The cerebellum is well established in subsecond motor timing, but its role in suprasecond interval timing remains unclear. Here, we investigated how cerebellar output influences time estimation over longer timescales. Male rats performed a nose-poke interval timing task in which reward availability could be predicted either from a fixed 2.5 s auditory cue (cued trials) or had to be estimated internally during uncued 3.5 s trials that demanded self-timing. Chemogenetic inhibition of the lateral cerebellar nucleus (LCN) produced bidirectional effects: delayed action initiation in predictable trials and premature (∼100-160 ms) responses when self-timing was required. Despite a slowing of movement, overall task success rates remained unchanged. Because motor slowing is likely to lead to later, not earlier, action initiation, these results implicate the LCN in computing internal time estimates. These findings demonstrate that the cerebellum integrates motor and cognitive processes for suprasecond timing, with differential effects on externally guided and self-generated timing.

The cerebellum is well-established in sub-second motor timing, but its role in supra-second interval timing remains unclear. Here, we investigate how cerebellar output influences time estimation over longer timescales. Rats performed an interval timing task, estimating time based on an auditory cue, while chemogenetic inhibition of the lateral cerebellar nucleus assessed its role in both predictable (externally cued) and unpredictable (internally cued) timing conditions. Cerebellar inhibition produced bidirectional effects: delayed action initiation in predictable trials and premature responses in unpredictable trials. Despite slowed movement, overall task success rates remained unchanged, suggesting a specific impairment in temporal estimation rather than motor execution. These findings demonstrate that the cerebellum integrates motor and cognitive processes for supra-second timing, with differential effects on externally guided and self-generated timing. Our results provide evidence that the lateral cerebellum contributes to supra-second interval timing, supporting its role in adaptive behavior across extended timescales. ### Competing Interest Statement The authors have declared no competing interest.

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding cerebro-cerebellar task-specific representations over learning, task-specific benefits of cerebellar predictions and the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

Performing successful adaptive behaviour relies on our ability to process a wide range of temporal intervals with certain precision. Studies on the role of the cerebellum in temporal information processing have adopted the dogma that the cerebellum is involved in sub-second processing. However, emerging evidence shows that the cerebellum might be involved in suprasecond temporal processing as well. Here we review the reciprocal loops between cerebellum and cerebral cortex and provide a theoretical account of cerebro-cerebellar interactions with a focus on how cerebellar output can modulate cerebral processing during learning of complex sequences. Finally, we propose that while the ability of the cerebellum to support millisecond timescales might be intrinsic to cerebellar circuitry, the ability to support supra-second timescales might result from cerebellar interactions with other brain regions, such as the prefrontal cortex.

Primary sensory cortex is thought to process incoming sensory information, while decision variables important for driving behavior are assumed to arise downstream in the processing hierarchy. Here, we used population two-photon calcium imaging and targeted two-photon optogenetic stimulation of neurons in layer 2/3 of mouse primary somatosensory cortex (S1) during a texture discrimination task to test for the presence of decision signals and probe their behavioral relevance. Small but distinct populations of neurons carried information about the stimulus irrespective of the behavioral outcome (stimulus neurons), or about the choice irrespective of the presented stimulus (decision neurons). Decision neurons show categorical coding that develops during learning, and lack a conclusive decision signal in Miss trials. All-optical photostimulation of decision neurons during behavior improves behavioral performance, establishing a causal role in driving behavior. The fact that stimulus and decision neurons are intermingled challenges the idea of S1 as a purely sensory area, and causal perturbation suggests a direct involvement of S1 decision neurons in the decision-making process.

We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.

The brain solves the credit assignment problem remarkably well. For credit to be assigned across neural networks they must, in principle, wait for specific neural computations to finish. How the brain deals with this inherent locking problem has remained unclear. Deep learning methods suffer from similar locking constraints both on the forward and feedback phase. Recently, decoupled neural interfaces (DNIs) were introduced as a solution to the forward and feedback locking problems in deep networks. Here we propose that a specialised brain region, the cerebellum, helps the cerebral cortex solve similar locking problems akin to DNIs. To demonstrate the potential of this framework we introduce a systems-level model in which a recurrent cortical network receives online temporal feedback predictions from a cerebellar module. We test this cortico-cerebellar recurrent neural network (ccRNN) model on a number of sensorimotor (line and digit drawing) and cognitive tasks (pattern recognition and caption generation) that have been shown to be cerebellar-dependent. In all tasks, we observe that ccRNNs facilitates learning while reducing ataxia-like behaviours, consistent with classical experimental observations. Moreover, our model also explains recent behavioural and neuronal observations while making several testable predictions across multiple levels. Overall, our work offers a novel perspective on the cerebellum as a brain-wide decoupling machine for efficient credit assignment and opens a new avenue between deep learning and neuroscience.

This perspective piece came about through the Generative Adversarial Collaboration (GAC) series of workshops organized by the Computational Cognitive Neuroscience (CCN) conference in 2020. We brought together a number of experts from the field of theoretical neuroscience to debate emerging issues in our understanding of how learning is implemented in biological recurrent neural networks. Here, we will give a brief review of the common assumptions about biological learning and the corresponding findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks commonly used in artificial intelligence. We will then outline the key issues discussed in the workshop: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. Finally, we conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help to bring clarity to these issues.

Event Abstract Back to Event Correlating calcium dynamics with network activity in an in vitro model of a cortical microcircuitry Ellen Boven1* and Michele Giugliano1, 2, 3 1 University of Antwerp, Theoretical Neurobiology & Neuroengineering, Belgium 2 University of Sheffield, United Kingdom 3 EPFL, Brain Mind Institute, Switzerland Spatiotemporal pattern formation and information processing in the brain depends on the interplay between cellular and network synchronization. A central theme in current research is how the brain’s spontaneous electrical activity [1] contributes to wiring of its networks, both during development and adulthood. Moreover, as synaptic plasticity is known to depend on the exact firing times of neurons [2], spontaneous activity in neuronal microcircuits is likely to contribute to and emerge from a balance between structure and function. On the other hand, calcium operates as a ubiquitous intracellular messenger and is known to link electrical activity to (sub)cellular pathways, ranging from gene expression to axonal pathfinding [3]. Despite our current knowledge on the biophysics of intracellular and extracellular calcium dynamics and storage, little is known about the exact relationship between neuronal activity and calcium, when large networks are considered. In this work, we combine two experimental techniques performing simultaneous recordings of network electrophysiological activity and of free intracellular calcium concentration, in primary neurons dissociated from the rat neocortex. This is achieved by substrate-integrated microelectrode arrays (MEA) and by fluorescent imaging of AAV-Synapsin-GCamp6. This combination enables us to monitor collective neural population spiking activity and calcium transients with high spatial resolution. We first verify that calcium transients across distinct portions of cultured cortical networks is characterised by spatially-synchronized intracellular waves. Then we report that these waves are largely co-occurring with recurrent synchronization of electrical activity, through the same networks. We finally investigate and challenge a contribution to the literature [4] on whether the same correlation between calcium and synchronous network electrical activity is indeed maintained throughout successive ex vivo developmental stages. Directly correlating fast, electrical neuronal activity and, slow, intracellular calcium concentration dynamics is therefore a key to link neuronal networks formation and (dys)functions. References [1] M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nat Rev Neurosci, vol. 8, no. 9, pp. 700–711, 2007. [2] H. Markram, W. Gerstner, and P. J. Sjöström, “Spike-timing-dependent plasticity: A comprehensive overview,” Front. Synaptic Neurosci., vol. 4, no. JULY, pp. 2010–2012, 2012. [3] J. Henley and M.-M. Poo, “Guiding neuronal growth cones by Ca2+ signals,” Trends Cell Biol, vol. 14, no. 6, pp. 320–330, 2011. [4] Y. Takayama, H. Moriguchi, K. Kotani, and Y. Jimbo, “Spontaneous Calcium Transients in Cultured Cortical Networks During Development,” vol. 56, no. 12, pp. 2949–2956, 2009. Keywords: microelectrode arrays, calcium imaging, neural networks, development, GCaMP6 Conference: 12th National Congress of the Belgian Society for Neuroscience, Gent, Belgium, 22 May - 22 May, 2017. Presentation Type: Poster Presentation Topic: Development Citation: Boven E and Giugliano M (2019). Correlating calcium dynamics with network activity in an in vitro model of a cortical microcircuitry. Front. Neurosci. Conference Abstract: 12th National Congress of the Belgian Society for Neuroscience. doi: 10.3389/conf.fnins.2017.94.00071 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 24 Apr 2017; Published Online: 25 Jan 2019. * Correspondence: Miss. Ellen Boven, University of Antwerp, Theoretical Neurobiology & Neuroengineering, Wilrijk, Antwerp, 2610, Belgium, ellen.boven@student.uantwerpen.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ellen Boven Michele Giugliano Google Ellen Boven Michele Giugliano Google Scholar Ellen Boven Michele Giugliano PubMed Ellen Boven Michele Giugliano Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.