Biology subjects

Humphries, M. D.

Publications and source records attributed to Humphries, M. D..

12 recordsLinked to original sources

bioRxiv2026

Distinct Orbitofrontal Feedback Signals Shape Sensory Behavioral Strategies during Flexible Learning

Animals adapt their behavior by integrating sensory evidence with prior experience and contextual information. Understanding how animals employ specific behavioral strategies to integrate these variables and how neural mechanisms support such strategies remains unclear. We trained mice on a tactile reversal learning task and used a trial-by-trial computational model to infer latent decision strategies. Early in learning, mice relied on action-based ("choice-driven") policies but progressively transitioned to stimulus-guided choices ("cue-driven") as they learned the task. Following a rule reversal, mice flexibly reinstated this policy to adapt their behavior. Chemogenetic silencing of the lateral orbitofrontal cortex (lOFC) delayed this transition and impaired reversal learning. To identify the neural basis of strategy learning, we developed a novel approach combining low-dimensional analysis of neural activity with decoding of behavioral strategies from longitudinal two-photon imaging. We revealed reward- and error-strategy representations in excitatory layer 2/3 neurons in the primary somatosensory cortex (S1) that were differentially modulated by OFC instructive signals. Within S1, distinct subpopulations of positive and negative-valence-coding neurons tracked evolving decision strategies through trial-history integration. Together, these findings revealed how mice flexibly deploy distinct exploratory strategies during adaptive behavior, highlighting OFCs role in supporting reward and error-guided learning through distinct corticocortical interactions.

neuroscience↗

bioRxiv2026

Primary and Supplementary Motor cortex implement parallel control solutions for rhythmic and discrete arm movements

Arm movements are rhythmic, discrete, or some combination of the two. Conflicting evidence supports each of two possible solutions for how motor cortex controls them: that either it uses the same strategy for controlling rhythmic and discrete movements or different strategies for distinct movement types. Using recurrent neural network modelling and multi-unit recordings during an arm-cycling task, we show that primate motor cortex uses both solutions. Primary motor cortex (M1) dynamics converge to the same limit-cycle when executing both movement types. In contrast, supplementary motor area (SMA) dynamics diverge according to the type of the upcoming movement before reaching a helical spiral. Our results reconcile opposing views on the cortical control of rhythmic and discrete movements by showing that the two solutions are not mutually exclusive but implemented in parallel within motor cortex. Instead, our results challenge us to understand why both solutions are implemented to solve the same problem.

neuroscience↗

bioRxiv2025

Stochastic choice drives variability in patch foraging decisions across species

Staying to exploit remaining resources or leaving to seek better options elsewhere is a fundamental decision across species. Optimal patch foraging theories propose deterministic rules for when to leave a depleting resource but real foragers show considerable variability in when they leave. Decisions between simultaneously-presented options are often assumed to follow a stochastic decision policy, adding randomness into the choice process to allow for exploration of potentially better alternatives. Whether a stochastic choice policy can account for variability in sequential foraging decisions, and what predictions such a policy makes for the mechanisms of foraging choice, are unknown. Here using patch foraging datasets in both humans (n = 39, n = 29) and rats (n = 8), we show that foragers making a stochastic choice of when to leave a patch is sufficient to explain their variability. We also show stochastic choice makes two unintuitive predictions, which we validate in our data. First, under a wide range of conditions, stochastic choice makes foragers leaving variability independent of the rewards available in the environment. Second, that foragers use a suboptimal internal function for setting their choice stochasticity from their environments average reward rate. Our findings suggest stochastic choice is an underappreciated but powerful contributor to foraging decisions, and highlight how behavioural variability, which is often overlooked, can reveal the algorithmic underpinning of decisions.

animal behavior and cognition↗

bioRxiv2024

The computational bottleneck of basal ganglia output (and what to do about it)

What the basal ganglia do is an oft-asked question; answers range from the selection of actions to the specification of movement to the estimation of time. Here I argue that how the basal ganglia do what they do is a less-asked but equally important question. I show that the output regions of the basal ganglia create a stringent computational bottleneck, both structurally, because they have far fewer neurons than do their target regions, and dynamically, because of their tonic, inhibitory output. My proposed solution to this bottleneck is that the activity of an output neuron is setting the weight of a basis function, a function defined by that neurons synaptic contacts. I illustrate how this may work in practice, allowing basal ganglia output to shift cortical dynamics and control eye movements via the superior colliculus. This solution can account for troubling issues in our understanding of the basal ganglia: why we see output neurons increasing their activity during behaviour, rather than only decreasing as predicted by theories based on disinhibition, and why the output of the basal ganglia seems to have so many codes squashed into such a tiny region of the brain. Significance statementThe basal ganglia are implicated in an extraordinary range of functions, from action selection to timing, and dysfunctions, from Parkinsons disease to obsessive compulsive disorder. Yet however the basal ganglia cause these functions and dysfunctions they must do so through a group of neurons that are dwarfed in number by both their inputs and their output targets. Here I lay out this bottleneck problem for basal ganglia computation, and propose a solution to how their outputs can control their many targets. That solution rethinks the output connections of the basal ganglia as a set of basis functions. In doing so, it provides explanations for previously troubling data on basal ganglia output, and strong predictions for how that output controls its targets.

neuroscience↗

bioRxiv2023

Motor cortex latent dynamics encode arm movement direction and urgency independently

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movements direction corre-sponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity. Significance StatementFrom delicate strokes while drawing to ballistic swings while playing tennis, a skilled arm movement requires precise control of both its direction and speed. Motor cortex is thought to play a key role in controlling both, but it is unclear how they are jointly controlled. We show here that the population activity in motor cortex represents both the spatial and temporal properties of arm movements in the same low-dimensional signal. This representation was remarkably simple: the movements direction is represented by the trajectory that signal takes; the movements speed by how quickly the signal moves along its trajectory. Our network modelling shows this encoding allows an arm movements direction and speed to be simultaneously and independently controlled.

neuroscience↗

bioRxiv2022

Tracking subject's strategies in behavioural choice experiments at trial resolution

Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

neuroscience↗

bioRxiv2021

Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections

The striatums complex microcircuit is made by connections within and between its D1- and D2-receptor expressing projection neurons and at least five species of interneuron. Precise knowledge of this circuit is likely essential to understanding striatums functional roles and its dysfunction in a wide range of movement and cognitive disorders. We introduce here a Bayesian approach to mapping neuron connectivity using intracellular recording data, which lets us simultaneously evaluate the probability of connection between neuron types, the strength of evidence for it, and its dependence on distance. Using it to synthesise a complete map of the mouse striatum, we find strong evidence for two asymmetries: a selective asymmetry of projection neuron connections, with D2 neurons connecting twice as densely to other projection neurons than do D1 neurons, but neither subtype preferentially connecting to another; and a length-scale asymmetry, with interneuron connection probabilities remaining non-negligible at more than twice the distance of projection neuron connections. We further show our Bayesian approach can evaluate evidence for wiring changes, using data from the developing striatum and a mouse model of Huntingtons disease. By quantifying the uncertainty in our knowledge of the microcircuit, our approach reveals a wide range of potential striatal wiring diagrams consistent with current data.

neuroscience↗

bioRxiv2019

On the use of calcium deconvolution algorithms in practical contexts

Calcium imaging is a powerful tool for capturing the simultaneous activity of large populations of neurons. Here we determine the extent to which our inferences of neural population activity, correlations, and coding depend on our choice of whether and how we deconvolve the calcium time-series into spike-driven events. To this end, we use a range of deconvolution algorithms to create nine versions of the same calcium imaging data obtained from barrel cortex during a pole-detection task. Seeking suitable values for the deconvolution algorithms parameters, we optimise them against ground-truth data, and find those parameters both vary by up to two orders of magnitude between neurons and are sensitive to small changes in their values. Applied to the barrel cortex data, we show that a substantial fraction of the processing methods fail to recover simple features of population activity in barrel cortex already established by electrophysiological recordings. Raw calcium time-series contain an order of magnitude more neurons tuned to features of the pole task; yet there is also qualitative disagreement between deconvolution methods on which neurons are tuned to the task. Finally, we show that raw and processed calcium time-series qualitatively disagree on the structure of correlations within the population and the dimensionality of its joint activity. Collectively, our results show that properties of neural activity, correlations, and coding inferred from calcium imaging are sensitive to the choice of if and how spike-evoked events are recovered. We suggest that quantitative results obtained from population calcium-imaging be verified across multiple processed forms of the calcium time-series.

neuroscience↗

bioRxiv2019

Independent population coding of the present and the past in prefrontal cortex during learning

Medial prefrontal cortex (mPfC) activity represents information about the state of the world, including present behaviour, such as decisions, and the immediate past, such as short-term memory. Unknown is whether information about different states of the world are represented in the same mPfC neural population and, if so, how they are kept distinct. To address this, we analyse here mPfC population activity of rats learning rules in a Y-maze, with self-initiated choice trials to an arm-end followed by a self-paced return during the inter-trial interval (ITI). We find that trial and ITI population activity from the same population fall into different low-dimensional subspaces. These subspaces encode different states of the world: multiple features of the task can be decoded from both trial and ITI activity, but the decoding axes for the same feature are roughly orthogonal between the two task phases, and the decodings are predominantly of features of the present during the trial but features of the preceding trial during the ITI. These subspace distinctions are carried forward into sleep, where population activity is preferentially reactivated in post-training sleep, but differently for activity from the trial and ITI subspaces. Our results suggest that the problem of interference when representing different states of the world is solved in mPfC by population activity occupying different subspaces for the world states, which can be independently decoded by downstream targets and independently addressed by upstream inputs. Significance statementActivity in the medial prefrontal cortex plays a roles in representing the current and past states of the world. We show that during a maze task the activity of a single population in medial prefrontal cortex represents at least two different states of the world. These representations were sequential and sufficiently distinct that a downstream population could separately read out either state from that activity. Moreover, the activity representing different states is differently reactivated in sleep. Different world states can thus be represented in the same medial prefrontal cortex population, but in such a way that prevents potentially catastrophic interference between them.

neuroscience↗

bioRxiv2018

Prediction of Choice from Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.\n\nSIGNIFICANCE STATEMENTDue to the difficulty of measuring the sensory input to moving sense organs, active perceptual decision making remains poorly understood. The whisker system provides a way forward since it is now possible to measure the mechanical forces due to whisker-object contact during behaviour. Here we train mice in a novel behavioural task that challenges them to use rich mechanosensory cues, but can be performed using one whisker and enables task-relevant mechanical forces to be precisely estimated. This approach enables rigorous study of how sensory cues translate into action during active, perceptual decision making. Our findings provide new insight into active touch and how sensory/internal signals interact to determine behavioural choices.

animal behavior and cognition↗

bioRxiv2017

Dynamical networks: finding, measuring, and tracking neural population activity using network theory

Systems neuroscience is in a head-long rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons comes the inescapable problems of visualising, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualise and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analysing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

neuroscience↗

bioRxiv2017

A spiral attractor network drives locomotion in Aplysia

The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysias pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit -a spiral -in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

neuroscience↗