Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.    In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels.    The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations. / <p>QC 20170421</p>

Hierarchical clustering is a family of machine learning methods that has many applications, amongst other data science and data mining. This thesis belongs to the research area of brain-like computing and introduces a novel approach to hierarchical clustering using a brain-like recurrent neural network. Attractor networks can cluster samples by converging to the same network state. We modulate the network behaviour by varying a parameter in the activity propagation rule such that the granularity of the resulting clustering is changed. A hierarchical clustering is then created by combining multiple levels of granularity. The method is developed for two different datasets and evaluated on a variety of clustering metrics. Its performance is compared to standard clustering algorithms and the structure and composition of the clustering is inspected. We show that the method can produce clusterings for different levels of granularity and new data without retraining. As a novel clustering method, it is relevant to machine learning applications. As a model for hierarchical recall in a memory model, it is relevant to computational neuroscience and neuromorphic computing. / Hierarkiskt klusterarbete är en grupp av maskininlärningsmetoder som har många tillämpningar, bland annat datavetenskap och datagrävning. Denna avhandling tillhör forskningsområdet för hjärnlikt databehandling och introducerar ett nytt tillvägagångssätt för hierarkiskt klusterarbete med hjälp av ett hjärnlikt återkommande neuronnätverk. Attraktornätverk kan klustra prover genom att konvergera till samma nätverksstadium. Vi modulerar nätverkets beteende genom att variera en parameter i regeln för aktivitetspropagering så att granulariteten i det resulterande klusterarbetet förändras. Ett hierarkiskt klusterarbete skapas sedan genom att kombinera flera nivåer av granularitet. Metoden utvecklas för två olika datasets och utvärderas med hjälp av olika klustringsmått. Dess prestanda jämförs med standard klusteringsalgoritmer och strukturen och sammansättningen av klusterarbetet inspekteras. Vi visar att metoden kan producera klusterarbeten för olika nivåer av granularitet och nya data utan omträning. Som en ny klusteringsmetod är den relevant för maskininlärningsapplikationer. Som en modell för hierarkisk återkallelse i en minnesmodell är den relevant för beräkningsneurovetenskap och neuromorfisk databehandling.

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.

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.

Die sequenzielle Aktivität von Ortszellen im Hippocampus entspricht vielfach früheren Erlebnissen, was auf eine Rolle in Gedächtnisprozessen hinweist. Jüngere experimentelle Befunde zeigen allerdings, dass Zielorte in sequenzieller Aktivität überrepräsentiert sind. Dies legt eine Rolle dieser Aktivitätsmuster in der Verhaltensplanung nahe, wobei ein detailliertes Verständnis sowohl des Ursprungs als auch der Funktion von Ortszellsequenzen im Hippocampus bislang fehlt. Insbesondere ist nicht bekannt, welcher Mechanismus solche Sequenzen auf adaptive und konstruktive Weise generiert, wodurch effizientes Planen ermöglicht würde. Um der Beantwortung dieser Fragen näher zu kommen, stelle ich ein neu entwickeltes pulscodiertes Netzwerkmodell vor, in dem räumliches Lernen und die Generierung von Sequenzen untrennbar voneinander abhängig sind. Anhand von Simulationen zeige ich, dass dieses Modell die Erzeugung von noch nicht erlebten Sequenztrajektorien in bekannten Umgebungen erklärt, was deren Nutzen für flexible Pfadplanung hervorhebt. Zusätzlich stelle ich die Ergebnisse eines detaillierten Vergleichs zwischen simulierten neuronalen Pulsfolgen und experimentellen Daten auf der Ebene der Populationsdynamik vor. Diese Resultate zeigen, wie sequenzielle räumliche Repräsentationen durch die Interaktion zwischen lokaler oszillatorischer Dynamik und externen Einflüssen geprägt werden.:1. Introduction 2. Neurobiological and theoretical accounts of hippocampal function 3. A computational model of place-cell sequences for goal-finding 4. A statistical note on step size decoding in place-cell sequences 5. Summary and Discussion Bibliography / Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.:1. Introduction 2. Neurobiological and theoretical accounts of hippocampal function 3. A computational model of place-cell sequences for goal-finding 4. A statistical note on step size decoding in place-cell sequences 5. Summary and Discussion Bibliography

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.