Activities in the motor cortex are found to be dynamical in nature. Modeling these activities and comparing them with neural recordings helps in understanding the underlying mechanism for the generation of these activities. For this purpose, Recurrent Neural networks or RNNs, have emerged as an appropriate tool. A clear understanding of how the design choices associated with these networks affect the learned dynamics and internal representation still remains elusive. A previous work exploring the dynamical properties of discrete time RNN architectures (LSTM, UGRNN, GRU, and Vanilla) such as the fixed point topology and the linearised dynamics remains invariant when trained on 3 bit Flip- Flop task. In contrast, they show that these networks have unique representational geometry. The goal for this work is to understand if these observations also hold for networks that are more biologically realistic in terms of neural activity. Therefore, we chose to analyze rate networks that have continuous dynamics and biologically realistic connectivity constraints and on spiking neural networks, where the neurons communicate via discrete spikes as observed in the brain. We reproduce the aforementioned study for discrete architectures and then show that the fixed point topology and linearized dynamics remain invariant for the rate networks but the methods are insufficient for finding the fixed points of spiking networks. The representational geometry for the rate networks and spiking networks are found to be different from the discrete architectures but very similar to each other. Although, a small subset of discrete architectures (LSTM) are observed to be close in representation to the rate networks. We show that although these different network architectures with varying degrees of biological realism have individual internal representations, the underlying dynamics while performing the task are universal. We also observe that some discrete networks have close representational similarities with rate networks along with the dynamics. Hence, these discrete networks can be good candidates for reproducing and examining the dynamics of rate networks. / Aktiviteter i motorisk cortex visar sig vara dynamiska till sin natur. Att modellera dessa aktiviteter och jämföra dem med neurala inspelningar hjälper till att förstå den underliggande mekanismen för generering av dessa aktiviteter. För detta ändamål har återkommande neurala nätverk eller RNN uppstått som ett lämpligt verktyg. En tydlig förståelse för hur designvalen associerade med dessa nätverk påverkar den inlärda dynamiken och den interna representationen är fortfarande svårfångad. Ett tidigare arbete som utforskar de dynamiska egenskaperna hos diskreta RNN- arkitekturer (LSTM, UGRNN, GRU och Vanilla), såsom fastpunkts topologi och linjäriserad dynamik, förblir oförändrad när de tränas på 3-bitars Flip- Flop-uppgift. Däremot visar de att dessa nätverk har unik representationsgeometri. Målet för detta arbete är att förstå om dessa observationer också gäller för nätverk som är mer biologiskt realistiska när det gäller neural aktivitet. Därför valde vi att analysera hastighetsnätverk som har kontinuerlig dynamik och biologiskt realistiska anslutningsbegränsningar och på spikande neurala nätverk, där neuronerna kommunicerar via diskreta spikar som observerats i hjärnan. Vi reproducerar den ovannämnda studien för diskreta arkitekturer och visar sedan att fastpunkts topologi och linjäriserad dynamik förblir oförändrad för hastighetsnätverken men metoderna är otillräckliga för att hitta de fasta punkterna för spiknätverk. Representationsgeometrin för hastighetsnätverk och spiknätverk har visat sig skilja sig från de diskreta arkitekturerna men liknar varandra. Även om en liten delmängd av diskreta arkitekturer (LSTM) observeras vara nära i förhållande till hastighetsnäten. Vi visar att även om dessa olika nätverksarkitekturer med varierande grad av biologisk realism har individuella interna representationer, är den underliggande dynamiken under uppgiften universell. Vi observerar också att vissa diskreta nätverk har nära representationslikheter med hastighetsnätverk tillsammans med dynamiken. Följaktligen kan dessa diskreta nätverk vara bra kandidater för att reproducera och undersöka dynamiken i hastighetsnät.

Este trabalho trata sobre a transferência de frequência em neurônios de disparo, especificamente neurônios integra-e-dispara com escoamento e neurônios de Izhikevich. Através de análises matemáticas analíticas e sistemáticas simulações numéricas é obtida a função de ganho, a transferência de frequência estacionária e dinâmica dos neurônios utilizados, para diversos valores dos parâmetros do modelo. Desse modo, são realizados múltiplos ajustes às curvas obtidas, e os coeficientes estimados são apresentados. Com base em todos esses dados, são obtidas diversas características dessas relações de transferência de frequência, e como suas propriedades variam com relação aos principais parâmetros do modelo de neurônio e sinapse utilizados. Diversos resultados interessantes foram apresentados, incluindo evidências de que a função ganho do neurônio integra-e-dispara pode se comportar de modo bastante semelhante à função de ganho e transferência estacionária do neurônio de Izhikevich, dependendo dos parâmetros adotados; a divisão do plano de parâmetros do modelo integra-e-dispara de acordo com a linearidade da transferência de frequência dinâmica; o limiar da intensidade de corrente contínua e de frequência de spikes pré-sinápticos de um neurônio de Izhikevich é determinado apenas pelo parâmetro b, no intervalo de parâmetros usual; modelos de sinapses distintos tendem a não alterar a forma da transferência de frequência estacionária de um neurônio de Izhikevich. / This work is about the frequency transfer of spiking neurons, specifically integrate-and-fire neurons and Izhikevich neurons. Through analytical and systematic numerical simulations the gain function, the stationary and dynamic frequency transfer of the adopted neuron models, are obtained for several values of the model parameters. Thus, multiple fits are made to the curves obtained, and the estimated coefficients are presented. Based on all these data, several characteristics of the frequency transfer relations are obtained, and information is obtained about how their properties vary with respect the parameters of the adopted neuron and synapse model. Several interesting results have been presented, including evidences that the integrate-and-fire neuron\'s gain function can behave quite similarly to the Izhikevich neuron\'s stationary transfer and gain function, depending of the adopted parameters. We also obtained the division of the parameters plane of integrate-and-fire model according to the linearity of the dynamic frequency transfer. It was also verified that the thresholds of the presynaptic spikes\' current intensity and frequency of an Izhikevich neuron are determined only by the parameter b, in the usual parameter range. In addition, it was observed that the considered distinct synapses models tend not to depart from the stationary frequency transfer of an Izhikevich neuron.

I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs.

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One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

S'ha investigat molt tant els mecanismes neuronals bàsics de l'audició com l'organització psicològica de la percepció de la parla. Tanmateix, en ambdós temes n'hi ha una relativa escassetat en quant a modelització. Aquí describim dos treballs de modelització. Un d'ells proposa un nou mecanisme de millora de selectivitat de freqüències que explica resultats de experiments neurofisiològics investigant manifestacions de forward masking y sobretot auditory streaming en l'escorça auditiva principal (A1). El mecanisme funciona en una xarxa feed-forward amb depressió sináptica entre el tàlem y l'escorça, però mostrem que és robust a l'introducció d'una organització realista del circuit de A1, que per la seva banda explica cantitat de dades neurofisiològics. L'altre treball descriu un mecanisme candidat d'explicar la trobada en estudis psicofísics de diferències en la percepció de paraules entre bilinguës primerencs y simultànis. Simulant tasques de decisió lèxica y discriminació de fonemes, fortifiquem l'hipòtesi de que persones sovint exposades a variacions dialectals de paraules poden guardar aquestes en el seu lèxic, sense alterar representacions fonemàtiques . / Though much experimental research exists on both basic neural mechanisms of hearing and the psychological organization of language perception, there is a relative paucity of modelling work on these subjects. Here we describe two modelling efforts. One proposes a novel mechanism of frequency selectivity improvement that accounts for results of neurophysiological experiments investigating manifestations of forward masking and above all auditory streaming in the primary auditory cortex (A1). The mechanism works in a feed-forward network with depressing thalamocortical synapses, but is further showed to be robust to a realistic organization of the neural circuitry in A1, which accounts for a wealth of neurophysiological data. The other effort describes a candidate mechanism for explaining differences in word/non-word perception between early and simultaneous bilinguals found in psychophysical studies. By simulating lexical decision and phoneme discrimination tasks in an attractor neural network model, we strengthen the hypothesis that people often exposed to dialectal word variations can store these in their lexicons, without altering their phoneme representations. / Se ha investigado mucho tanto los mecanismos neuronales básicos de la audición como la organización psicológica de la percepción del habla. Sin embargo, en ambos temas hay una relativa escasez en cuanto a modelización. Aquí describimos dos trabajos de modelización. Uno propone un nuevo mecanismo de mejora de selectividad de frecuencias que explica resultados de experimentos neurofisiológicos investigando manifestaciones de forward masking y sobre todo auditory streaming en la corteza auditiva principal (A1). El mecanismo funciona en una red feed-forward con depresión sináptica entre el tálamo y la corteza, pero mostramos que es robusto a la introducción de una organización realista del circuito de A1, que a su vez explica cantidad de datos neurofisiológicos. El otro trabajo describe un mecanismo candidato de explicar el hallazgo en estudios psicofísicos de diferencias en la percepción de palabras entre bilinguës tempranos y simultáneos. Simulando tareas de decisión léxica y discriminación de fonemas, fortalecemos la hipótesis de que personas expuestas a menudo a variaciones dialectales de palabras pueden guardar éstas en su léxico, sin alterar representaciones fonémicas.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.