A dive into a few of our key papers - see below for the full list!
A SPIRAL ATTRACTOR NETWORK DRIVES RHYTHMIC LOCOMOTION
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 Aplysia's 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.
POPULATION-WIDE DISTRIBUTIONS OF NEURAL ACTIVITY DURING PERCEPTUAL DECISION-MAKING
We reviewed the statistical distributions of activity in large populations of cortical neurons, in awake behaving animals, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity and tuning, where quasi-silence seems to be the rule for a majority of neurons; (ii) activity distributions are barely distinguishable between spontaneous and active states; (iii) distributions of tuning parameters for sensory and motor variables show an extensive extrapolation and fragmentation of their representations in the periphery; and (iv) population-wide dynamics that reveal rotations of internal
representations over time, whose traces can be found both in stimulus-driven and internally generated activity. These insights lead us away from the notion of discrete classes of cortical neuron.
DOPAMINERGIC CONTROL OF THE EXPLORATION-EXPLOITATION TRADE-OFF VIA THE BASAL GANGLIA
The `exploration-exploitation' trade-off depends on the environment: stability favours exploiting knowledge to maximise gains; volatility favours exploring new options and discovering new outcomes. Here we proposed the hypothesis that tonic dopamine in the striatum, the basal ganglia's input nucleus, sets the current exploration-exploitation tradeoff. We first advance the idea of interpreting the basal ganglia output as a probability distribution function for action selection. Using computational models of the full basal ganglia circuit, we showed that, under this interpretation, the actions of dopamine within the striatum change the basal ganglia's output to favour the level of exploration or exploitation encoded in the probability distribution. We also found that our models predict striatal dopamine controls the exploration-exploitation trade-off if we instead read out the probability distribution from the target nuclei of the basal ganglia, where their inhibitory input shapes the cortical input to these nuclei. Finally, by integrating the basal ganglia within a reinforcement learning model, we showed how dopamine's effect on the exploration-exploitation trade-off could be measurable in a forced two-choice task. These simulations also showed how tonic dopamine can appear to affect learning while only directly altering the trade-off.
Humphries, M. D., Caballero, J. A.*, Evans, M.*, Maggi, S.* & Singh, A.* (2019) Spectral rejection for testing hypotheses of structure in networks. arXiv: 1901.04747. [* all these authors contributed equally, and are listed alphabetically]
Gilbertson, T., Humphries, M. D. & Steele, J. D. (2019)
Maladaptive striatal plasticity and abnormal reward-learning in cervical dystonia. European Journal of Neuroscience, in press.
Singh, A., Peyrache, A. & Humphries. M. D. (2019) Medial prefrontal cortex population activity is plastic irrespective of learning. Journal of Neuroscience, 39, 3470-3483
[Read the write-up at PNAS Front Matter]
Campagner, D., Evans, M. H., Chlebikova, K., Colins-Rodriguez, A., Loft, M. S., Fox, S., Pettifer, D., Humphries, M. D., Svoboda, K. & Petersen, R. S. (2019) Prediction of choice from competing mechanosensory and choice-memory cues during active tactile decision making. Journal of Neuroscience, 39, 3921-3933.
Maggi, S., Peyrache, A. & Humphries, M. D. (2018) An ensemble code in medial prefrontal cortex links prior events to outcomes during learning. Nature Communications, 9, 2204
Humphries, M. D., Obeso, J. A., & Dreyer, J. (2018) Insights into Parkinson’s disease from computational models of the basal ganglia. Journal of Neurology, Neurosurgery and Psychiatry, 89, 1181-1188.
Caballero, J., Humphries, M. D. *, & Gurney, K. * (2018) A probabilistic, brain-distributed, recursive mechanism for decision-making. PLoS Computational Biology, 14, e1006033. [* Joint senior authors]
Bruno, A., Frost, W. F., & Humphries, M. D. (2017) A spiral attractor generates rhythmic locomotion. eLife, 6, e27342.
Humphries, M. D. (2017) Dynamical networks: finding, measuring, and tracking neural population activity using network science. Network Neuroscience, 1, 324-338
Humphries, M. D. (2016) The Goldilocks zone in neural circuits. eLife, 5, e22735
[Open access download]
And a bunch of preprints that eventually turned into the above papers...
Bruno, A., Frost, W. F., & Humphries, M. D. (2015) Modular deconstruction reveals the dynamical and physical building blocks of a locomotion motor program. Neuron, 86, 304-318.
[Read the Preview in the same issue: Brownstone, R. M. & Stifani, N. (2015) Unraveling a locomotor network, many neurons at a time. Neuron, 86, 9-11]
Gurney, K. Humphries, M. D.* & Redgrave, P.* (2015) A new framework for cortico-striatal plasticity: behavioural theory meets in vitro data at the reinforcement-action interface. PLoS Biology, 13, e1002034. [* These authors contributed equally to this work]
[Read the Synopsis in the same issue: Weaver, J (2015) Computational framework explains how animals select actions with rewarding outcomes. PLoS Biology, 13, e1002035]
Singh, A. & Humphries, M. D. (2015) Finding communities in sparse networks. Scientific Reports, 5, 8828.
[Open access download]
Beste, C.*, Humphries, M. D.* & Saft, C.* (2014) Striatal disorders dissociate mechanisms of enhanced and impaired response selection - Evidence from cognitive neurophysiology and computational modelling. Neuroimage Clinical, 4, 623-634. [* All authors contributed equally to this work]
Carron, R., Filipchuk, A., Nardou, R., Singh, A., Michel, F. J., Humphries, M. D. & Hammond, C. (2014) Early hypersynchrony in juvenile PINK1(-)/(-) motor cortex is rescued by antidromic stimulation. Frontiers in Systems Neuroscience, 8, 95
Tomkins, A., Vasilaki, E., Beste, C., Gurney, K. & Humphries, M.D. (2014) Transient and steady-state selection in the striatal microcircuit. Frontiers in Computational Neuroscience, 7, 192.
[Open access download]
Wohrer, A.*, Humphries, M. D.* & Machens, C. (2013) Population-wide distributions of neural activity during perceptual decision-making. Progress in Neurobiology, 103, 156-193. [* These authors contributed equally to this work]
Cazé, R. D., Humphries, M. & Gutkin, B. (2013) Passive dendrites enable single neurons to compute linearly non-separable functions. PLoS Computational Biology, 2013, 9, e1002867
Khamassi, M. & Humphries, M. D. (2012) Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies. Frontiers in Behavioural Neuroscience, 6, 79
Humphries, M. D., & Gurney, K. (2012) Network effects of subthalamic deep brain stimulation drive a unique mixture of responses in basal ganglia output. European Journal of Neuroscience, 36, 2240-2251.
Humphries, M. D., Khamassi, M. & Gurney, K. (2012) Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Frontiers in Neuroscience, 6:9.
[Open access download]
Dehorter, N., Michel, F., Marissal, T., Rotrou, Y., Matrot, B., Lopez, C., Humphries, M. & Hammond, C. (2011) Onset of pup locomotion coincides with loss of NR2C/D-mediated corticostriatal EPSCs and dampening of striatal network immature activity. Frontiers in Cellular Neuroscience, 5, 24. 22.
Humphries, M. D. (2011) Spike-train communities: finding groups of similar spike-trains. Journal of Neuroscience, 31, 2321-2336.
Humphries, M. D., Gurney, K. & Prescott, T. (2011) The medial reticular formation: a brainstem substrate for simple action selection? In A. K. Seth, T. J. Prescott & J. J. Bryson (Eds) Modelling Natural Action Selection (pp. 300-329). Cambridge, UK: CUP.
Humphries, M. D., Wood, R. & Gurney, K. (2010) Reconstructing the three dimensional GABAergic microcircuit of the striatum. PLoS Computational Biology, 6, e1001011.
Humphries, M. D. & Prescott, T. (2010). The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward. Progress in Neurobiology, 90: 385-417.
[Read highlights at the Faculty of 1000]
Humphries, M. D., Lepora, N., Wood, R. & Gurney, K. (2009). Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models. Frontiers in Computational Neuroscience, 3, 26.
Humphries, M. D., Wood, R. & Gurney, K. (2009) Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit. Neural Networks, 22, 1174-1188.
Fox, C. W., Humphries, M. D., Mitchinson, B., Somogyvari, Z., Kiss, T & Prescott, T. (2009) Technical integration of hippocampus, basal ganglia, and physical models for spatial navigation. Frontiers in Neuroinformatics, 3, 6.
[Open access download]
Lowe, R., Humphries, M. & Ziemke, T. (2009). The dual-route hypothesis: Evaluating a neurocomputational model of fear conditioning in rats. Connection Science, 21, 15-37.
Humphries, M. D. & Gurney, K. (2008). Network `small-world-ness': a quantitative method for determining canonical network equivalences. PLoS One, 3: e0002051.
Humphries, M. D. & Gurney, K. (2007) Solutions methods for a new class of simple model neurons. Neural Computation, 19: 3216-3225.
Humphries, M. D., Gurney, K. & Prescott, T. (2007) Is there a brainstem substrate for action selection? Philosophical Transactions of the Royal Society B. Biological Sciences, 362: 1627-1639.
Humphries, M. D. & Gurney, K. (2007) A means to an end: validating models by fitting experimental data. Neurocomputing, 70:1892-1896
Humphries, M. D., Stewart, R. D. & Gurney, K. (2006) A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. Journal of Neuroscience, 26: 12921-12942.
Humphries, M. D., Gurney, K. & Prescott, T. (2006) The brainstem reticular formation is a small-world, not scale-free, network. Proceedings of the Royal Society B. Biological Sciences, 273: 503-511.
Humphries, M., Prescott, T. (2006). Distributed action selection by a brainstem neural substrate: an embodied evaluation. In S. Nolfi, G. Baldassarre, R. Calabretta, J. Hallam, D. Marocco, O. Miglino, J.-A. Meyer, & D. Parisi (Eds.) From Animals to Animats 9 (pp. 199-210). Berlin, Germany: Springer-Verlag.
Prescott, T. J., Montes Gonzalez, F. M., Gurney, K., Humphries, M. D., & Redgrave, P. (2006). A robot model of the basal ganglia: behavior and intrinsic processing. Neural Networks, 19: 31-61.
Humphries, M. D., Gurney, K. & Prescott, T. J. (2005) Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of the reticular formation viewed as an action selection device. Adaptive Behaviour, 13: 97-113.
Gurney, K. N., Humphries, M., Wood, R. Prescott, T. J. & Redgrave, P. (2004) Testing computational hypotheses of brain systems function using high level models: a case study with the basal ganglia. Network: Computation in Neural Systems, 15: 263-290.
Humphries, M. D. & Gurney, K. N. (2002). The role of intra-thalamic and thalamocortical circuits in action selection. Network: Computation in Neural Systems, 13: 131-156.