77 research outputs found
Neuromorphic Engineering Editors' Pick 2021
This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. AndrĂ© van Schaik and BernabĂ© Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiersâ strong community by recognizing highly deserving authors
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2â7 July 201
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
The dynamics of neural codes in biological and artificial neural networks
Advancing our knowledge of how the brain processes information remains a key
challenge in neuroscience. This thesis combines three different approaches to
the study of the dynamics of neural networks and their encoding
representations: a computational approach, that builds upon basic biological
features of neurons and their networks to construct effective models that can
simulate their structure and dynamics; a machine-learning approach, which draws
a parallel with the functional capabilities of brain networks, allowing us to
infer the dynamical and encoding properties required to solve certain
input-processing tasks; and a final, theoretical treatment, which will take us
into the fascinating hypothesis of the "critical" brain as the mathematical
foundation that can explain the emergent collective properties arising from the
interactions of millions of neurons. Hand in hand with physics, we venture into
the realm of neuroscience to explain the existence of quasi-universal scaling
properties across brain regions, setting out to quantify the distance of their
dynamics from a critical point. Next, we move into the grounds of artificial
intelligence, where the very same theory of critical phenomena will prove very
useful for explaining the effects of biologically-inspired plasticity rules in
the forecasting ability of Reservoir Computers. Halfway into our journey, we
explore the concept of neural representations of external stimuli, unveiling a
surprising link between the dynamical regime of neural networks and the optimal
topological properties of such representation manifolds. The thesis ends with
the singular problem of representational drift in the process of odor encoding
carried out by the olfactory cortex, uncovering the potential synaptic
plasticity mechanisms that could explain this recently observed phenomenon.Comment: A dissertation submitted to the University of Granada in partial
fulfillment of the requirements for the degree of Doctor of Philosoph
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
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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