Implementación en VLSI de un modelo de aprendizaje con plasticidad basado en calcio

Descargue el texto completo en el repositorio institucional de la Universität Bielefeld: https://pub.uni-bielefeld.de/record/2919341Los sistemas autónomos deben poder adaptarse a un entorno en constante cambio. Esta adaptabilidad requiere importantes recursos computacionales dedicados al aprendizaje, sin embargo, los sistemas artificiales actuales carecen de estos recursos en comparación con los humanos y los animales. Nuestro objetivo es producir redes neuronales con spikes con VLSI que presenten estructuras de aprendizaje similares a las de la biología con el objetivo de lograr el rendimiento y la eficiencia de los sistemas naturales. La literatura de neurociencia sugiere que los iones de calcio juegan un papel clave en la explicación de la dependencia de la plasticidad sináptica a largo plazo de múltiples factores, como el tiempo de los spikes y la frecuencia de los estímulos. Aquí presentamos una implementación VLSI novedosa de un modelo de plasticidad sináptica basado en calcio, comparaciones entre el modelo y las simulaciones de circuito, y mediciones del circuito fabricado.This thesis aims at the implementation of biologically inspired learning algorithm to be embedded in full-custom VLSI spiking neural networks with the goal of constructing compact real-time low-power learning systems with potential application in computational neuroscience basic research investigation, and applications where input data is ambiguous such as in patter recognition. The starting point of this research is based on recent studies that demonstrated a key role of calcium ions for long term synaptic plasticity. These experimental results have inspired mathematical models and hardware implementations of calcium based learning algorithms. Here I present two prototypes of a novel Very-large-scale Integration (VLSI) implementation of a recently proposed calcium-based learning algorithm, its circuital and computation model simulation results and comparison with the mathematical model. The second improved circuit corrects errors observed in the first chip and it is connected to a low-power neuron in a small array. The elaboration of this learning system embedded in a chip provides insight and significant progress in the complex task to understand how to build brain-like integrated systems. This system can be used also as a tool for validating hypotheses arising from experimental observations of biological systems and computational models.Alemania. Center of Excellence - Cognitive Interaction Technology. Beca CITe

VLSI implementation of a calcium-based plasticity learning model

Maldonado Huayaney FL. VLSI implementation of a calcium-based plasticity learning model. Bielefeld: Universität Bielefeld; 2018.A key feature of autonomous systems is the ability to solve computationally intensive tasks while adapting to changes in the environment; therefore, in these systems learning is needed to predict the responses of the environment to the system actions, thus guiding the system to achieve its goals. However, the learning capabilities required for this feature are underdeveloped in artificial systems, especially when compared to those of humans and animals. Highly-computational processors are embedded in chip technology (i.e. CPU and GPU) which every year uses lower dimension transistors yielding high speed, low leakage power, and low cost per transistor. However, the conventional approach to computation, based on the von Neumann architecture with separate units for information storage and processing, is still outperformed in energy efficiency by biological nervous systems in cognitive tasks, such as classification and prediction, where the input data is characterized by ambiguity and uncertainty. In this sense neuromorphic engineering solves specific tasks which are easily performed by biological systems using computational models discovered in biological organisms and where classical processors' architecture would have difficulties. This thesis aims at the implementation of biologically inspired learning algorithm to be embedded in full-custom VLSI spiking neural networks with the goal of constructing compact real-time low-power learning systems with potential application in computational neuroscience basic research investigation, and applications where input data is ambiguous such as in patter recognition. The starting point of this research is based on recent studies that demonstrated a key role of calcium ions for long term synaptic plasticity. These experimental results have inspired mathematical models and hardware implementations of calcium based learning algorithms. Here I present two prototypes of a novel Very-large-scale Integration (VLSI) implementation of a recently proposed calcium-based learning algorithm, its circuital and computation model simulation results and comparison with the mathematical model. The second improved circuit corrects errors observed in the first chip and it is connected to a low-power neuron in a small array. The elaboration of this learning system embedded in a chip provides insight and significant progress in the complex task to understand how to build brain-like integrated systems. This system can be used also as a tool for validating hypotheses arising from experimental observations of biological systems and computational models

Implementation of a Calcium based plasticity learning model VLSI chip

Maldonado Huayaney FL. Implementation of a Calcium based plasticity learning model VLSI chip. Presented at the 2nd International Symposium and Workshop on Cognitive Neuroscience Robotics, Osaka, Japan

Implementation of a Calcium based plasticity learning model VLSI chip

Maldonado Huayaney FL, Chicca E. Implementation of a Calcium based plasticity learning model VLSI chip. Presented at the 2nd MemoCIS Training School, Alghero, Italy

A VLSI Implementation of a calcium-based plasticity learning model

Maldonado Huayaney FL, Chicca E. A VLSI Implementation of a calcium-based plasticity learning model. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS). Piscataway, NJ: IEEE; 2016: 373-376

Learning in Silicon Beyond STDP: A Neuromorphic Implementation of Multi-Factor Synaptic Plasticity With Calcium-Based Dynamics

Maldonado Huayaney FL, Nease S, Chicca E. Learning in Silicon Beyond STDP: A Neuromorphic Implementation of Multi-Factor Synaptic Plasticity With Calcium-Based Dynamics. IEEE Transactions on Circuits and Systems I: Regular Papers. 2016;63(12):2189-2199.Autonomous systems must be able to adapt to a constantly-changing environment. This adaptability requires significant computational resources devoted to learning, and current artificial systems are lacking in these resources when compared to humans and animals. We aim to produce VLSI spiking neural networks which feature learning structures similar to those in biology, with the goal of achieving the performance and efficiency of natural systems. The neuroscience literature suggests that calcium ions play a key role in explaining long-term synaptic plasticity’s dependence on multiple factors, such as spike timing and stimulus frequency. Here we present a novel VLSI implementation of a calcium-based synaptic plasticity model, comparisons between the model and circuit simulations, and measurements of the fabricated circuit