In this paper, we test one of the hypothesis from the "Causes and consequences of representational drift" review: that neural population codes might support a stable readout, even as single neural tunings reconfigure.
We examined long-term neuroimaging recordings form Driscoll et al. (2017). We found that the population code does, indeed, preserve a stable readout over time. This is because much of the reconfiguration in the neural code is orthogonal to the directions that encode behavior.
Even this readout isn't perfectly stable, however, so some amount of ongoing compensation must occur to allow readouts to adjust to the shifting neural code. This residual reconfiguration is much slower than the apparent day-to-day variability in single neurons, so it could be easily tracked with synaptic plasticity.
Many thanks to all! This really was a team effort: data were collected by Laura Driscoll and colleagues in the Harvey lab, and most of the analysis was started by Adriana Loback. Dhruva Raman was instrumental in sorting out the null model simulations. The paper can be cited as
Rule, M.E., Loback, A.R., Raman, D.V., Driscoll, L.N., Harvey, C.D. and O'Leary, T., 2020. Stable task information from an unstable neural population. Elife, 9, p.e51121.
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| A slowly-varying component of drift disrupts the behavior-coding subspace. (a) The small error increase when training concatenated decoders (Figure 3) suggests that plasticity is needed to maintain good decoding in the long term. We assess the minimum rate for this plasticity by training a separate decoder Md for each day, while minimizing the change in weights across days. The parameter λ controls how strongly we constrain weight changes across days (the inset equation reflects the objective function to be minimized; Methods). (b) Decoders trained on all days (cyan) perform better than chance (red), but worse than single-day decoders (ochre). Black traces illustrate the plasticity-accuracy trade-off for adaptive decoding. Modest weight changes per day are sufficient to match the performance of single-day decoders (Boxes: inner 50% of data, horizontal lines: median, whiskers: 5–95th%). (c) Across days, the mean neural activity associated with a particular phase of the task changes (Δμ). We define an alignment measure ρ (Materials and methods) to assess the extent to which these changes align with behavior-coding directions in the population code (blue) verses directions of noise correlations (ochre). (d) Drift is more aligned with noise (ochre) than it is with behavior-coding directions (blue). Nevertheless, drift overlaps this behavior-coding subspace much more than chance (grey; dashed line: 95% Monte-Carlo sample). Each box reflects the distribution over all maze locations, with all consecutive pairs of sessions combined. |
I've started a new postdoc, working on a collaboration between the O'Leary, Ziv, and Harvey labs, on the Human Frontiers Science Program grant, "Building a theory of shifting representations in the mammalian brain".
To start, I've been working with Adrianna Loback to try to make sense of a puzzling result from Driscoll et al. (2017): the neural code for sensorimotor variables in parietal cortex is unstable, changing dramatically even for habitual tasks in which no learning takes place.
We think the brain might be using a distributed population code. Because there are so many possible ways to read-out a distributed and redundant population code, it could be that there is a stable representation at the population level, despite the apparent instability of single neurons.
I'll be presenting our work to date as a poster at the UK Neural Computation conference in Nottingham, July 1st-3rd.
Recent experiments reveal that neural populations underlying behavior reorganize their tunings over days to weeks, even for routine tasks. How can we reconcile stable behavioral performance with ongoing reconfiguration in the underlying neural populations? We examine drift in the population encoding of learned behaviour in posterior parietal cortex of mice navigating a virtual-reality maze environment. Over five to seven days, we find a subspace of population activity that can partially decode behaviour despite shifts in single-neuron tunings. Additionally, directions of trial-to-trial variability on a single day predict the direction of drift observed on the following day. We conclude that day-to-day drift is concentrated in a subspace that could facilitate stable decoding if trial-to-trial variability lies in an encoding-null space. However, a residual component of drift remains aligned with the task-coding subspace, eventually disrupting a fixed decoder on longer timescales. We illustrate that this slower drift could be compensated in a biologically plausible way, with minimal synaptic weight changes and using a weak error signal. We conjecture that behavioral stability is achieved by active processes that constrain plasticity and drift to directions that preserve decoding, as well as adaptation of brain regions to ongoing changes in the neural code.
Rule, M. E., Loback, A. R., Raman, D. V., Harvey, C. D., O'Leary, T. S. (2019) Constrained plasticity can compensate for ongoing drift in neural populations. [Poster] UK Neural Computation 2019, July 1st Nottingham, UK
Edit: this work has now been published as two papers. The first finds that mesoscopic beta-LFP oscillations may arise due to synchronization of rhythmic spiking in single neurons. The second explores how changes in synchronization relate to the diverse patterns illustrated in the poster below.
I'll present some of my ongoing thesis research at SfN as a poster this year. (This was originally titled "Identification of (~20 Hz) beta spatiotemporal dynamics in motor cortex LFPs".)
Modulation of beta (10-45Hz) oscillations is a prominent feature of primate motor cortex. Beta power is typically elevated during movement preparation, suppressed around movement onset, and enhanced during isometric force tasks. As shown by previous studies, beta oscillations can also appears as traveling waves in primate motor cortex. Understanding the mechanisms underlying the rapid modulation of beta LFP activity and the associated spatiotemporal patterns may shed light on the functional roles of these oscillations. It may also have important implications for movement disorders where regulation of motor cortex beta activity is abnormal (e.g. Parkinson's disease). Here, we examine motor cortex beta spatiotemporal LFP activity using multielectrode arrays (MEAs) in m. mulatta during a cued reaching and grasping task with instructed delay. Data from two monkeys are analyzed, each with a 96-MEA in ventral premotor cortex (PMv), and two 48-MEAs in the primary motor cortex (M1) and dorsal premotor cortex (PMd), respectively. Our main findings are threefold: (1) The transient nature of beta oscillation events together with variations in the beta band center frequency makes the identification of spatiotemporal structures challenging. In particular, different filtering and preprocessing steps can alter the apparent spatiotemporal dynamics. (2) Furthermore, attempts to summarize wave dynamics in terms of simple global structures, like plane waves, or rotating (radiating) waves around (from) a critical point, may fail to meaningfully describe the full range of beta spatiotemporal activity. (3) Despite these challenges, we find a variety of beta spatiotemporal patterns ranging from asynchronous states, i.e. states with no clear wave dynamics, to more locally synchronized states with complex wave dynamics, to globally coherent states. These globally coherent states may exhibit either traveling wave dynamics or homogeneous synchrony. We conjecture that the transitions among these different patterns may result from fast modulations of the effective lateral connectivity or from changes in spatiotemporal inputs to the cortical area.
Rule, M. E., Vargas-Irwin, C., Donoghue, J., Truccolo, W. (2015) Identification of (~20 Hz) beta spatiotemporal dynamics in motor cortex LFPs. [Poster] Society for Neuroscience 2015, Oct 19th, Chicago, Il, USA.
Rule, M.E., Vargas-Irwin, C.E., Donoghue, J.P. and Truccolo, W., 2017. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. Journal of neurophysiology, 117(4), pp.1524-1543.
Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2018. Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation. Journal of neurophysiology, 119(6), pp.2212-2228.
