Biology subjects

Burkitt, A. N.

Publications and source records attributed to Burkitt, A. N..

At least 19 recordsLinked to original sources

bioRxiv2026

Characterising cortical perturbation-recovery dynamics using TMS-EEG: The Brain Dynamic Toolbox

Objective: To adapt dynamical measures used in computational and experimental perturbation models to human transcranial magnetic stimulation and electroencephalography (TMS-EEG) and determine whether analysis of the extended post-stimulation trajectory reveals additional features of the cortical response to long-interval intracortical inhibition (LICI). Methods: Single pulse and paired pulse LICI TMS-EEG responses from the bilateral dorsolateral prefrontal cortex were re-analysed in 16 healthy adults. At the stimulation site, 75 single-pulse and 75 paired-pulse trials were delivered; paired pulses comprised conditioning and test stimuli separated by 100 ms. Responses from 20 frontocentral EEG channels were analysed from 50-950 ms following TMS using the open source Brain Dynamic Metric Toolbox. Perturbative Integration Latency Index (PILI) indexed cortical resilience via response energy and recovery, Autocorrelation Function Width (ACFW) captured temporal memory, and Lyapunov Exponent (LE) indicated the dynamical stability. Linear mixed-effects models examined temporal, spectral, and time-frequency effects from the measured metrics. Results: LICI altered the temporal evolution of the dynamical measures. PILI distinguished conditions in the earliest post-stimulation window before converging, whereas ACFW differences persisted across a broader early interval. LE showed weaker and less consistent discrimination. Spectral effects were most evident for ACFW, while time-frequency analyses revealed later, frequency-specific LICI effects for PILI and ACFW but not LE. Conclusions: The extended post-stimulation trajectory revealed LICI-related differences in cortical resilience and temporal memory with distinct temporal and spectral profiles, while effects on dynamical stability were less consistent. Significance: The Brain Dynamic Metric Toolbox provides a reproducible framework for quantifying perturbation-recovery dynamics in TMS-EEG and demonstrates that the extended perturbational response contains information about LICI not captured by discrete early response measures.

neuroscience↗

bioRxiv2025

Modelling Predictive Coding in the Primary Visual Cortex (V1): Layer 2/3 Circuits for Prediction Error Computation through Compartmentalized Spiking Neurons

Cortical Layer 2/3 has been consistently implicated as the locus of prediction-error signalling in hierarchical models of cortical sensory processing. However, the circuit mechanisms that generate biologically plausible prediction-error (PE) signals remain elusive. A spiking network model is presented here in which two-compartment excitatory pyramidal neurons interact with three inhibitory subtypes: parvalbumin-expressing (PV), somatostatin-expressing (SOM), and vasoactive-intestinal-peptide-expressing (VIP) interneurons, to compute sign-specific prediction errors (positive and negative PEs). Feedforward input targets the soma, whereas top-down feedback reaches the distal apical dendrite, enabling a local somato-dendritic comparison. A PE emerges whenever the balance between excitation and inhibition is selectively disrupted within one compartment, recruiting either positive-error (PE+) or negative-error (PE-) subpopulations of pyramidal neurons. Unlike prior learning-dependent, rate-based accounts, this fixed-weight spiking circuit shows that bidirectional PE signals (PE+ and PE-) can arise online from compartment-specific balance without any synaptic weight updates. The model reproduces key experimental observations, including sparse mismatch responses, compartment-specific inhibition, and VIP-mediated disinhibition. Across four canonical sensory-prediction configurations, the circuit maintains a tight balance during matched input and generates bidirectional PE signals only under mismatch. By routing sensory drive from Layer 4 into Layer 2/3 and allowing the resulting PE activity to project toward deeper feedback generators, the model situates Layer 2/3 as a dedicated, feature-specific mismatch detector within a hierarchical inference network. These results provide a mechanistic bridge from dendritic computation to laminar predictive coding, demonstrating how realistic spiking dynamics can implement fast, sign-specific PE signaling without learning. Author summaryIn this study, we present a biologically grounded spiking model of layer 2/3 in primary visual cortex within the predictive coding framework. Our goal is to explain how superficial cortical circuits compute fast, sign-specific prediction errors when sensory input does not match top-down expectations. The model uses two-compartment pyramidal neurons whose somata receive feedforward drive from layer 4 while apical dendrites receive feedback, together with three key inhibitory interneuron classes, parvalbumin-expressing (PV), somatostatin-expressing (SOM), and vasoactive-intestinal-peptide-expressing (VIP), that provide compartment-specific inhibition and disinhibition. When input and prediction match, excitation and inhibition remain tightly balanced and activity is sparse; when they differ, this balance is transiently broken in the appropriate compartment, and distinct populations signal either a positive error (unexpected presence) or a negative error (unexpected absence). The circuit reproduces several in vivo observations in layer 2/3, including sparse mismatch responses, compartment-specific inhibition, and VIP-mediated disinhibition, and it does so without requiring synaptic weight changes. By routing feature-selective signals from layer 4 into layer 2/3 and relaying the resulting errors toward deeper layers, the model positions layer 2/3 as a local, feature-specific mismatch detector in a hierarchical system. This work provides a concrete, testable mechanism linking dendritic computation, inhibitory diversity, and predictive coding in the cortex.

neuroscience↗

bioRxiv2025

Modelling Predictive Coding in the Primary Visual Cortex (V1): Layer 4 Receptive Field Properties in a Balanced Recurrent Spiking Neuronal Network

Understanding how the cortex encodes sensory input in a biologically efficient and computationally robust manner remains a central question in neuroscience. Predictive coding offers a compelling theoretical framework for such cortical processing, but existing models lack the biological detail to fully explain the function of the cortical microcircuits. This study introduces a spiking neural network model of layer 4 of the primary visual cortex (V1), grounded in predictive coding principles, to clarify how the thalamorecipient layer transforms feedforward input into prediction-error-like signals under realistic excitatory-inhibitory constraints and to yield testable circuit-level predictions. The model integrates structured feedforward input, distinct excitatory and inhibitory populations, and balanced lateral connectivity to simulate spontaneous and stimulus-driven activity. Network responses are systematically examined under spatially unstructured noise input and structured grating stimuli. Neural membrane potentials encode real-time reconstruction errors between external input and internal estimates, with spikes dynamically correcting these mismatches. The network reproduces hallmark in vivo features, including irregular spontaneous activity, sparse and selective responses, and emergent orientation and phase tuning. Excitatory-Inhibitory (E-I) balance was maintained across conditions, with inhibitory neurons exhibiting tighter input coupling than excitatory neurons. Furthermore, the network exhibited contrast-dependent modulation of firing rates and E-I balance, dynamically adjusting its activity to changes in input strength. Decoding analyses demonstrates that structured inputs can be robustly reconstructed under moderate noise levels, although decoding fidelity declines sharply under severe corruption. Together, these results suggest that cortical layer 4 may serve as a structured sensory encoding stage in a hierarchical predictive coding system, providing a biologically grounded foundation for modeling prediction error computations in higher cortical areas. Author summaryIn this Study, we present a biologically grounded spiking neural network model of layer 4 of the primary visual cortex, built within the predictive coding framework. The aim is to better understand how this early cortical layer encodes sensory information while maintaining realistic neural dynamics. Many predictive coding models focus on higher cortical layers 2/3 and overlook layer 4s role. To address this, we develop here a network that integrates structured feedforward input via Gaborfiltered receptive fields, distinct excitatory and inhibitory populations, and fixed lateral connectivity, all adhering to Dales law. The model reproduces several in vivo features observed in layer 4 of the visual cortex, including sparse, irregular spiking, emergent orientation and phase tuning, and contrast-dependent firing. Notably, excitation and inhibition are dynamically balanced across input conditions without requiring synaptic learning. We also show that decoding performance remains robust under moderate noise levels, supporting that layer 4 provides a stable sensory foundation for higher-level prediction. This model offers a biologically realistic implementation of prediction error computation and sets the stage for hierarchical extensions that include feedback and learning. Overall, this work provides insights into how structured sensory representations and balance emerge in cortical microcircuits through architecture alone.

neuroscience↗

bioRxiv2025

Forecasting Seizure Duration from Neural Connectivity Patterns in a Neural Mass Model

Understanding seizure duration is crucial for predicting seizure evolution and developing targeted therapeutic strategies. This study investigates how changes in neural connectivity patterns estimated in a Neural Mass Model (NMM) relate to seizure duration and whether these parameters can be used to forecast seizure duration. We applied a biologically plausible NMM to intracranial EEG recordings from a Tetanus Toxin rat model of epilepsy. Model parameters, representing synaptic connectivity strengths between excitatory and inhibitory neuronal populations across different cortical layers, were estimated using the Unscented Kalman Filter. A Random Forest classifier was trained to predict seizure duration (short vs. long) based on these connectivity parameters. We assessed the classification performance using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Our findings reveal that stronger excitatory-to-excitatory connectivity before seizure onset was associated with longer seizure durations. In contrast, inhibitory-to-inhibitory and excitatory-to-inhibitory connectivity strengths decreased before long seizures. The Random Forest classifier achieved an AUC of 0.91 when using both preictal (1 minute before seizure onset) and early ictal (first 5 seconds of the seizures) parameters, demonstrating excellent predictive power. Classification using only preictal parameters resulted in an AUC of 0.70. Our study highlights the critical role of preictal neural connectivity in determining seizure duration. The findings suggest that network excitability and inhibitory control before seizure onset influence how long seizures persist. These insights provide a foundation for personalized, time-based therapeutic interventions and seizure management strategies, ultimately improving the quality of life of patients with epilepsy.

pathology↗

bioRxiv2024

Emergence of Sparse Coding, Balance and Decorrelation from a Biologically-Grounded Spiking Neural Network Model of Learning in the Primary Visual Cortex

1Many computational studies attempt to address the question of information representation in biological neural networks using an explicit optimization based on an objective function. These approaches begin with principles of information representation that are expected to be found in the network and from which learning rules can be derived. This study approaches the question from the opposite direction; beginning with a model built upon the experimentally observed properties of neural responses, homeostasis, and synaptic plasticity. The known properties of information representation are then expected to emerge from this substrate. A spiking neural model of the primary visual cortex (V1) was investigated. Populations of both inhibitory and excitatory leaky integrate-and-fire neurons with recurrent connections were provided with spiking input from simulated ON and OFF neurons of the lateral geniculate nucleus. This network was provided with natural image stimuli as input. All synapses underwent learning using spike-timing-dependent plasticity learning rules. A homeostatic rule adjusted the weights and thresholds of each neuron based on target homeostatic spiking rates and mean synaptic input values. These experimentally grounded rules resulted in a number of the expected properties of information representation. The network showed a temporally sparse spike response to inputs and this was associated with a sparse code with Gabor-like receptive fields. The network was balanced at both slow and fast time scales; increased excitatory input was balanced by increased inhibition. This balance was associated with decorrelated firing that was observed as population sparseness. This population sparseness was both the cause and result of the decorrelation of receptive fields. These observed emergent properties (balance, temporal sparseness, population sparseness, and decorrelation) indicate that the network is implementing expected principles of information processing: efficient coding, information maximization (infomax), and a lateral or single-layer form of predictive coding. These emergent features of the network were shown to be robust to randomized jitter of the values of key simulation parameters.

neuroscience↗

bioRxiv2024

Cycles in Seizure Duration and Their Underlying Dynamics in the Tetanus Toxin Rat Model

Seizure duration, a characteristic of epilepsy that is understudied in relation in its relationship with rhythmic cycles, provides critical insights into the severity and temporal dynamics of seizures. This study investigates the rhythmic patterns of seizure duration in the Tetanus Toxin (TT) rat model of epilepsy, which is a well-established platform enabling long-term, stable recordings and observation of seizure emergence and remission. Our analysis shows significant cyclical patterns in seizure durations, with periods ranging from 4 to 8 days across rats. The synchronization index and circular-linear correlations revealed phase-locked relationships between seizure durations and cycles, suggesting non-random, predictable temporal dynamics. Further analyses examined the relationship between seizure durations, inter-seizure intervals, and dominant EEG power. The relationship between inter-seizure intervals and seizure duration was modest, suggesting little to no temporal dependency. In contrast, seizure duration showed stronger associations with EEG power in dominant frequency bands, potentially reflecting fluctuations in neural excitability. The findings highlight that seizure durations exhibit predictable rhythms, which could transform seizure prediction and enable time-based intervention strategies, ultimately improving epilepsy management and patient outcomes. These insights lay the groundwork for personalized, rhythm-aware therapeutic approaches.

neuroscience↗

bioRxiv2024

Distinct cortical spatial representations learned along disparate visual pathways

Recent experimental studies have discovered diverse spatial properties, such as head direction tuning and egocentric tuning, of neurons in the postrhinal cortex (POR) and revealed how the POR spatial representation is distinct from the retrosplenial cortex (RSC). However, how these spatial properties of POR neurons emerge is unknown, and the cause of distinct cortical spatial representations is also unclear. Here, we build a learning model of POR based on the pathway from the superior colliculus (SC) that has been shown to have motion processing within the visual input. Our designed SC-POR model demonstrates that diverse spatial properties of POR neurons can emerge from a learning process based on visual input that incorporates motion processing. Moreover, combining SC-POR model with our previously proposed V1-RSC model, we show that distinct cortical spatial representations in POR and RSC can be learnt along disparate visual pathways (originating in SC and V1), suggesting that the varying features encoded in different visual pathways contribute to the distinct spatial properties in downstream cortical areas. Conflict of interest statementThe authors declare no competing financial interests.

neuroscience↗

bioRxiv2024

Reduced Synaptic Heterogeneity in a Tetanus ToxinModel of Epilepsy: Insights from ComputationalModeling

A neural mass model was used to assess connectivity strength across diverse populations by fitting the model to background EEG data obtained from a Tetanus Toxin rat model of epilepsy. Our findings reveal a notable decline in the variability of estimated parameters when using EEG data recorded from rats in the Tetanus Toxin group compared with the control group. A detailed comparison of standard deviations in estimated parameters between day 1 and day 20 recordings, coinciding with a heightened number of seizures, underscores the impact of Tetanus Toxin on diminishing synaptic strength variability across recordings. This study supports electrophysiological studies suggesting that epileptogenesis induces a reduction in biophysical heterogeneity, potentially leading to an increase in network synchrony associated with epilepsy. Furthermore, our computational model establishes a foundation for future explorations of the implications of this diminished variability.

neuroscience↗

bioRxiv2024

Balancing Prior Knowledge and Sensory Data in a Predictive Coding Model: Insights into Coherent Motion Detection in Schizophrenia

This study introduces a biologically plausible computational model based on the predictive coding algorithm, providing insights into motion detection processes and potential deficiencies in schizophrenia. The model decomposes motion structures into individual and shared sources, highlighting a critical role of surround suppression in detecting global motion. This biologically plausible model sheds light on how the brain extracts the structure of motion and comprehends shared or coherent motion within the visual field. The results obtained from random dot stimuli underscore the delicate balance between sensory data and prior knowledge in coherent motion detection. Model testing across varying noise levels reveals longer convergence times with higher noise, consistent with psychophysical experiments showing that response duration (e.g., reaction time or decision-making time) also increases with noise levels. The model suggests that an excessive emphasis on prior knowledge extends the convergence time in motion detection. Conversely, for faster convergence, the model requires a certain level of prior knowledge to prevent excessive disturbance due to noise. These findings contribute to potential explanations for motion detection deficiencies observed in schizophrenia.

neuroscience↗

bioRxiv2024

Neural Dynamics and Seizure Correlations: Insights from Neural Mass Models in a Tetanus Toxin Rat Model of Epilepsy

This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three-layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.

neuroscience↗

bioRxiv2023

Learning spatio-temporal V1 cells from diverse LGN inputs

Since Hubel and Wiesels discovery of simple cells and complex cells in cats primary visual cortex (V1), many experimental studies of V1 cells from animal recordings have shown the spatial and temporal structure of their response properties. Although numerous computational learning models can account for how spatial properties of V1 cells are learnt, how temporal properties emerge through learning is still not well understood. In this study, a learning model based on sparse coding is used to show that spatio-temporal V1 cells, such as biphasic and direction-selective cells, can emerge via synaptic plasticity when diverse spatio-temporal lateral geniculate nucleus (LGN) cells are used as upstream input to V1 cells. We demonstrate that V1 cells with spatial structures and temporal properties (such as a temporal biphasic response and direction selectivity) emerge from a learning process that promotes sparseness while encoding upstream LGN input with spatio-temporal properties. This model provides an explanation for the observed spatio-temporal properties of V1 cells from a learning perspective, enhancing our understanding of how neural circuits learn and process complex visual stimuli.

neuroscience↗

bioRxiv2023

Neural Networks Are Tuned Near Criticality During a Cognitive Task and Distanced from Criticality In a Psychopharmacological Model of Alzheimer's Disease

Dynamical systems exhibit transitions between ordered and disordered states and "criticality" occurs when the system lies at the borderline between these states at which the input is neither strongly damped nor excessively amplified. Impairments in brain function such as dementia or epilepsy could arise from failure of adaptive criticality, and deviation from criticality may be a potential biomarker for cognition-related neurological and psychiatric impairments. Miniscope wide-field calcium imaging of several hundred hippocampal CA1 neurons in freely-behaving mice was studied during rest, a cognitive task of novel object recognition (NOR), and novel object recognition following scopolamine administration that greatly impairs spatial memory encoding. We find that while hippocampal networks exhibit characteristics of a near-critical system at rest, the network activity shifts significantly closer to a critical state when the mice engaged in the NOR task. The dynamics shift away from criticality with impairment of novel object performance due to scopolamine-induced memory impairment. These results support the concept that hippocampal neural networks move closer to criticality when successfully processing increased cognitive load, taking advantage of maximal dynamical range, information content, and transmission that occur in critical regimes.

neuroscience↗

bioRxiv2023

Unifying sparse coding, predictive coding, and divisive normalization

Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. Sparse coding and predictive coding can be reconciled into a learning framework with predictive structure and sparse responses, termed as sparse/predictive coding. However, how sparse/predictive coding (a learning model) is connected with divisive normalization (not a learning model) is still not well investigated. In this paper, we show how sparse coding, predictive coding, and divisive normalization can be described within a unified framework, and illustrate this explicitly within the context of a two-layer neural learning model of sparse/predictive coding. This two-layer model is constructed in a way that implements sparse coding with a network structure that is constructed by implementing predictive coding. We demonstrate how a homeostatic function that regulates neural responses in the model can shape the nonlinearity of neural responses in a way that replicates different forms of divisive normalization. Simulations show that the model can learn simple cells in the primary visual cortex with the property of contrast saturation, which has previously been explained by divisive normalization. In summary, the study demonstrates that the three principles of sparse coding, predictive coding, and divisive normalization can be connected to provide a learning framework based on biophysical properties, such as Hebbian learning and homeostasis, and this framework incorporates both learning and more diverse response nonlinearities observed experimentally. This framework has the potential to also be used to explain how the brain learns to integrate input from different sensory modalities. Author SummaryComputational principles are often proposed to reveal the neural computations underlying brain functions. In the past three decades, sparse coding, predictive coding and divisive normalization have been three influential computational principles that have much success in different areas of neuroscience. Sparse coding offers insights into how the brain learns meaningful associations based on the hypothesis of brain being very efficient. With an emphasis on prediction, predictive coding provides an appealing hierarchical framework of only sending prediction errors to higher layers. Divisive normalization is a mathematical equation designed to account for the extensive nonlinearities in the brain. All these three computational principles along their variants have greatly improved our understanding of the underlying mechanism of the brain. Though connection between sparse and predictive coding has been studied previously, how sparse/predictive coding is connected to a seemingly different principle, divisive normalization, to provide a unified understanding of the brain is still unclear. In this paper, we show that sparse coding, predictive coding and divisive normalization can be connected from first principles. We propose a learning framework that is based on the hypothesis of efficiency, implemented with a predictive structure and displays response nonlinearities of divisive normalization. This framework can be potentially examined and used in a broader context such as multi-sensory integration.

neuroscience↗

bioRxiv2022

Critical dynamics arise during structured information presentation: analysis of embodied in vitro neuronal networks

AO_SCPLOWBSTRACTC_SCPLOWAmongst the characteristics about information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations have precluded a definite answer about when and why neural criticality arises. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of Pong. We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings have compelling implications for the role of neural criticality.

neuroscience↗

bioRxiv2022

A neural framework for spontaneous development of orientation selectivity in the primary visual cortex

The mechanism or microcircuitry behind orientation selectivity in primary visual cortex (V1), and the means by which it develops without supervision or visual input, both remain unresolved questions. Work on the developmental question has assumed the prevalent spatial convergence model of orientation selectivity as the target mechanism. Encouraged by growing evidence challenging both the completeness of this model and its developmental viability, we investigated an alternative scheme. Accordingly, we demonstrate computationally how a scheme in which orientation selectivity originates from the orientation biases already in the retina and lateral geniculate nucelus (LGN) can answer both the mechanistic and developmental questions. In this scheme, the divergence of outputs from the retina allows retinal spontaneous activity to create correlations within the LGN. These correlations in turn allow a Hebbian plasticity mechanism to strengthen those LGN inputs to V1 which carry similar orientation biases and thus provide an orientation tuned excitatory input.

neuroscience↗

bioRxiv2022

Quantifying Visual Acuity for Pre-Clinical Testing of Visual Prostheses.

Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity. This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provide a quantified level of visual perception in the clinical Snellen and MAR scales. The measure was accurate in providing an estimate of the perceivable feature sizes in scaled standardized "C" and "E" optotypes. The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different phosphene sizes and electrode pitch configurations. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in these circumstances. Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.

bioengineering↗

bioRxiv2022

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animals point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas including the grid cells in entorhinal cortex and place cells in the hippocampus.

neuroscience↗

bioRxiv2021

Learning spatio-temporal properties of hippocampal place cells

Hippocampal place cells have spatio-temporal properties: they generally respond to a single spatial location of a small environment; in addition, they also display the temporal response property of theta phase precession, namely that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in layer II of the medial entorhinal cortex (MEC) also have spatio-temporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Other non-grid spatial cells are also abundant in the entorhinal cortex (EC). Because the EC is the upstream area that projects strongly to the hippocampus, a number of EC-hippocampus models have been proposed to explain how the spatial receptive field properties of place cells emerge. However, none of these learning models have explained how the temporal response properties of hippocampal place cells emerge as a result of the EC input. A learning model is presented here based on non-negative sparse coding in which we show that the spatial and temporal properties of hippocampal place cells can be simultaneously learnt from EC input: both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells while MEC grid cells predominantly determine the temporal response properties of hippocampal place cells.

neuroscience↗