacts with structural recurrency and noise in a recurrent neural network (RNN) model. By simulating synaptic-level noise through random connectivity manip

…behavior is stable, a process dubbed ‘representational drift’. This poses a critical challenge for the brain – how can animals maintain stable perception and

…environments, a phenomenon called representational drift. Theoretical studies suggest that multiple neural representations can correspond to a memory, with post-learning exploration of these representation solutions driving drift. However, the functional implications of drift-explored representations versus those initially learned remain poorly understood. Here we show that drift uncovers robust, sparsely engaged r

…shifts over days, a phenomenon termed representational drift [Driscoll et al., 2017, Rule et al., 2020]. Thus, it becomes crucial to account for such non-stationarities when investigating the roles of circuits in the brain. While linear dynamical system (LDS) models are commonly used to characterize the dynamics of neural population activity, they assume a stable mapping from the latent states to neural activity. This limits their ability to capture non-stationary processes---such as representational drif

…reveals the presence of a systematic drift, manifested as rotation of population vectors away from their initial representations. We explain these phenomena using a recurrent inhibition feedback model with unsupervised Hebbian learning. Our model predicts a power- law decay in mean population activity over days of odor expo

…reformatting, a phenomenon known as representational drift. Using chronic calcium imaging in mouse auditory cortex, we show that during this representational drift signal correlations predict future noise correlations, suggesting that stimulus-driven co-activation strengthens effective connectivity via Hebbian-like plasticity. Linear network models reveal that these temporal dependencies between signal and noise correlations emerge only when Hebbian learning balances stochastic synaptic changes, preventing functional degradation

…over days, and this phenomenon, termed representational drift, should be the consequence of highly plastic nature of biologically constrained synapses in the olfactory system. In this work, we buil

…reproducing these findings is given by drifting assemblies [2]: In a gradual process, assemblies exchange individual neurons between one another due to ever-present synaptic plasticity. So far, only single-layer networks of drifting assemblies have been considered. Biological neuronal assemblies are, however, thought to be distribu

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon that is hypothesized to be critical to understanding synaptic plasticity and neural network stability [1,2]. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. This model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then use an artificial neural network structure where synaptic strengths fluctuate accor

Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently developed method called SpikeShip based on optimal transport theory, which captures information from all the relative spike-timing relations among neurons. We compared spike-rate and spike-timing codes in neural ensembles from six visual areas during natural video presentations.

…neural representations are stable or "drifting" has significant implications for how downstream neural populations process these representations and incorporate them into computations driving perception, behavior, and memory. Most studies on the stability of neural coding for movements have focused on simple, stereotyped, and overtrained behaviors, finding that neural representations are stable [6-8]. However, it is unclear if this stability results from t

…This representational drift causes fixed readouts from a neural population to gradually deteriorate, suggesting that downstream populations adapt to these tuning changes. Many statistical distributions of tuning curve changes can produce comparable population-wide drift. We argue that heavy-tailed drift statistics, in which populations exhibit sparse and sudden changes in neuronal tuning, are relatively easy to adapt to. We present preliminary evidence that neural populations favour suc

…that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4

Synaptic and representational drift describe the dynamic changes in neuronal connections and activity over time. However, the relationship between these two processes remains elusive. Here, we integrated representational dynamics and synaptic drift into a firing rate model to explore the influence of synaptic drift on representational drift.In mouse auditory cortex, neuronal populations respond to stimuli in a clustered manner (Aschauer et al., 2022). Our model, comprising inhibitory and excitatory neurons, reproduces these response patterns, especially under inhibition-dominated conditions. W

…remain the same – a phenomenon termed representational drift. Here we test whether neural circuit remodeling (defined as any change in structural connectivity) contributes to representational drift. To do this, we tracked how hippocampal CA1 spatial representations of a familiar environment change with t

…This phenomenon called “representational drift” has also been observed in other sensory and higher cortical areas and has profound implications for coding strategies in the brain.It is unknown which tuning feature changes of individual neurons underlie representational drift in the primary visual cortex (V1), and what regulates their drift. Using repeated 2-photon calcium imaging in awake mice, we show a slow, time-dependent drift of the preferred orientation of V1 neurons over

…change over time – a phenomenon termed representational drift. Yet, how inhibitory neurons contribute to representational drift remains largely unknown.Here, we examined the collective statistics of inhibitory and excitatory neurons in layers 2/3 o

…and weeks in a phenomenon known as representational drift. The rate of drift varies across cells in a population, and it remains unclear what underlies this heterogeneity. In this study, we explored whether drift is influenced by a neuron's propensity to synchronise or desynchronise its activity with other neurons in the population. Using data from parietal cortex (Driscoll et al. 2017) and visual cortex (Marks & Goard 2021), we evaluated pairwise encodings of task information to derive measure

…that reproduces many features of this “representational drift”. We develop an analytically tractable model for the dynamics of memory engrams that is structurally similar to a recurren