Computational aspects of parvalbumin-positive interneuron function

The activity of neurons is dependent on the manner in which they process synaptic inputs from other cells. In the event of clustered synaptic input, neurons can respond in a nonlinear manner through synaptic and dendritic mechanisms. Such mechanisms are well established in principal excitatory neurons throughout the brain, where they increase neuronal computational ability and information storage capacity. In contrast for parvalbumin-positive (PV+) interneurons, the most common cortical class of in- hibitory interneuron, synaptic integration is thought to be either linear or sub-linear in nature, facilitating their role as mediators of precise and fast inhibition. This thesis addresses situations in which PV+ interneurons integrate synaptic inputs in a nonlinear manner, and explores the functions of this synaptic processing. First, I describe a form of cooperative supralinear synaptic integration by local excitatory inputs onto PV+ interneurons, and I extend these results to show how this augments the computational capability of PV+ cells within spiking neuron networks. I also explore the importance of polyamine-modulation of synaptic receptors in mediating sublinear synaptic integration, and discuss how this expands the array of mechanisms known to perform similar functions in PV+ cells. Finally, I present work manipulating PV+ cells experimentally during epilepsy. I consider these findings together with recent scientific advances and suggest how they account for a number of open questions and previously contradictory theories of PV+ interneuron function

Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons

The aim of this work is to introduce modular processing mechanisms for cortical functions implemented in networks of spiking neurons. Neural maps are a feature of cortical processing found to be generic throughout sensory cortical areas, and self-organisation to the fundamental properties of input spike trains has been shown to be an important property of cortical organisation. Additionally, oscillatory behaviour, temporal coding of information, and learning through spike timing dependent plasticity are all frequently observed in the cortex. The traditional self-organising map (SOM) algorithm attempts to capture the computational properties of this cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM using oscillations, phasic coding and STDP has been implemented. This model is capable of mapping to distributions of input data in a manner consistent with the traditional SOM algorithm, and of categorising generic input data sets. Higher-level cortical processing areas appear to feature a hierarchical category structure that is founded on a feature-based object representation. The spiking SOM model is therefore extended to facilitate input patterns in the form of sets of binary feature-object relations, such as those seen in the field of formal concept analysis. It is demonstrated that this extended model is capable of learning to represent the hierarchical conceptual structure of an input data set using the existing learning scheme. Furthermore, manipulations of network parameters allow the level of hierarchy used for either learning or recall to be adjusted, and the network is capable of learning comparable representations when trained with incomplete input patterns. Together these two modules provide related approaches to the generation of both topographic mapping and hierarchical representation of input spaces that can be potentially combined and used as the basis for advanced spiking neuron models of the learning of complex representations

IST Austria Thesis

Distinguishing between similar experiences is achieved by the brain in a process called pattern separation. In the hippocampus, pattern separation reduces the interference of memories and increases the storage capacity by decorrelating similar inputs patterns of neuronal activity into non-overlapping output firing patterns. Winners-take-all (WTA) mechanism is a theoretical model for pattern separation in which a "winner" cell suppresses the activity of the neighboring neurons through feedback inhibition. However, if the network properties of the dentate gyrus support WTA as a biologically conceivable model remains unknown. Here, we showed that the connectivity rules of PV+interneurons and their synaptic properties are optimizedfor efficient pattern separation. We found using multiple whole-cell in vitrorecordings that PV+interneurons mainly connect to granule cells (GC) through lateral inhibition, a form of feedback inhibition in which a GC inhibits other GCs but not itself through the activation of PV+interneurons. Thus, lateral inhibition between GC–PV+interneurons was ~10 times more abundant than recurrent connections. Furthermore, the GC–PV+interneuron connectivity was more spatially confined but less abundant than PV+interneurons–GC connectivity, leading to an asymmetrical distribution of excitatory and inhibitory connectivity. Our network model of the dentate gyrus with incorporated real connectivity rules efficiently decorrelates neuronal activity patterns using WTA as the primary mechanism. This process relied on lateral inhibition, fast-signaling properties of PV+interneurons and the asymmetrical distribution of excitatory and inhibitory connectivity. Finally, we found that silencing the activity of PV+interneurons in vivoleads to acute deficits in discrimination between similar environments, suggesting that PV+interneuron networks are necessary for behavioral relevant computations. Our results demonstrate that PV+interneurons possess unique connectivity and fast signaling properties that confer to the dentate gyrus network properties that allow the emergence of pattern separation. Thus, our results contribute to the knowledge of how specific forms of network organization underlie sophisticated types of information processing

Self-Organization of Spiking Neural Networks for Visual Object Recognition

On one hand, the visual system has the ability to differentiate between very similar objects. On the other hand, we can also recognize the same object in images that vary drastically, due to different viewing angle, distance, or illumination. The ability to recognize the same object under different viewing conditions is called invariant object recognition. Such object recognition capabilities are not immediately available after birth, but are acquired through learning by experience in the visual world. In many viewing situations different views of the same object are seen in a tem- poral sequence, e.g. when we are moving an object in our hands while watching it. This creates temporal correlations between successive retinal projections that can be used to associate different views of the same object. Theorists have therefore pro- posed a synaptic plasticity rule with a built-in memory trace (trace rule). In this dissertation I present spiking neural network models that offer possible explanations for learning of invariant object representations. These models are based on the following hypotheses: 1. Instead of a synaptic trace rule, persistent firing of recurrently connected groups of neurons can serve as a memory trace for invariance learning. 2. Short-range excitatory lateral connections enable learning of self-organizing topographic maps that represent temporal as well as spatial correlations. 3. When trained with sequences of object views, such a network can learn repre- sentations that enable invariant object recognition by clustering different views of the same object within a local neighborhood. 4. Learning of representations for very similar stimuli can be enabled by adaptive inhibitory feedback connections. The study presented in chapter 3.1 details an implementation of a spiking neural network to test the first three hypotheses. This network was tested with stimulus sets that were designed in two feature dimensions to separate the impact of tempo- ral and spatial correlations on learned topographic maps. The emerging topographic maps showed patterns that were dependent on the temporal order of object views during training. Our results show that pooling over local neighborhoods of the to- pographic map enables invariant recognition. Chapter 3.2 focuses on the fourth hypothesis. There we examine how the adaptive feedback inhibition (AFI) can improve the ability of a network to discriminate between very similar patterns. The results show that with AFI learning is faster, and the network learns selective representations for stimuli with higher levels of overlap than without AFI. Results of chapter 3.1 suggest a functional role for topographic object representa- tions that are known to exist in the inferotemporal cortex, and suggests a mechanism for the development of such representations. The AFI model implements one aspect of predictive coding: subtraction of a prediction from the actual input of a system. The successful implementation in a biologically plausible network of spiking neurons shows that predictive coding can play a role in cortical circuits

Synaptic competition in the amygdala : heterosynaptic plasticity between thalamic and cortical projections to the lateral amygdala

Tese de mestrado, Neurociências, Universidade de Lisboa, Faculdade de Medicina, 2018Aprender e a principal propriedade do cerebro atraves da qual, com base na experiencia, os animais se adaptam as caracteristicas do ambiente envolvente. Embora cada evento de aprendizagem possa ser considerado como um novo processo de formacao de memorias, existem evidencias de que as memorias sao formadas sobre uma rede de informacoes pre-existentes. E comumente aceite que as memorias evoluem ao longo do tempo, sendo que, logo apos a aprendizagem, as memorias sao instaveis e suscetiveis de serem modificadas ou perdidas por interferencia de eventos concorrentes. Estas sao chamadas memorias de curta duracao (short-term memories – STM), em que atraves de um processo dependente da sintese de novas proteinas denominado por consolidacao, sao estabilizadas em memorias de longa duracao (long-term memories – LTM). A reativacao das LTM torna-as instaveis pelo que para se tornarem novamente estaveis, necessitam de ser reconsolidadas, atraves de um processo denominado por reconsolidacao. Apos a reativacao de uma memoria existe um processo alternativo, definido como extincao, que resulta de uma exposicao mais prolongada e diminui a expressao da memoria inicial. Assim, apos a reativacao de uma memoria adquirida, a sua expressao pode ser mantida ou aumentada por reconsolidacao, ou reduzida por extincao. Neste contexto, a reativacao de memorias previamente consolidadas, apos a aquisicao de novas memorias, pode resultar na manutencao de uma LTM por mecanismos de cooperacao, ou no seu enfraquecimento por competicao. Os modelos celulares mais aceites que explicam a formacao de memorias traduzem alteracoes na forca das sinapses e consistem em modelos de plasticidade sinaptica. Nestes, formas duradouras de plasticidade de sinaptica, como a potenciacao de longa duracao (long-term potentiation – LTP), tambem sao caracterizadas como tendo diferentes etapas. Mais concretamente, formas transientes de LTP podem serem relacionadas com as STM, e formas persistentes de plasticidade (que sao mais estaveis e dependem de sintese proteica) podem ser relacionadas com as LTM. Esta visao de que formas persistentes de LTP dependem de sintese proteica, e que a inducao de LTP so ocorre em sinapses previamente ativadas, sugere que as sinapses sao marcadas por um sinal molecular local (tag), com o objetivo das proteinas associadas a plasticidade (plasticityrelated proteins – PRPs) serem alocadas. Assim, dado que a plasticidade sináptica depende da alocacao de PRPs e que as sinapses partilham as PRPs disponiveis, para que formas persistentes de plasticidade sejam induzidas, as sinapses terao de cooperar ou competir entre si. Com o intuito de compreender melhor de que forma estes mecanismos de cooperacao e de competicao sinaptica interferem no processo de formacao de memorias, o nosso grupo decidiu estudar estes mecanismos de plasticidade sinaptica na amigdala, mais concretamente na amigdala lateral (lateral amygdala – LA). A LA e as suas vias aferentes talamicas e corticais formam um circuito necessario para a formacao de memorias condicionadas pelo medo (fear conditioning memories). Dado que este circuito se encontra muito bem descrito sob o ponto de vista anatomico e comportamental, e um modelo que permite ligar a fisiologia celular com o comportamento. O principal modelo celular subjacente ao paradigma de condicionamento por medo auditivo (auditory fear conditioning) consiste numa forma de LTP induzida pela associacao entre as projecoes talamicas auditivas e corticais auditivas (estimulo condicionado/ conditioned-stimulus – CS) e o estimulo nociceptivo (estimulo nao condicionado/ unconditioned-stimulus – US). Estudos recentes do nosso grupo demonstraram que as sinapses corticais e talamicas cooperam, resultando na manutencao de formas transientes de LTP por partilha de PRPs entre os dois grupos de sinapses ativadas, e no reforco de ambos os inputs de um modo associativo. Este mecanismo de cooperacao demonstra ser bidirecional e ocorrer dentro de uma janela temporal prolongada. Contudo, esta partilha revela ser assimetrica dado o facto da capacidade das sinapses talamicas capturarem PRPs decair muito mais rapidamente comparativamente com as sinapses corticais. Alem disso, demonstrou-se que a janela temporal da cooperacao talamica e limitada pela ativacao do receptor cannabinoid 1 (CB1), em que a inibicao dos receptores dos endocanabinoides permite estender a janela de cooperacao cortico-talamica. Com a realizacao deste trabalho pretendemos abordar, a nivel celular, de que forma os mecanismos de cooperacao e de competicao contribuem para a formacao e manutencao de memorias, utilizando como modelo de aprendizagem a formacao de memorias de medo associativo (associative fear memories). Assim, o primeiro objetivo deste trabalho consistiu em testar se as sinapses talamicas e corticais interagem por cooperacao sinaptica, e confirmar que este mecanismo e dependente de sintese proteica. Seguidamente, pretendeu-se testar se estas mesmas sinapses interagem por competicao sinaptica, determinar quais as regras temporais desta forma de plasticidade e qual o impacto da modulacao da ativacao dos recetores CB1. Para testar as hipoteses acima referidas, recorremos a tecnica de patch-clamp em current-clamp, na configuracao whole-cell, e registamos potenciais excitatorios pos-sinapticos em neuronios piramidais na LA, desencadeados pela estimulacao dos inputs talamicos e corticais. Numa primeira abordagem, a associacao das vias corticais e talamicas foi testada atraves da coativacao dos inputs talamicos e corticais, por estimulacoes fracas (tetanus fracos). Verificamos que o LTP das duas vias nao se revelou persistente ao longo do registo. De seguida optamos por associar uma estimulacao cortical forte (tetanus forte) sucedida por uma estimulacao talamica fraca, e desta forma confirmamos a existencia de cooperacao sinaptica. Com recurso a um inibidor da sintese proteica, comprovamos que este mecanismo e dependente de sintese proteica. Com a estimulacao de uma projecao talamica adicional (por um tetanus fraco), foi possivel verificar que as sinapses talamicas e corticais competem quando e gerado um desequilibrio entre o numero de sinapses ativadas e a quantidade de PRPs disponiveis. Demonstramos que a competicao sinaptica e modulada pelo tempo, pois o aumento da janela temporal (30-min) da segunda estimulacao talamica diminuiu a competicao sinaptica, e que esta esta relacionada com a disponibilidade reduzida de PRPs. A semelhanca da cooperacao, a ativacao dos receptores CB1 tambem modula a competicao sinaptica. Relativamente a este ultimo aspeto, a inibicao dos receptores CB1 leva a um aumento da competicao; e um aumento da ativacao dos receptores CB1, por aumento da disponibilidade de endocanabinoides, resulta numa diminuicao da competicao. O facto dos receptores CB1 modularem a forca do tag das sinapses talamicas podera explicar estes resultados. Verificamos ainda que tanto a competicao como a cooperacao resultam de um balanco entre a excitacao e a inibicao, uma vez que inibindo os recetores GABAA (Gamma- Amino Butyric Acid) a cooperacao e facilitada. Assim, os nossos resultados demonstram que os inputs corticais e talamicos para a LA podem interagir entre si dentro de determinadas janelas temporais, competindo quando a disponibilidade de PRPs e o numero de sinapses se encontra desequilibrada. Um aspeto interessante e a possivel relacao com a aprendizagem discriminativa, quando um animal aprende a discriminar um CS+/US de uma associacao CS-/US. Nesta situacao, os neuronios piramidais da LA aumentam as suas respostas ao CS+ e mostram uma diminuicao paralela no CS-. Uma das hipoteses baseia-se no facto desta diminuicao da CS-resposta ser resultante de mecanismos de competicao sinaptica que ocorrem durante a aprendizagem. Em concordancia com este mecanismo, o aumento da disponibilidade de PRPs diminui a aprendizagem discriminativa. Estas observacoes geram um grande impacto na estrutura conceptual da aprendizagem de medo associativa (associative fear learning), uma vez que fornecem um mecanismo celular para a integracao continua da informacao nas sinapses da LA. Alem disso, ao trabalhar numa area do cerebro bem caracterizada sob o ponto de vista comportamental, este projeto oferece a possibilidade de integrar informacoes de diferentes niveis de investigacao, conduzindo a uma visao unificadora da formacao de memorias.Learning is the main property of the brain through which, based on experience, animals learn to adapt to the characteristics of the environment. Although each learning event can be considered as a new memory formation process, there is evidence that memories are formed over a network of preexisting information. It is commonly accepted that memories evolve over time, and soon after learning, memories are unstable and susceptible to be modified or lost by interference from competing events. The most accepted cellular models that explain memory formation translate the changes in the strength of the synapses, and consist of models of synaptic plasticity. Longterm potentiation (LTP) requires input-specific allocation of plasticity-related proteins (PRPs) for its maintenance. This view that persistent forms of LTP depend on protein synthesis, as well as the induction of LTP only occurs in previously activated synapses, suggests that these synapses are marked by a local molecular signal (tag), allowing PRPs to be allocated. Thus, since synaptic plasticity depends on the allocation of PRPs, in which synapses share the available PRPs, to induce persistent forms of plasticity synapses will have to cooperate or compete. In order to better understand how synaptic cooperation and competition are orchestrated as well as their implication in memory formation, we have studied the interaction between the cortical and thalamic afferents to projection neurons of the lateral amygdala (LA). This circuit is known to be involved in the formation of fear conditioning memories. The leading cellular model underlying auditory fear conditioning is a form of Hebbian LTP, induced by the association between the auditory thalamic and auditory cortex projections (conditioned-stimulus – CS) and the nociceptive input (unconditioned-stimulus – US). Recent studies from our group have demonstrated that cortical and thalamic synapses cooperate, resulting in the maintenance of transient forms of LTP by sharing PRPs between these groups of activated synapses. In addition, the temporal window for thalamic cooperation is limited by the activation of the cannabinoid 1 (CB1) receptor. The goal of this work was to assess, at the cellular level, how the mechanisms of cooperation and competition contribute to the formation and maintenance of memories, based on associative fear learning. Thus, the first objective of this work was to test whether thalamic and cortical synapses interact through synaptic cooperation, and confirm that this mechanism depends on protein synthesis. Next, we wanted to test if these synapses interact by synaptic competition, uncover what are the temporal rules of this form of plasticity and assess the impact of eCBs receptors activation. We recorded excitatory post-synaptic potentials in pyramidal neurons in the LA, evoked by stimulation of thalamic and cortical inputs. We found that cortical and thalamic synapses can cooperate by sharing PRPs, resulting in the re-enforcement of both inputs. Nevertheless, thalamic and cortical synapses also compete. The stimulation of an additional thalamic projection leads to an unbalance between the number of activated synapses and PRPs availability, resulting in competition. Synaptic competition is modulated by time, whereas extending the time window decreases synaptic competition, and depends on the reduced availability of PRPs. Interestingly, we have further found that both competition and cooperation result from a balance between excitation and inhibition since GABAA receptors blockage enhances cooperation. Activation of the endocannabinoid receptor CB1 (CB1R) also modulates synaptic competition – increased activation of CB1R decreases competition and CB1R blockage enhances competition. Our results show that cortical and thalamic inputs to the LA can interact with each other within large time windows, competing when the availability of PRPs and the number of activated synapses is unbalanced. This observation has a profound impact on the conceptual framework of associative fear learning, as it provides a cellular mechanism for continuous integration of information at amygdala synapses

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

Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons

This document is the Accepted Manuscript version of a published work that appeared in final form in Journal of Neurophysiology after peer review and technical editing by the publisher. Under embargo until 1 July 2018. To access the final edited and published work see: https://doi.org/10.1152/jn.00789.2016.Inhibitory interneurons interconnected via electrical and chemical (GABAA receptor) synapses form extensive circuits in several brain regions. They are thought to be involved in timing and synchronization through fast feedforward control of principal neurons. Theoretical studies have shown, however, that whereas self-inhibition does indeed reduce response duration, lateral inhibition, in contrast, may generate slow response components through a process of gradual disinhibition. Here we simulated a circuit of interneurons (stellate and basket cells) of the molecular layer of the cerebellar cortex and observed circuit time constants that could rise, depending on parameter values, to >1 s. The integration time scaled both with the strength of inhibition, vanishing completely when inhibition was blocked, and with the average connection distance, which determined the balance between lateral and self-inhibition. Electrical synapses could further enhance the integration time by limiting heterogeneity among the interneurons and by introducing a slow capacitive current. The model can explain several observations, such as the slow time course of OFF-beam inhibition, the phase lag of interneurons during vestibular rotation, or the phase lead of Purkinje cells. Interestingly, the interneuron spike trains displayed power that scaled approximately as 1/f at low frequencies. In conclusion, stellate and basket cells in cerebellar cortex, and interneuron circuits in general, may not only provide fast inhibition to principal cells but also act as temporal integrators that build a very short-term memory.NEW & NOTEWORTHY The most common function attributed to inhibitory interneurons is feedforward control of principal neurons. In many brain regions, however, the interneurons are densely interconnected via both chemical and electrical synapses but the function of this coupling is largely unknown. Based on large-scale simulations of an interneuron circuit of cerebellar cortex, we propose that this coupling enhances the integration time constant, and hence the memory trace, of the circuit.Peer reviewe

Homeostatische Plastizität - algorithmische und klinische Konsequenzen

Plasticity supports the remarkable adaptability and robustness of cortical processing. It allows the brain to learn and remember patterns in the sensory world, to refine motor control, to predict and obtain reward, or to recover function after injury. Behind this great flexibility hide a range of plasticity mechanisms, affecting different aspects of neuronal communication. However, little is known about the precise computational roles of some of these mechanisms. Here, we show that the interaction between spike-timing dependent plasticity (STDP), intrinsic plasticity and synaptic scaling enables neurons to learn efficient representations of their inputs. In the context of reward-dependent learning, the same mechanisms allow a neural network to solve a working memory task. Moreover, although we make no any apriori assumptions on the encoding used for representing inputs, the network activity resembles that of brain regions known to be associated with working memory, suggesting that reward-dependent learning may be a central force in working memory development. Lastly, we investigated some of the clinical implications of synaptic scaling and showed that, paradoxically, there are situations in which the very mechanisms that normally are required to preserve the balance of the system, may act as a destabilizing factor and lead to seizures. Our model offers a novel explanation for the increased incidence of seizures following chronic inflammation.Das menschliche Gehirn ist in der Lage sich an dramatische Veränderungen der Umgebung anzupassen. Hinter der Anpassungsfähigkeit des Gehirns stecken verschiedenste ernmechanismen. Einige dieser Mechanismen sind bereits relativ gut erforscht, wahrend bei anderen noch kaum bekannt ist, welche Rolle sie innerhalb der Informationsverarbeitungsprozesse im Gehirn spielen. Hier, soll gezeigt werden, dass das Zusammenspiel von Spike-Timing Dependent Plasticity' (STDP) mit zwei weiteren Prozessen, Synaptic Scaling' und Intrinsic Plasticity' (IP), es Nervenzellen ermöglicht Information effizient zu kodieren. Die gleichen Mechanismen führen dazu, dass ein Netzwerk aus Neuronen in der Lage ist, ein Arbeitsgedächtnis' für vergangene Stimuli zu entwickeln. Durch die Kombination von belohnungsabhängigem STDP und homöostatischen Mechanismen lernt das Netzwerk, die Stimulus-Repräsentationen für mehrere Zeitschritte verfügbar zu halten. Obwohl in unserem Modell-Design keinerlei. Informationen über die bevorzugte Art der Kodierung enthalten sind, finden wir nach Ende des Trainings neuronale Repräsentationen, die denjenigen aus vielen Arbeitsgedächtnis-Experimenten gleichen. Unser Modell zeigt, dass solche Repräsentationen durch Lernen enstehen können und dass Reward-abhängige Prozesse eine zentrale Kraft bei der Entwicklung des Arbeitsgedächtnisses spielen können. Abschliessend werden klinische Konsequenzen einiger Lern-Prozesse untersucht. Wir konnten zeigen, dass der selbe Mechanismus, der normalerweise die Aktivität im Gehirn in Balance hält, in speziellen Situationen auch zu Destabilisierung führen und epileptische Anfälle auslösen kann. Das hier vorgestellte Modell liefert eine neuartige Erklärung zur Entstehung von epileptischen Anfällen bei chronischen Entzündungen

Constraining the function of CA1 in associative memory models of the hippocampus

Institute for Adaptive and Neural ComputationCA1 is the main source of afferents from the hippocampus, but the function of CA1 and its perforant path (PP) input remains unclear. In this thesis, Marr’s model of the hippocampus is used to investigate previously hypothesized functions, and also to investigate some of Marr’s unexplored theoretical ideas. The last part of the thesis explains the excitatory responses to PP activity in vivo, despite inhibitory responses in vitro. Quantitative support for the idea of CA1 as a relay of information from CA3 to the neocortex and subiculum is provided by constraining Marr’s model to experimental data. Using the same approach, the much smaller capacity of the PP input by comparison implies it is not a one-shot learning network. In turn, it is argued that the entorhinal-CA1 connections cannot operate as a short-term memory network through reverberating activity. The PP input to CA1 has been hypothesized to control the activity of CA1 pyramidal cells. Marr suggested an algorithm for self-organising the output activity during pattern storage. Analytic calculations show a greater capacity for self-organised patterns than random patterns for low connectivities and high loads, confirmed in simulations over a broader parameter range. This superior performance is maintained in the absence of complex thresholding mechanisms, normally required to maintain performance levels in the sparsely connected networks. These results provide computational motivation for CA3 to establish patterns of CA1 activity without involvement from the PP input. The recent report of CA1 place cell activity with CA3 lesioned (Brun et al., 2002. Science, 296(5576):2243-6) is investigated using an integrate-and-fire neuron model of the entorhinal-CA1 network. CA1 place field activity is learnt, despite a completely inhibitory response to the stimulation of entorhinal afferents. In the model, this is achieved using N-methyl-D-asparate receptors to mediate a significant proportion of the excitatory response. Place field learning occurs over a broad parameter space. It is proposed that differences between similar contexts are slowly learnt in the PP and as a result are amplified in CA1. This would provide improved spatial memory in similar but different contexts