Cell assemblies are considered to be physiological as well as functional units in the brain. A repetitive and stereotypical sequential activation of many neurons was observed, but the mechanisms underlying it are not well understood. Feedforward networks, such as synfire chains, with the pools of excitatory neurons unidirectionally connected and facilitating signal transmission in a cascade-like fashion were proposed to model such sequential activity. When embedded in a recurrent network, these were shown to destabilise the whole network’s activity, challenging the suitability of the model. Here, we investigate a feedforward chain of excitatory pools enriched by inhibitory pools that provide disynaptic feedforward inhibition. We show that when embedded in a recurrent network of spiking neurons, such an augmented chain is capable of robust signal propagation. We then investigate the influence of overlapping two chains on the signal transmission as well as the stability of the host network. While shared excitatory pools turn out to be detrimental to global stability, inhibitory overlap implicitly realises the motif of lateral inhibition, which, if moderate, maintains the stability but if substantial, it silences the whole network activity including the signal. Addition of a disinhibitory pathway along the chain proves to rescue the signal transmission by transforming a strong inhibitory wave into a disinhibitory one, which specifically guards the excitatory pools from receiving excessive inhibition and thereby allowing them to remain responsive to the forthcoming activation. Disinhibitory circuits not only improve the signal transmission, but can also control it via a gating mechanism. We demonstrate that by manipulating a firing threshold of the disinhibitory neurons, the signal transmission can be enabled or completely blocked. This mechanism corresponds to cholinergic modulation, which was shown to be signalled by volume as well as phasic transmission and variably target classes of neurons. Furthermore, we show that modulation of the feedforward inhibition circuit can promote generating spontaneous replay at the absence of external inputs. This mechanism, however, tends to also cause global instabilities. Overall, these results underscore the importance of inhibitory neuron populations in controlling signal propagation in cell assemblies as well as global stability. Specific inhibitory circuits, when controlled by neuromodulatory systems, can robustly guide or block the signals and invoke replay. This mounts to evidence that the population of interneurons is diverse and can be best categorised by neurons’ specific circuit functions as well as their responsiveness to neuromodulators.

Um dos grandes mistérios da ciência é compreender como sistemas nervosos são capazes de realizar as extraordinárias operações computacionais que realizam. Provavelmente, encéfalos são as estruturas nas quais energia e matéria estão organizadas da forma mais complexa no universo. Central na computação cerebral está o conceito de neurônio. A forma como neurônios computam é motivo de intensa investigação científica. Um consenso atual é que neurônios formam grupos transientes (assembleias) a fim de representar coisas, de realizar operações computacionais, e de executar processos cognitivos; embora os mecanismos que fundamentam a computação por assembleias ainda não seja bem compreendido. Aqui é proposta uma forma pela qual se explica como computação por assembleias pode acontecer. Dois componentes são fundamentais para formação de coalizões neurais: a relação temporal entre grupos de neurônios e o fator de acoplamento entre eles. Assembleias pressupõe neurônios pulsantes; portanto, simulamos computação por assembleias em redes neurais pulsantes. A abordagem usada nesta tese é funcional; apresentamos um arcabouço teórico sobre propriedades, princípios, e dinâmicas que permitem operações computacionais por coalizões neurais. É apresentado na tese que: (i) quando neurônios formam assembleias está implícito que um tipo de função lógica estocástica ocorre, (ii) assembleias podem formar grupos com feedback, criando grupos biestáveis, (iii) grupos biestáveis criam representações internas dos eventos que os criaram, (iv) assembleias podem se ramificar e também dissolver outras assembleias, o que dá origem a algoritmos complexos. Esta é uma investigação inicial sobre computação em assembleias neurais, e há muito a ser feito. Nesta tese apresentamos os conceitos basais para esta nova abordagem. Há um conjunto de programas nos apêndices que permitem ao leitor simular formações de assembleias, ramificações, inibições, reverberações, entre outras propriedades e componentes de nossa proposta. / One of the greatest mysteries in science is to comprehend how brains are capable of realizing the extraordinary computational operations they do. Probably, brains are the structures in which matter and energy are organized in the most complex way in the Universe. Central to the brain computation is the concept of neuron. How neurons compute is motive of intensive scientific investigation. A prevailing consensus is that neurons form transient groups (assemblies) in order to represent things, for realizing computational operations, and for executing cognitive processes; although the mechanisms that substantiate such computation by neural assemblies are not yet well understood. In this thesis we propose a form that explains how neural assembly computation may occur. It is shown that two components are fundamentals for neural coalition formation: the temporal relation among neural groups, and the coupling factor among them. In this sense, neural assemblies presuppose spiking neurons; therefore, here we simulate assembly computing using spiking neural networks. In this thesis it is presented basically a functional approach; thus, it presents a theoretical approach concerning the properties, principles, characteristics, and components that allow the computational operations in neural coalitions. It is presented in the thesis that: (i) as neurons form assemblies it is implicit that a kind of stochastic logic function occurs; (ii) assemblies may form groups that feedback each other, creating bistable groups; (iii) bistable groups internally represent the event that created them; (iv) assemblies may branch and dissolve other assemblies, what give rise to complex algorithms. This is an initial investigation about neural assembly computing and there is a lot to be done; however, in this thesis we present the basal concepts for this new approach. There are programs in the appendices that allow the reader to simulate assembly formation, branching, inhibition, reverberation, among other properties and components in our proposal.

Um dos grandes mistérios da ciência é compreender como sistemas nervosos são capazes de realizar as extraordinárias operações computacionais que realizam. Provavelmente, encéfalos são as estruturas nas quais energia e matéria estão organizadas da forma mais complexa no universo. Central na computação cerebral está o conceito de neurônio. A forma como neurônios computam é motivo de intensa investigação científica. Um consenso atual é que neurônios formam grupos transientes (assembleias) a fim de representar coisas, de realizar operações computacionais, e de executar processos cognitivos; embora os mecanismos que fundamentam a computação por assembleias ainda não seja bem compreendido. Aqui é proposta uma forma pela qual se explica como computação por assembleias pode acontecer. Dois componentes são fundamentais para formação de coalizões neurais: a relação temporal entre grupos de neurônios e o fator de acoplamento entre eles. Assembleias pressupõe neurônios pulsantes; portanto, simulamos computação por assembleias em redes neurais pulsantes. A abordagem usada nesta tese é funcional; apresentamos um arcabouço teórico sobre propriedades, princípios, e dinâmicas que permitem operações computacionais por coalizões neurais. É apresentado na tese que: (i) quando neurônios formam assembleias está implícito que um tipo de função lógica estocástica ocorre, (ii) assembleias podem formar grupos com feedback, criando grupos biestáveis, (iii) grupos biestáveis criam representações internas dos eventos que os criaram, (iv) assembleias podem se ramificar e também dissolver outras assembleias, o que dá origem a algoritmos complexos. Esta é uma investigação inicial sobre computação em assembleias neurais, e há muito a ser feito. Nesta tese apresentamos os conceitos basais para esta nova abordagem. Há um conjunto de programas nos apêndices que permitem ao leitor simular formações de assembleias, ramificações, inibições, reverberações, entre outras propriedades e componentes de nossa proposta. / One of the greatest mysteries in science is to comprehend how brains are capable of realizing the extraordinary computational operations they do. Probably, brains are the structures in which matter and energy are organized in the most complex way in the Universe. Central to the brain computation is the concept of neuron. How neurons compute is motive of intensive scientific investigation. A prevailing consensus is that neurons form transient groups (assemblies) in order to represent things, for realizing computational operations, and for executing cognitive processes; although the mechanisms that substantiate such computation by neural assemblies are not yet well understood. In this thesis we propose a form that explains how neural assembly computation may occur. It is shown that two components are fundamentals for neural coalition formation: the temporal relation among neural groups, and the coupling factor among them. In this sense, neural assemblies presuppose spiking neurons; therefore, here we simulate assembly computing using spiking neural networks. In this thesis it is presented basically a functional approach; thus, it presents a theoretical approach concerning the properties, principles, characteristics, and components that allow the computational operations in neural coalitions. It is presented in the thesis that: (i) as neurons form assemblies it is implicit that a kind of stochastic logic function occurs; (ii) assemblies may form groups that feedback each other, creating bistable groups; (iii) bistable groups internally represent the event that created them; (iv) assemblies may branch and dissolve other assemblies, what give rise to complex algorithms. This is an initial investigation about neural assembly computing and there is a lot to be done; however, in this thesis we present the basal concepts for this new approach. There are programs in the appendices that allow the reader to simulate assembly formation, branching, inhibition, reverberation, among other properties and components in our proposal.

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