Biology subjects

Gillon, C. J.

Publications and source records attributed to Gillon, C. J..

5 recordsLinked to original sources

bioRxiv2026

Few-shot learning of predictive features with dendrites and behavioural timescale synaptic plasticity in the hippocampus

When an animal enters a new environment, neurons in the hippocampus begin to map out the space. They become selectively responsive to features like the animals location, the position of rewards, and the presence of stimuli relevant to navigating or performing a specific task. With experience, hippocampal neurons also develop behaviour-related biases. It is thus believed that the hippocampus encodes multi-sensory, behaviourally-relevant cognitive maps of environments that are critical to navigation, learning and guiding behaviour. The predictive learning hypothesis proposes that these complex maps emerge because a core goal of the brain is to learn to predict the features of its environment. In sensory cortex, predictive learning provides a compelling explanation of anticipatory and error-like sensory responses. Pyramidal neurons receive top-down and bottom-up inputs to their proximal basal and distal apical dendrites, respectively. These complementary inputs streams are thought to enable them to act as comparison units, signaling discrepancies between predictive and sensory inputs. In the hippocampus, however, the potential link between pyramidal neurons and predictive learning is still underexplored. Here, we investigate the possibility that pyramidal neurons perform a similar comparator function in the hippocampus. In our model, two-compartment pyramidal neurons receive sensory information about salient features of the environment at their distal apical dendrites which is compared to tuned spatial inputs received more proximally to their cell body. We demonstrate how predictive learning implemented in this circuit using behavioural timescale synaptic plasticity and distal apical inhibition can explain a variety of spatial and behaviourally-relevant features encoded in the hippocampus. We also lay out key predictions for validating our model experimentally. As such, our work helps bridge an important gap in the literature on predictive learning in the hippocampus and set the stage for more robust experimental validation of this prominent hypothesis.

neuroscience↗

bioRxiv2023

Learning predictive signals within a local recurrent circuit

The predictive coding hypothesis proposes that top-down predictions are compared with incoming bottom-up sensory information, with prediction errors signaling the discrepancies between these inputs. While this hypothesis explains the presence of prediction errors, recent experimental studies suggest that prediction error signals can emerge within a local circuit, that is, from bottom-up sensory input alone. In this paper, we test whether local circuits alone can generate predictive signals by training a recurrent spiking network using local plasticity rules. Our network model replicates prediction errors resembling various experimental results, such as a biphasic pattern of prediction errors and context-specific representation of error signals. Our findings shed light on how synaptic plasticity can shape prediction errors and enables the acquisition and updating of an internal model of sensory input within a recurrent neural network.

neuroscience↗

bioRxiv2022

Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Cortical neurons in primary sensory cortex carry not only sensory but also behavior-related information. However, it remains unclear how these types of information emerge and are integrated with one another over learning and what the relative contribution of activity in individual cells versus neuronal populations is in this process. Current evidence supports two opposing views of learning-related changes: 1) sensory information increases in primary cortex or 2) sensory information remains stable in primary cortex but its readout efficiency in association cortices increases. Here, we investigate these questions in primary sensory cortex during learning of a sensory task. Over the course of weeks, we imaged neuronal activity at different depths within layers 2 and 3 of the mouse vibrissal primary somatosensory cortex (vS1) before, during, and after training on a whisker-based object-localization task. We leveraged information theoretical analysis to quantify stimulus and behavior-related information in vS1 and estimate how much neural activity encoding sensory information is used to inform perceptual choices as sensory learning progresses. We also quantified the extent to which these types of information are supported by an individual neuron or population code. We found that, while sensory information rises progressively from the start of training, choice information is only present in the final stages of learning and is increasingly supported by a population code. Moreover, we demonstrate that not only the increase in available information, but also a more efficient readout of such information in primary sensory cortex mediate sensory learning. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

neuroscience↗

bioRxiv2021

Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

neuroscience↗

bioRxiv2021

Learning from unexpected events in the neocortical microcircuit

Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. According to this conjecture, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of information. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. In line with this conjecture, different responses to expected and unexpected sensory features have been observed in spiking and somatic calcium events. However, it remains unknown whether these unexpected event signals occur in the distal apical dendrites where many top-down signals are received, and whether these signals govern subsequent changes in the brains stimulus representations. Here, we show that both somata and distal apical dendrites of cortical pyramidal neurons exhibit distinct unexpected event signals that systematically change over days. These findings were obtained by tracking the responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons over multiple days in primary visual cortex of awake, behaving mice using two-photon calcium imaging. Many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. Interestingly, these responses evolved in opposite directions in the somata and distal apical dendrites. These differences between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively.

neuroscience↗