Modeling rhythmic patterns in the hippocampus

We investigate different dynamical regimes of neuronal network in the CA3 area of the hippocampus. The proposed neuronal circuit includes two fast- and two slowly-spiking cells which are interconnected by means of dynamical synapses. On the individual level, each neuron is modeled by FitzHugh-Nagumo equations. Three basic rhythmic patterns are observed: gamma-rhythm in which the fast neurons are uniformly spiking, theta-rhythm in which the individual spikes are separated by quiet epochs, and theta/gamma rhythm with repeated patches of spikes. We analyze the influence of asymmetry of synaptic strengths on the synchronization in the network and demonstrate that strong asymmetry reduces the variety of available dynamical states. The model network exhibits multistability; this results in occurrence of hysteresis in dependence on the conductances of individual connections. We show that switching between different rhythmic patterns in the network depends on the degree of synchronization between the slow cells.Comment: 10 pages, 9 figure

Different CA1 and CA3 Representations of Novel Routes in a Shortcut Situation

International audiencePlace cells are hippocampal neurons whose discharge is strongly related to a rat's location in its environment. The existence of place cells has led to the proposal that they are part of an integrated neural system dedicated to spatial navigation. To further understand the relationships between place cell firing and spatial problem solving, we examined the discharge of CA1 and CA3 place cells as rats were exposed to a shortcut in a runway maze. On specific sessions, a wall section of the maze was removed so as to open a shorter novel route within the otherwise familiar maze. We found that the discharge of both CA1 and CA3 cells was strongly affected in the vicinity of the shortcut region but was much less affected farther away. In addition, CA3 fields away from the shortcut were more altered than CA1 fields. Thus, place cell firing appears to reflect more than just the animal's spatial location and may provide additional information about possible motions, or routes, within the environment. This kinematic representation appears to be spatially more extended in CA3 than in CA1, suggesting interesting computational differences between the two subregions

Fast-slow bursters in the unfolding of a high codimension singularity and the ultra-slow transitions of classes

Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a model that, appropriately tuned, can display several types of bursting behaviors. The model contains two subsystems acting at different timescales. For the fast subsystem we use the planar unfolding of a high codimension singularity. In its bifurcation diagram, we locate paths that underly the right sequence of bifurcations necessary for bursting. The slow subsystem steers the fast one back and forth along these paths leading to bursting behavior. The model is able to produce almost all the classes of bursting predicted for systems with a planar fast subsystems. Transitions between classes can be obtained through an ultra-slow modulation of the model's parameters. A detailed exploration of the parameter space allows predicting possible transitions. This provides a single framework to understand the coexistence of diverse bursting patterns in physical and biological systems or in models.Comment: 22 pages, 15 figure

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

Cellular properties of the medial entorhinal cortex as possible mechanisms of spatial processing

Cells of the rodent medial entorhinal cortex (EC) possess cellular properties hypothesized to underlie the spatially periodic firing behaviors of 'grid cells' (GC) observed in vivo. Computational models have simulated experimental GC data, but a consensus as to what mechanism(s) generate GC properties has not been reached. Using whole cell patch-clamp and computational modeling techniques this thesis investigates resonance, rebound spiking and persistent spiking properties of medial EC cells to test potential mechanisms generating GC firing. The first experiment tested the voltage dependence of resonance frequency in layer II medial EC stellate cells. Some GC models use interference between velocity-controlled oscillators to generate GCs. These interference mechanisms work best with a linear relationship between voltage and resonance frequency. Experimental results showed resonance frequency decreased linearly with membrane potential depolarization, suggesting resonance properties could support the generation of GCs. Resonance appeared in medial EC but not lateral EC consistent with location of GCs. The second experiment tested predictions of a recent network model that generates GCs using medial EC stellate cell resonance and rebound spiking properties. Sinusoidal oscillations superimposed with hyperpolarizing currents were delivered to layer II stellate cells. Results showed that relative to the sinusoid, a limited phase range of hyperpolarizing inputs elicited rebound spikes, and the phase range of rebound spikes was even narrower. Tuning model parameters of the stellate cell population to match experimental rebound spiking properties resulted in GC spatial periodicity, suggesting resonance and rebound spiking are viable mechanisms for GC generation. The third experiment tested whether short duration current inputs can induce persistent firing and afterdepolarization in layer V pyramidal cells. During muscarinic acetylcholine receptor activation 1-2 second long current injections have been shown to induce persistent firing in EC principal cells. Persistent firing may underlie working memory performance and has been used to model GCs. However, input stimuli during working memory and navigation may be much shorter than 1-2 seconds. Data showed that input durations of 10, 50 and 100 ms could elicit persistent firing, and revealed time courses and amplitude of afterdepolarization that could contribute to GC firing or maintenance of working memory

Neural ensemble interactions underlying memory consolidation

Episodic memory formation and spatial navigation are core functions of the hippocampus. Embedded in a path integration based navigational system, the hippocampus generates orthogonal codes for different environments. To separate events within the same spatial map, the magnitude of individual place cell firing is modulated by external sensory information. The rate differences are also expressed to separate different running directions within an environment. Previous work suggested that the maps can be perturbed by external cues, but that the rate perturbations are not associatively stored. The present result shows that the rate code is reinstated offline and thus likely associatively stored, which fits well with the theory that describes the hippocampus as generating an index code for episodic memories to assist in retrieval of distributed information stored in the cortex. Lastly, this thesis addresses the methodological challenges of current electrophysiological techniques in detecting excitatory local connectivity on the example of the prefrontal cortex.AI-HS scholarship to CDS and Polaris Award to BL

Two photon interrogation of hippocampal subregions CA1 and CA3 during spatial behaviour

The hippocampus is crucial for spatial navigation and episodic memory formation. Hippocampal place cells exhibit spatially selective activity within an environment and form the neural basis of a cognitive map of space which supports these mnemonic functions. Hebb’s (1949) postulate regarding the creation of cell assemblies is seen as the pre-eminent model of learning in neural systems. Investigating changes to the hippocampal representation of space during an animal’s exploration of its environment provides an opportunity to observe Hebbian learning at the population and single cell level. When exploring new environments animals form spatial memories that are updated with experience and retrieved upon re-exposure to the same environment, but how this is achieved by different subnetworks in hippocampal CA1 and CA3, and how these circuits encode distinct memories of similar objects and events remains unclear. To test these ideas, we developed an experimental strategy and detailed protocols for simultaneously recording from CA1 and CA3 populations with 2P imaging. We also developed a novel all-optical protocol to simultaneously activate and record from ensembles of CA3 neurons. We used these approaches to show that targeted activation of CA3 neurons results in an increasing excitatory amplification seen only in CA3 cells when stimulating other CA3 cells, and not in CA1, perhaps reflecting the greater number of recurrent connections in CA3. To probe hippocampal spatial representations, we titrated input to the network by morphing VR environments during spatial navigation to assess the local CA3 as well as downstream CA1 responses. To this end, we found CA1 and CA3 neural population responses behave nonlinearly, consistent with attractor dynamics associated with the two stored representations. We interpret our findings as supporting classic theories of Hebbian learning and as the beginning of uncovering the relationship between hippocampal neural circuit activity and the computations implemented by their dynamics. Establishing this relationship is paramount to demystifying the neural underpinnings of cognition

Control of clustered action potential firing in a mathematical model of entorhinal cortex stellate cells.

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The entorhinal cortex is a crucial component of our memory and spatial navigation systems and is one of the first areas to be affected in dementias featuring tau pathology, such as Alzheimer's disease and frontotemporal dementia. Electrophysiological recordings from principle cells of medial entorhinal cortex (layer II stellate cells, mEC-SCs) demonstrate a number of key identifying properties including subthreshold oscillations in the theta (4-12 Hz) range and clustered action potential firing. These single cell properties are correlated with network activity such as grid firing and coupling between theta and gamma rhythms, suggesting they are important for spatial memory. As such, experimental models of dementia have revealed disruption of organised dorsoventral gradients in clustered action potential firing. To better understand the mechanisms underpinning these different dynamics, we study a conductance based model of mEC-SCs. We demonstrate that the model, driven by extrinsic noise, can capture quantitative differences in clustered action potential firing patterns recorded from experimental models of tau pathology and healthy animals. The differential equation formulation of our model allows us to perform numerical bifurcation analyses in order to uncover the dynamic mechanisms underlying these patterns. We show that clustered dynamics can be understood as subcritical Hopf/homoclinic bursting in a fast-slow system where the slow sub-system is governed by activation of the persistent sodium current and inactivation of the slow A-type potassium current. In the full system, we demonstrate that clustered firing arises via flip bifurcations as conductance parameters are varied. Our model analyses confirm the experimentally suggested hypothesis that the breakdown of clustered dynamics in disease occurs via increases in AHP conductance.The contribution of MG, KTR and JB was generously supported by a Wellcome Trust Institutional Strategic Support Award (WT105618MA). MG and KT gratefully acknowledge the financial support of the EPSRC via grant EP/N014391/1. LT’s doctoral studentship is supported by the Alzheimer’s Society in partnership with the Garfield Weston Foundation (grant reference 231). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference