Synaptic Integration of Spatiotemporal Features with a Dynamic
Neuromorphic Processor
- URL: http://arxiv.org/abs/2002.04924v2
- Date: Tue, 1 Jun 2021 14:05:32 GMT
- Title: Synaptic Integration of Spatiotemporal Features with a Dynamic
Neuromorphic Processor
- Authors: Mattias Nilsson, Foteini Liwicki and Fredrik Sandin
- Abstract summary: We show that a single point-neuron with dynamic synapses in the DYNAP-SENAP can respond selectively to presynaptic spikes with a particulartemporal structure.
This structure enables, for instance, visual feature tuning of single neurons.
- Score: 0.1529342790344802
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spiking neurons can perform spatiotemporal feature detection by nonlinear
synaptic and dendritic integration of presynaptic spike patterns.
Multicompartment models of non-linear dendrites and related neuromorphic
circuit designs enable faithful imitation of such dynamic integration
processes, but these approaches are also associated with a relatively high
computing cost or circuit size. Here, we investigate synaptic integration of
spatiotemporal spike patterns with multiple dynamic synapses on point-neurons
in the DYNAP-SE neuromorphic processor, which offers a complementary
resource-efficient, albeit less flexible, approach to feature detection. We
investigate how previously proposed excitatory--inhibitory pairs of dynamic
synapses can be combined to integrate multiple inputs, and we generalize that
concept to a case in which one inhibitory synapse is combined with multiple
excitatory synapses. We characterize the resulting delayed excitatory
postsynaptic potentials (EPSPs) by measuring and analyzing the membrane
potentials of the neuromorphic neuronal circuits. We find that biologically
relevant EPSP delays, with variability of order 10 milliseconds per neuron, can
be realized in the proposed manner by selecting different synapse combinations,
thanks to device mismatch. Based on these results, we demonstrate that a single
point-neuron with dynamic synapses in the DYNAP-SE can respond selectively to
presynaptic spikes with a particular spatiotemporal structure, which enables,
for instance, visual feature tuning of single neurons.
Related papers
- Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
We introduce Artificial Kuramotoy Neurons (AKOrN) as a dynamical alternative to threshold units.
We show that this idea provides performance improvements across a wide spectrum of tasks.
We believe that these empirical results show the importance of our assumptions at the most basic neuronal level of neural representation.
arXiv Detail & Related papers (2024-10-17T17:47:54Z) - Single Neuromorphic Memristor closely Emulates Multiple Synaptic
Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions.
These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
arXiv Detail & Related papers (2024-02-26T15:01:54Z) - Astrocytes as a mechanism for meta-plasticity and contextually-guided
network function [2.66269503676104]
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell.
Astrocytes may play a more direct and active role in brain function and neural computation.
arXiv Detail & Related papers (2023-11-06T20:31:01Z) - Inferring Relational Potentials in Interacting Systems [56.498417950856904]
We propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions.
NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed.
It allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting.
arXiv Detail & Related papers (2023-10-23T00:44:17Z) - 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) - Understanding Neural Coding on Latent Manifolds by Sharing Features and
Dividing Ensembles [3.625425081454343]
Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity.
These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity.
We propose feature sharing across neural tuning curves, which significantly improves performance and leads to better-behaved optimization.
arXiv Detail & Related papers (2022-10-06T18:37:49Z) - An accurate and flexible analog emulation of AdEx neuron dynamics in
silicon [0.0]
This manuscript presents the analog neuron circuits of the mixed-signal accelerated neuromorphic system BrainScaleS-2.
They are capable of flexibly and accurately emulating the adaptive exponential integrate-and-fire model equations in combination with both current- and conductance-based synapses.
arXiv Detail & Related papers (2022-09-19T18:08:23Z) - Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons
with Balanced Synapses [0.27998963147546135]
Mixed-signal neuromorphic processors could be used for inference and learning.
We show how inhomogeneous synaptic circuits could be utilized for resource-efficient implementation of network layers.
arXiv Detail & Related papers (2021-06-10T12:04:03Z) - 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) - The distribution of inhibitory neurons in the C. elegans connectome
facilitates self-optimization of coordinated neural activity [78.15296214629433]
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.
arXiv Detail & Related papers (2020-10-28T23:11:37Z)
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.