A versatile circuit for emulating active biological dendrites applied to
sound localisation and neuron imitation
- URL: http://arxiv.org/abs/2311.12861v1
- Date: Wed, 25 Oct 2023 09:42:24 GMT
- Title: A versatile circuit for emulating active biological dendrites applied to
sound localisation and neuron imitation
- Authors: Daniel John Mannion
- Abstract summary: We introduce a versatile circuit that emulates a segment of a dendrite which exhibits gain, introduces delays, and performs integration.
We also find that dendrites can form bursting neurons.
This significant discovery suggests the potential to fabricate neural networks solely comprised of dendrite circuits.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Sophisticated machine learning struggles to transition onto battery-operated
devices due to the high-power consumption of neural networks. Researchers have
turned to neuromorphic engineering, inspired by biological neural networks, for
more efficient solutions. While previous research focused on artificial neurons
and synapses, an essential component has been overlooked: dendrites. Dendrites
transmit inputs from synapses to the neuron's soma, applying both passive and
active transformations. However, neuromorphic circuits replace these
sophisticated computational channels with metallic interconnects. In this
study, we introduce a versatile circuit that emulates a segment of a dendrite
which exhibits gain, introduces delays, and performs integration. We show how
sound localisation - a biological example of dendritic computation - is not
possible with the existing passive dendrite circuits but can be achieved using
this proposed circuit. We also find that dendrites can form bursting neurons.
This significant discovery suggests the potential to fabricate neural networks
solely comprised of dendrite circuits.
Related papers
- Learning with Chemical versus Electrical Synapses -- Does it Make a
Difference? [61.85704286298537]
Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems.
We conduct experiments with autonomous lane-keeping through a photorealistic autonomous driving simulator to evaluate their performance under diverse conditions.
arXiv Detail & Related papers (2023-11-21T13:07:20Z) - The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron.
Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters.
We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
arXiv Detail & Related papers (2023-06-14T13:34:13Z) - Artificial Dendritic Computation: The case for dendrites in neuromorphic
circuits [0.0]
We investigate the motivation for replicating dendritic computation and present a framework to guide future attempts.
We evaluate the impact of dendrites on an BiLSTM neural network's performance, finding that dendrite pre-processing reduce the size of network required for a threshold performance.
arXiv Detail & Related papers (2023-04-03T13:15:32Z) - 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) - 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) - Phenomenological Model of Superconducting Optoelectronic Loop Neurons [0.0]
Superconducting optoelectronic loop neurons are a class of circuits potentially conducive to networks for large-scale artificial cognition.
To date, all simulations of loop neurons have used first-principles circuit analysis to model the behavior of synapses, dendrites, and neurons.
Here we introduce a modeling framework that captures the behavior of the relevant synaptic, dendritic, and neuronal circuits.
arXiv Detail & Related papers (2022-10-18T16:38:35Z) - Spiking neural network for nonlinear regression [68.8204255655161]
Spiking neural networks carry the potential for a massive reduction in memory and energy consumption.
They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware.
A framework for regression using spiking neural networks is proposed.
arXiv Detail & Related papers (2022-10-06T13:04:45Z) - 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) - IC Neuron: An Efficient Unit to Construct Neural Networks [8.926478245654703]
We propose a new neuron model that can represent more complex distributions.
The Inter-layer collision (IC) neuron divides the input space into multiple subspaces used to represent different linear transformations.
We build the IC networks by integrating the IC neurons into the fully-connected (FC), convolutional, and recurrent structures.
arXiv Detail & Related papers (2020-11-23T08:36:48Z)
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.