Stable task information from an unstable neural population

Abstract

Over days and weeks, neural activity representing an animal's position and movement in sensorimotor cortex has been found to continually reconfigure or 'drift' during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

“…Instead, the internal mappings between neurons persist through time. From the reference frame of the environment, the neural code for some variable (e.g., spatial location) may be drifting, but from the reference frame of the neurons, representations might remain stable due to compensatory adaptation at the network level ( Felipe et al, 2020 ; Gonzalez et al, 2019 ; Kinsky et al, 2018 ; Rule et al, 2019 ; Rule et al, 2020 ). Such an outcome may arise from slow, coherent representational shifts such that different brain regions collectively shift in a coordinated manner.…”

“…Most surprisingly, motor patterns are unchanged despite drift in neural activity from motor areas ( Liberti et al, 2016 ; Rokni et al, 2007 , but see Katlowitz et al, 2018 ). Perhaps behavioral stability could be attributed to the consistency of the overall population regardless of the activity of individual neurons ( Rule et al, 2020 ). Others have shown that from a population standpoint, the variability of any individual neuron is inconsequential to the fidelity of the overall neural code ( Gallego et al, 2020 ; Gonzalez et al, 2019 ; Rokni et al, 2007 ; Rule et al, 2019 ; Rule et al, 2020 ).…”

“…Perhaps behavioral stability could be attributed to the consistency of the overall population regardless of the activity of individual neurons ( Rule et al, 2020 ). Others have shown that from a population standpoint, the variability of any individual neuron is inconsequential to the fidelity of the overall neural code ( Gallego et al, 2020 ; Gonzalez et al, 2019 ; Rokni et al, 2007 ; Rule et al, 2019 ; Rule et al, 2020 ). A recent study proposed that downstream readers can hypothetically compensate for drift, suggesting that as long as the interpreter of an upstream neural code is capable of re-weighting its inputs, a stable readout can still be achieved ( Felipe et al, 2020 ; Rule et al, 2020 ).…”

“…Others have shown that from a population standpoint, the variability of any individual neuron is inconsequential to the fidelity of the overall neural code ( Gallego et al, 2020 ; Gonzalez et al, 2019 ; Rokni et al, 2007 ; Rule et al, 2019 ; Rule et al, 2020 ). A recent study proposed that downstream readers can hypothetically compensate for drift, suggesting that as long as the interpreter of an upstream neural code is capable of re-weighting its inputs, a stable readout can still be achieved ( Felipe et al, 2020 ; Rule et al, 2020 ). Although the neurobiological principles governing this re-weighting have yet to be determined, they may hinge upon neuromodulatory feedback signals such as dopamine or acetylcholine ( Duszkiewicz et al, 2019 ; Hasselmo, 2006 ).…”

Representational drift in the mouse visual cortex

“…Recent advances in electrophysiology and optical imaging techniques enable to study in awake behaving animals the persistence over time of neuronal coding properties, such as the tuning of neurons to specific stimuli (Rokni et al ., 2007; Tolias et al ., 2007; Bondar et al ., 2009; Andermann, Kerlin and Reid, 2010; Huber et al ., 2012; Ziv et al ., 2013; Peron et al ., 2015; Poort et al ., 2015; Okun et al ., 2016; Dhawale et al ., 2017; Jun et al ., 2017). Some of these studies exposed a substantial degree of variability in neuronal responses to the same stimuli over timescales spanning minutes to weeks, prompting neuroscientists to question the naïve assumption that stable neuronal codes are essential for stable brain functionality (Tolhurst, Movshon and Dean, 1983; Arieli et al ., 1996; Rokni et al ., 2007; Faisal, Selen and Wolpert, 2008; Minerbi et al ., 2009; Cohen and Maunsell, 2010; Huber et al ., 2012; Ziv et al ., 2013; Lütcke, Margolis and Helmchen, 2013; Montijn, Goltstein and Pennartz, 2015; Rubin et al ., 2015; Schölvinck et al ., 2015; Rose et al ., 2016; Chambers and Rumpel, 2017; Clopath et al ., 2017; Dhawale et al ., 2017; Driscoll et al ., 2017; Engel and Steinmetz, 2019; Rule et al ., 2020; Sheintuch et al ., 2020).…”