Representational Drift

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

SeminarNeuroscienceRecording

Network mechanisms underlying representational drift in area CA1 of hippocampus

Alex Roxin

CRM, Barcelona

Feb 1, 2022

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo plasticity, while spatially tuned inputs are largely stable over time. The heterogeneity in stability across the cell assembly, as well as the decrease in correlation of the population vector of activity over time, are both quantitatively fit by a simple model with Gaussian input statistics. In fact, such input statistics emerge naturally in a network of spiking neurons operating in the fluctuation-driven regime. This correspondence allows one to map the parameters of a large-scale spiking network model of CA1 onto the simple statistical model, and thereby fit the experimental data quantitatively. Importantly, we show that the observed drift is entirely consistent with random, ongoing synaptic turnover. This synaptic turnover is, in turn, consistent with Hebbian plasticity related to continuous learning in a fast memory system.

Representational Drift

representational drift

Representational drift in human visual cortex

Stability of visual processing in passive and active vision

Network mechanisms underlying representational drift in area CA1 of hippocampus

Computational mechanisms of odor perception and representational drift in rodent olfactory systems

Hippocampal representational drift and the impact of Alzheimer’s disease

Sudden tuning curve jumps in cortical representational drift facilitate stable downstream population readouts

Synaptic fluctuation induces representational drift while preserving discriminability

Differential effects of time and experience on hippocampal representational drift

Disentangling Fast Representational Drift in Mouse Visual Cortex

A Model for Representational Drift: Implications for the Olfactory System

A Model for Representational Drift: Implications for the Olfactory System

Neural network size balances representational drift and flexibility during Bayesian sampling

Neural network size balances representational drift and flexibility during Bayesian sampling

Experience, Not Time, Determines Representational Drift in the Hippocampus

Representational Drift Across Short Timescales in the Mouse Visual Cortex

Representational drift from a population view of memory consolidation

Representational drift leads to sparse activity solutions that are robust to noise and learning

Representational Drift as a Result of Implicit Regularization

The recurrency level is a key determinant of representational drift

Representational drift in primary vibrissal somatosensory cortex is receptive field dependent

Representational Drift: Transitioning from a Learning-Conducive to Robust Regime

Abrupt transitions interrupt slow, ongoing representational drift in experiment and model

Exercise accelerates place cell representational drift

Mechanisms controlling representational drift in mouse visual cortex

Neural dynamics and representational drift of inhibitory neurons in mouse auditory cortex

Rate of representational drift correlates with information theoretic measures of neural and behavioural coupling

Representational drift from a population view of memory consolidation

Representational drift without synaptic plasticity