The distribution of inhibitory neurons in the C. elegans connectome
facilitates self-optimization of coordinated neural activity
- URL: http://arxiv.org/abs/2010.15272v1
- Date: Wed, 28 Oct 2020 23:11:37 GMT
- Title: The distribution of inhibitory neurons in the C. elegans connectome
facilitates self-optimization of coordinated neural activity
- Authors: Alejandro Morales and Tom Froese
- Abstract summary: The nervous system of the nematode Caenorhabditis elegans exhibits remarkable complexity despite the worm's small size.
A general challenge is to better understand the relationship between neural organization and neural activity at the system level.
We implemented an abstract simulation model of the C. elegans connectome that approximates the neurotransmitter identity of each neuron.
- Score: 78.15296214629433
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The nervous system of the nematode soil worm Caenorhabditis elegans exhibits
remarkable complexity despite the worm's small size. A general challenge is to
better understand the relationship between neural organization and neural
activity at the system level, including the functional roles of inhibitory
connections. Here we implemented an abstract simulation model of the C. elegans
connectome that approximates the neurotransmitter identity of each neuron, and
we explored the functional role of these physiological differences for neural
activity. In particular, we created a Hopfield neural network in which all of
the worm's neurons characterized by inhibitory neurotransmitters are assigned
inhibitory outgoing connections. Then, we created a control condition in which
the same number of inhibitory connections are arbitrarily distributed across
the network. A comparison of these two conditions revealed that the biological
distribution of inhibitory connections facilitates the self-optimization of
coordinated neural activity compared with an arbitrary distribution of
inhibitory connections.
Related papers
- NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes [4.362614418491178]
We introduce the concept of topological detour to characterize how a ubiquitous instance of FC is supported by neural pathways (detour) physically wired by SC.
In the clich'e of machine learning, the multi-hop detour pathway underlying SC-FC coupling allows us to devise a novel multi-head self-attention mechanism.
We propose a biological-inspired deep model, coined as NeuroPath, to find putative connectomic feature representations from the unprecedented amount of neuroimages.
arXiv Detail & Related papers (2024-09-26T03:40:12Z) - Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes [0.7255608805275865]
Brain-state-specific neural mechanisms play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence.
This work aims to provide a two-compartment spiking neuron model that incorporates features essential for supporting brain-state-specific learning.
arXiv Detail & Related papers (2023-11-10T14:16:46Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Self-Evolutionary Reservoir Computer Based on Kuramoto Model [1.7072337666116733]
As a biologically inspired neural network, reservoir computing (RC) has unique advantages in processing information.
We propose a structural autonomous development reservoir computing model (sad-RC), which structure can adapt to the specific problem at hand without any human expert knowledge.
arXiv Detail & Related papers (2023-01-25T15:53:39Z) - Constraints on the design of neuromorphic circuits set by the properties
of neural population codes [61.15277741147157]
In the brain, information is encoded, transmitted and used to inform behaviour.
Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
arXiv Detail & Related papers (2022-12-08T15:16:04Z) - A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for
Caenorhabditis elegans [0.0]
Cross-organism comparison enables a wide variety of research including whole-brain analysis of functional networks.
Recent development of pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach.
The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped.
I propose an alternative neuronal identification technique using only single-color fluorescent images.
arXiv Detail & Related papers (2022-10-24T00:17:26Z) - POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with
Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations.
The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
arXiv Detail & Related papers (2022-01-19T09:26:34Z) - Continuous Learning and Adaptation with Membrane Potential and
Activation Threshold Homeostasis [91.3755431537592]
This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model.
The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input.
Experiments demonstrate the model's ability to adapt to and continually learn from its input.
arXiv Detail & Related papers (2021-04-22T04:01:32Z) - Modeling the Nervous System as An Open Quantum System [4.590533239391236]
We propose a neural network model of multi-neuron interacting system that simulates neurons to interact each other.
We physically model the neuronal cell surroundings, including the dendrites, the axons and the synapses.
We find that this model can generate random neuron-neuron interactions and is proper to describe the process of information transmission in the nervous system physically.
arXiv Detail & Related papers (2021-03-18T10:17:09Z) - And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks.
We define AND-like neurons and propose measures to increase their proportion in the network.
Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
arXiv Detail & Related papers (2021-02-15T08:19:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.