820 research outputs found
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
Computational Models of Timing Mechanisms in the Cerebellar Granular Layer
A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum
Computational roles of cortico-cerebellar loops in temporal credit assignment
Animal survival depends on behavioural adaptation to the environment. This is thought to be enabled by plasticity in the neural circuit. However, the laws which govern neural plasticity are unclear. From a functional aspect, it is desirable to correctly identify, or assign “credit” for, the neurons or synapses responsible for the task decision and subsequent performance. In the biological circuit, the intricate, non-linear interactions involved in neural networks makes appropriately assigning credit to neurons highly challenging. In the temporal domain, this is known as the temporal credit assignment (TCA) problem. This Thesis considers the role the cerebellum – a powerful subcortical structure with strong error-guided plasticity rules – as a solution to TCA in the brain. In particular, I use artificial neural networks as a means to model and understand the mechanisms by which the cerebellum can support learning in the neocortex via the cortico-cerebellar loop. I introduce two distinct but compatible computational models of cortico-cerebellar interaction. The first model asserts that the cerebellum provides the neocortex predictive feedback, modeled in the form of error gradients, with respect to its current activity. This predictive feedback enables better credit assignment in the neocortex and effectively removes the lock between feedforward and feedback processing in cortical networks. This model captures observed long-term deficits associated with cerebellar dysfunction, namely cerebellar dysmetria, in both the motor and non-motor domain. Predictions are also made with respect to alignment of cortico-cerebellar activity during learning and the optimal task conditions for cerebellar contribution. The second model also looks at the role of the cerebellum in learning, but now considers its ability to instantaneously drive the cortex towards desired task dynamics. Unlike the first model, this model does not assume any local cortical plasticity need take place at all and task-directed learning can effectively be outsourced to the cerebellum. This model captures recent optogenetic studies in mice which show the cerebellum as a necessary component for the maintenance of desired cortical dynamics and ensuing behaviour. I also show that this driving input can eventually be used as a teaching signal for the cortical circuit, thereby conceptually unifying the two models. Overall, this Thesis explores the computational role of the cerebellum and cortico-cerebellar loops for task acquisition and maintenance in the brain
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125
This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications,
including image classification, game playing, protein structure analysis,
language translation, and content generation. Despite these powerful
applications, there are still many tasks in our daily life that are rather
simple to humans but pose great challenges to AI. These include image and
language understanding, few-shot learning, abstract concepts, and low-energy
cost computing. Thus, learning from the brain is still a promising way that can
shed light on the development of next-generation AI. The brain is arguably the
only known intelligent machine in the universe, which is the product of
evolution for animals surviving in the natural environment. At the behavior
level, psychology and cognitive sciences have demonstrated that human and
animal brains can execute very intelligent high-level cognitive functions. At
the structure level, cognitive and computational neurosciences have unveiled
that the brain has extremely complicated but elegant network forms to support
its functions. Over years, people are gathering knowledge about the structure
and functions of the brain, and this process is accelerating recently along
with the initiation of giant brain projects worldwide. Here, we argue that the
general principles of brain functions are the most valuable things to inspire
the development of AI. These general principles are the standard rules of the
brain extracting, representing, manipulating, and retrieving information, and
here we call them the first principles of the brain. This paper collects six
such first principles. They are attractor network, criticality, random network,
sparse coding, relational memory, and perceptual learning. On each topic, we
review its biological background, fundamental property, potential application
to AI, and future development.Comment: 59 pages, 5 figures, review articl
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