Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code

Slow Inhibition and Inhibitory Recruitment in the Hippocampal Dentate Gyrus

L’hippocampe joue un rôle central dans la navigation spatiale, la mémoire et l’organisation spatio-temporelle des souvenirs. Ces fonctions sont maintenues par la capacité du gyrus denté (GD) de séparation des patrons d'activité neuronales. Le GD est situé à l’entrée de la formation hippocampique où il reconnaît la présence de nouveaux motifs parmi la densité de signaux afférant arrivant par la voie entorhinale (voie perforante). Le codage parcimonieux est la marque distinctive du GD. Ce type de codage est le résultat de la faible excitabilité intrinsèque des cellules granulaires (CGs) en combinaison avec une inhibition locale prédominante. En particulier, l’inhibition de type « feedforward » ou circuit inhibiteur antérograde, est engagée par la voie perforante en même temps que les CGs. Ainsi les interneurones du circuit antérograde fournissent des signaux GABAergique aux CGs de manière presque simultanée qu’elles reçoivent les signaux glutamatergiques. Cette thèse est centrée sur l’étude des interactions entre ces signaux excitateurs de la voie entorhinale et les signaux inhibiteurs provenant des interneurones résidant dans le GD et ceci dans le contexte du codage parcimonieux et le patron de décharge en rafale caractéristique des cellules granulaires. Nous avons adressé les relations entre les projections entorhinales et le réseau inhibitoire antérograde du GD en faisant des enregistrements électrophysiologiques des CG pendant que la voie perforante est stimulée de manière électrique ou optogénétique. Nous avons découvert un nouvel mécanisme d’inhibition qui apparait à délais dans les CGs suite à une stimulation dans les fréquences gamma. Ce mécanisme induit une hyperpolarisation de longue durée (HLD) et d’une amplitude prononce. Cette longue hyperpolarisation est particulièrement prolongée et dépasse la durée d’autres types d’inhibition transitoire lente décrits chez les CGs. L’induction de HLD crée une fenêtre temporaire de faible excitabilité suite à laquelle le patron de décharge des CGs et l’intégration d’autres signaux excitateurs sont altérés de manière transitoire. Nous avons donc conclu que l’activité inhibitrice antérograde joue un rôle central dans les processus de codage dans le GD. Cependant, alors qu’il existe une multitude d’études décrivant les interneurones qui font partie de ce circuit inhibiteur, la question de comment ces cellules sont recrutées par la voie entorhinale reste quelque peu explorée. Pour apprendre plus à ce sujet, nous avons enregistré des interneurones résidant iii dans la couche moléculaire du GD tout en stimulant la voie perforante de manière optogénétique. Cette méthode de stimulation nous a permis d’induire la libération de glutamate endogène des terminales entorhinales et ainsi d’observer le recrutement purement synaptique d’interneurones. De manière surprenante, les résultats de cette expérience démontrent un faible taux d’activation des interneurones, accompagné d’un tout aussi faible nombre total de potentiels d’action émis en réponse à la stimulation même à haute fréquence. Ce constat semble contre-intuitif étant donné qu’en générale on assume qu’une forte activité inhibitrice est requise pour le maintien du codage parcimonieux. Tout de même, l’analyse des patrons de décharge des interneurones qui ont été activés a fait ressortir la prééminence de trois grands types: décharge précoce, retardée ou régulière par rapport le début des pulses lumineux. Les résultats obtenus durant cette thèse mettent la lumière sur l’important conséquences fonctionnelles des interactions synaptique et polysynaptique de nature transitoire dans les réseaux neuronaux. Nous aimerions aussi souligner l’effet prononcé de l’inhibition à court terme du type prolongée sur l’excitabilité des neurones et leurs capacités d’émettre des potentiels d’action. De plus que cet effet est encore plus prononcé dans le cas de HLD dont la durée dépasse souvent la seconde et altère l’intégration d’autres signaux arrivants simultanément. Donc on croit que les effets de HLD se traduisent au niveau du réseaux neuronal du GD comme une composante cruciale pour le codage parcimonieux. En effet, ce type de codage semble être la marque distinctive de cette région étant donné que nous avons aussi observé un faible niveau d’activation chez les interneurones. Cependant, le manque d’activité accrue du réseau inhibiteur antérograde peut être compensé par le maintien d’un gradient GABAergique constant à travers le GD via l’alternance des trois modes de décharges des interneurones. En conclusion, il semble que le codage parcimonieux dans le GD peut être préservé même en absence d’activité soutenue du réseau inhibiteur antérograde et ceci grâce à des mécanismes alternatives d’inhibition prolongée à court terme.The hippocampus is implicated in spatial navigation, the generation and recall of memories, as well as their spatio-temporal organization. These functions are supported by the processes of pattern separation that occurs in the dentate gyrus (DG). Situated at the entry of the hippocampal formation, the DG is well placed to detect and sort novelty patterns amongst the high-density excitatory signals that arrive via the entorhinal cortex (EC). A hallmark of the DG is sparse encoding that is enabled by a combination of low intrinsic excitability of the principal cells and local inhibition. Feedforward inhibition (FFI) is recruited directly by the EC and simultaneously with the granule cells (GCs). Therefore, FFI provides fast GABA release and shapes input integration at the millisecond time scale. This thesis aimed to investigate the interplay of entorhinal excitatory signals with GCs and interneurons, from the FFI in the DG, in the framework of sparse encoding and GC’s characteristic burst firing. We addressed the long-range excitation – local inhibitory network interactions using electrophysiological recordings of GCs – while applying an electrical or optogenetic stimulation of the perforant path (PP) in the DG. We discovered and described a novel delayed-onset inhibitory post synaptic potential (IPSP) in GCs, following PP stimulation in the gamma frequency range. Most importantly, the IPSP was characterized by a large amplitude and prolonged decay, outlasting previously described slow inhibitory events in GCs. The long-lasting hyperpolarization (LLH) caused by the slow IPSPs generates a low excitability time window, alters the GCs firing pattern, and interferes with other stimuli that arrive simultaneously. FFI is therefore a key player in the computational processes that occurs in the DG. However, while many studies have been dedicated to the description of the various types of the interneurons from the FFI, the question of how these cells are synaptically recruited by the EC remains not entirely elucidated. We tackled this problem by recording from interneurons in the DG molecular layer during PP-specific optogenetic stimulation. Light-driven activation of the EC terminals enabled a purely synaptic recruitment of interneurons via endogenous glutamate release. We found that this method of stimulation recruits only a subset of interneurons. In addition, the total number of action potentials (AP) was surprisingly low even at high frequency stimulation. This result is counterintuitive, as strong and persistent inhibitory signals are assumed to restrict GC v activation and maintain sparseness. However, amongst the early firing interneurons, late and regular spiking patterns were clearly distinguishable. Interestingly, some interneurons expressed LLH similar to the GCs, arguing that it could be a commonly used mechanism for regulation of excitability across the hippocampal network. In summary, we show that slow inhibition can result in a prolonged hyperpolarization that significantly alters concurrent input’s integration. We believe that these interactions contribute to important computational processes such as sparse encoding. Interestingly, sparseness seems to be the hallmark of the DG, as we observed a rather low activation of the interneuron network as well. However, the alternating firing of ML-INs could compensate the lack of persistent activity by the continuous GABA release across the DG. Taken together these results offer an insight into a mechanism of feedforward inhibition serving as a sparse neural code generator in the DG

Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages

Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (approximately hundreds of milliseconds) and strong (~50 mV from rest) depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium or by NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate from the dendritic to the somatic compartment via a long plateau-conductance. We show principled differences in the way dendritic vs. somatic inhibition controls spike timing, and demonstrate how this could implement a mechanism of spike time control in the face of barrages of synaptic inputs

Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers

This PhD thesis is focused on the central idea that single neurons in the brain should be regarded as temporally precise and highly complex spatio-temporal pattern recognizers. This is opposed to the prevalent view of biological neurons as simple and mainly spatial pattern recognizers by most neuroscientists today. In this thesis, I will attempt to demonstrate that this is an important distinction, predominantly because the above-mentioned computational properties of single neurons have far-reaching implications with respect to the various brain circuits that neurons compose, and on how information is encoded by neuronal activity in the brain. Namely, that these particular "low-level" details at the single neuron level have substantial system-wide ramifications. In the introduction we will highlight the main components that comprise a neural microcircuit that can perform useful computations and illustrate the inter-dependence of these components from a system perspective. In chapter 1 we discuss the great complexity of the spatio-temporal input-output relationship of cortical neurons that are the result of morphological structure and biophysical properties of the neuron. In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific spatio-temporal input patterns with a very simple biologically plausible learning rule. In chapter 3, we use the differentiable deep network analog of a realistic cortical neuron as a tool to approximate the gradient of the output of the neuron with respect to its input and use this capability in an attempt to teach the neuron to perform nonlinear XOR operation. In chapter 4 we expand chapter 3 to describe extension of our ideas to neuronal networks composed of many realistic biological spiking neurons that represent either small microcircuits or entire brain regions

Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It’s about Time

It was recently shown that multiple excitatory inputs to CA1 pyramidal neuron dendrites must be activated nearly simultaneously to generate local dendritic spikes and supralinear responses at the soma; even slight input desynchronization prevented local spike initiation (Gasparini and Magee, 2006; Losonczy and Magee, 2006). This led to the conjecture that CA1 pyramidal neurons may only express their non-linear integrative capabilities during the highly synchronized sharp waves and ripples that occur during slow wave sleep and resting/consummatory behavior, whereas during active exploration and REM sleep (theta rhythm), inadequate synchronization of excitation would lead CA1 pyramidal cells to function as essentially linear devices. Using a detailed single neuron model, we replicated the experimentally observed synchronization effect for brief inputs mimicking single synaptic release events. When synapses were driven instead by double pulses, more representative of the bursty inputs that occur in vivo, we found that the tolerance for input desynchronization was increased by more than an order of magnitude. The effect depended mainly on paired-pulse facilitation of NMDA receptor-mediated responses at Schaffer collateral synapses. Our results suggest that CA1 pyramidal cells could function as non-linear integrative units in all major hippocampal states

Improving Associative Memory in a Network of Spiking Neurons

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory

Role of the hippocampus in goal representation : Insights from behavioural and electrophysiological approaches

The hippocampus plays an important role in spatial cognition, as supported by the location-specific firing of hippocampal place cells. In random foraging tasks, each place cell fires at a specific position (‘place field’) while other hippocampal pyramidal neurons remain silent. A recent study evidenced a reliable extra-field activity in most CA1 place cells of rats waiting for reward delivery in an uncued goal zone. While the location-specific activity of place cells is thought to underlie a flexible representation of space, the nature of this goal-related signal remains unclear. To test whether hippocampal goal-related activity reflects a representation of goal location or a reward-related signal, we designed a two-goal navigation task in which rats were free to choose between two uncued spatial goals to receive a reward. The magnitude of reward associated to each goal zone was modulated, therefore changing the goal value. We recorded CA1 and CA3 unit activity from rats performing this task. Behaviourally, rats were able to remember each goal location and flexibly adapt their choices to goal values. Electrophysiological data showed that a large majority of CA1-CA3 place and silent cells expressed goal-related activity. This activity was independent from goal value and rats’ behavioural choices. Importantly, a large proportion of cells expressed a goal-related activity at one goal zone only. Altogether, our findings suggest that the hippocampus processes and stores relevant information about the spatial characteristics of the goal. This goal representation could be used in cooperation with structures involved in decision-making to optimise goal-directed navigation