Topic: representational drift

Even in stable environments, sensory responses undergo continuous 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

In many brain regions, the stimulus tuning of neurons is stable on a timescale of hours but not on a timescale of weeks, a phenomenon often called ‘representational drift’. For example, in piriform cortex, which is commonly considered primary olfactory cortex, the cells responsive to a given odor are completely uncorrelated with those activated by the same odor two weeks later (Schoonover et al., 2021). This would seem to imply that piriform cortex, like other brain regions whose activity appears to drift, is useless for the retrieval of associative memories learned several weeks prior. Howeve

Olfaction facilitates a large variety of animal behaviors such as feeding, mating, and communication. In the mammal olfactory system, the olfactory bulb (OB) and piriform cortex (PCx) are responsible for preliminary odor encoding and corresponding memory storage respectively. Recent evidence demonstrated the dynamic nature of OB and PCx odor representations upon frequent repetition of the same odors 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

Associative memories might be represented by groups of strongly interconnected neurons, called neuronal assemblies. Recent experimental findings suggest that such assemblies are not static but composed of different neurons at different times [1]. A theoretical model 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.

The stability of the relationship between neural activity and external variables is a matter of debate for many brain areas [1-5]. Whether 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

Neuronal tuning changes over time, even in the absence of obvious signs of learning or changes in behaviour. 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

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed 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