Publications

Abstract

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modelled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding, and we show that it solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod neuron can be used efficiently in simulations of larger cortical circuits.

Abstract

Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

Abstract

Sentence production is the process we use to create language-specific sentences that convey particular meanings. In production, there are complex interactions between meaning, words, and syntax at different points in sentences. Computational models can make these interactions explicit and connectionist learning algorithms have been useful for building such models. Connectionist models use domaingeneral mechanisms to learn internal representations and these mechanisms can also explain evidence of long-term syntactic adaptation in adult speakers. This paper will review work showing that these models can generalize words in novel ways and learn typologically-different languages like English and Japanese. It will also present modeling work which shows that connectionist learning algorithms can account for complex sentence production in children and adult production phenomena like structural priming, heavy NP shift, and conceptual/lexical accessibility.

Abstract

Relative clauses are a syntactic device to create complex sentences and they make language structurally productive. Despite a considerable number of experimental studies, it is still largely unclear how children learn relative clauses and how these are processed in the language system. Researchers at the MPI for Psycholinguistics used a computational learning model to gain novel insights into these issues. The model explains the differential development of relative clauses in English as well as cross-linguistic differences