Rabbits can learn the biological analogue of a simple recursive function by relying only on the neurons of the cerebellum.
Sequential patterns like the Fibonacci numbers, as well as the movements that produce a tied shoelace, are examples of recursion: the process begins with a seed that a system uses to generate an output. That output is then fed back to the system as a self-generated input, which in turn becomes a new output. The result is a recursive function that uses a seed (external input) at time t to generate outputs at times t, t + D, t + 2D and so on (where D is a constant interval of time). Now, in eLife, Andrei Khilkevich, Juan Zambrano, Molly-Marie Richards and Michael Mauk of the University of Texas at Austin report the results of experiments on rabbits which shed light on how the brain learns the biological analogue of a recursive function (Khilkevich et al., 2018).
To present the seed that started a sequence of motor outputs, Khilkevich et al. electrically stimulated the mossy fibers that provided inputs to the cerebellum. Near the end of the period of mossy fiber stimulation, they electrically stimulated the skin near the eyelid, which caused the rabbits to blink. The blink was the motor output. With repeated exposure to the mossy fiber input and the eyelid stimulation, the cerebellum learned to predict that the mossy fiber stimulation would be followed by the eyelid stimulation (Krupa et al., 1993), which then led to an anticipatory blink at time t. That is, given an input to the cerebellum at time t, the animals learned to produce an output at the same time. The technical term for this kind of learning is classical conditioning.
However, the goal for the rabbits was to learn to blink not just at time t, but also at times t + D, t + 2D and so on. That is, the challenge for the animal was to learn to use its own motor output at time t (the eye blink) as the cue needed to produce a second blink at t + D. To do this, Khilkevich et al. measured the eyelid response at time t. If the eye was less than 50% closed, they stimulated the eyelid as usual. However, if the eye was more than 50% closed, they stimulated it 600 milliseconds later (that is, at t + D). The critical point is that there was no input to the mossy fibers at t + D. Although earlier experiments had shown that the cerebellum was not able to associate a mossy fiber input with stimulation of the eyelid when the delay between them was longer than 400 milliseconds (Kalmbach et al., 2010), Khilkevich et al. found that the animals learned to blink not only at time t, but also at time t + D.
Their hypothesis was that the sequence was learned through recursion: a copy of the commands for the blink at t was sent as input to the cerebellum, allowing it to associate these commands with the eyelid stimulation at t + D, and thereby learning to blink at t + D. An elegant experiment confirmed this hypothesis: Khilkevich et al. found that after training was completed and the rabbits blinked at times t and t + D, omitting the eyelid stimulation at time t resulted in the extinction of the blinks at times t and t + D. Moreover, and rather remarkably, even if the eyelid was subsequently stimulated at time t + D, there was still no blink. This established the fundamental feature of the recursive function: without the blink at time t, which was generated because of the mossy fiber input at t, the animal could not produce a blink at time t + D.
Under normal conditions, the principal cells of the cerebellum, Purkinje cells, produce a steady stream of simple spikes. As the animal learns to associate the mossy fiber input with the eyelid stimulation, the Purkinje cells reduce their simple spike discharge just before the blink at time t, and again before the second blink at t + D (Jirenhed et al., 2017). Khilkevich et al. found that the modulation of the spikes before t + D appeared to be causal, because there was no blink response at t + D if there was no modulation around time t + D. The timing of the modulation at t and t + D also appeared consistent with a role for the cerebellum in generating the recursive function.
The results of Khilkevich and co-workers expand the range of learning behaviors that have been ascribed to the cerebellum. Earlier work had shown that Purkinje cells learn to associate motor commands with their sensory consequences (Herzfeld et al., 2015), forming 'forward models' that enable animals to control their movements with precision and accuracy (Heiney et al., 2014; Herzfeld et al., 2018). The new results demonstrate that Purkinje cells can also learn recursive functions, using a seed plus feedback from the animal’s own actions to construct a sequence of movements.
Precise control of movement kinematics by optogenetic inhibition of Purkinje cell activityJournal of Neuroscience 34:2321–2330.https://doi.org/10.1523/JNEUROSCI.4547-13.2014
Temporal patterns of inputs to cerebellum necessary and sufficient for trace eyelid conditioningJournal of Neurophysiology 104:627–640.https://doi.org/10.1152/jn.00169.2010
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
Neurons communicate by the activity-dependent release of small-molecule neurotransmitters packaged into synaptic vesicles (SVs). Although many molecules have been identified as neurotransmitters, technical limitations have precluded a full metabolomic analysis of SV content. Here, we present a workflow to rapidly isolate SVs and to interrogate their metabolic contents at high-resolution using mass spectrometry. We validated the enrichment of glutamate in SVs of primary cortical neurons using targeted polar metabolomics. Unbiased and extensive global profiling of SVs isolated from these neurons revealed that the only detectable polar metabolites they contain are the established neurotransmitters glutamate and GABA. In addition, we adapted the approach to enable quick capture of SVs directly from brain tissue and determined the neurotransmitter profiles of diverse brain regions in a cell-type-specific manner. The speed, robustness, and precision of this method to interrogate SV contents will facilitate novel insights into the chemical basis of neurotransmission.
In vitro work revealed that excitatory synaptic inputs to hippocampal inhibitory interneurons could undergo Hebbian, associative, or non-associative plasticity. Both behavioral and learning-dependent reorganization of these connections has also been demonstrated by measuring spike transmission probabilities in pyramidal cell-interneuron spike cross-correlations that indicate monosynaptic connections. Here we investigated the activity-dependent modification of these connections during exploratory behavior in rats by optogenetically inhibiting pyramidal cell and interneuron subpopulations. Light application and associated firing alteration of pyramidal and interneuron populations led to lasting changes in pyramidal-interneuron connection weights as indicated by spike transmission changes. Spike transmission alterations were predicted by the light-mediated changes in the number of pre- and postsynaptic spike pairing events and by firing rate changes of interneurons but not pyramidal cells. This work demonstrates the presence of activity-dependent associative and non-associative reorganization of pyramidal-interneuron connections triggered by the optogenetic modification of the firing rate and spike synchrony of cells.