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:redstar: Spartacus

#IRANIAN TV RELEASES VIDEO SHOWING TARGETS FOR IMMINENT STRIKES

Iranian channel #SNN has unveiled a detailed 3D image of key military and intelligence sites in Israel, including the Ramat David Airbase, a focal point in recent Israeli attacks.

These sites are now listed as legitimate targets for upcoming missile and drone strikes.

Public
Albert Cardona

"A spiking neural network model for proprioception of limb kinematics in insect locomotion", by van der Veen et al. 2025.

biorxiv.org/content/10.1101/20

bioRxiv · A spiking neural network model for proprioception of limb kinematics in insect locomotionProprioception plays a key role in all behaviours that involve the control of force, posture or movement. Computationally, many proprioceptive afferents share three common features: First, their strictly local encoding of stimulus magnitudes leads to range fractionation in sensory arrays. As a result, encoding of large joint angle ranges requires integration of convergent afferent information by first-order interneurons. Second, their phasic-tonic response properties lead to fractional encoding of the fundamental sensory magnitude and its derivatives (e.g., joint angle and angular velocity). Third, the distribution of disjunct sensory arrays across the body accounts for distributed encoding of complex movements, e.g., at multiple joints or by multiple limbs. The present study models the distributed encoding of limb kinematics, proposing a multi-layer spiking neural network for distributed computation of whole-body posture and movement. Spiking neuron models are biologically plausible because they link the sub-threshold state of neurons to the timing of spike events. The encoding properties of each network layer are evaluated with experimental data on whole-body kinematics of unrestrained walking and climbing stick insects, comprising concurrent joint angle time courses of 6 × 3 leg joints. The first part of the study models strictly local, phasic-tonic encoding of joint angle by proprioceptive hair field afferents by use of Adaptive Exponential Integrate-and-Fire neurons. Convergent afferent information is then integrated by two types of first-order interneurons, modelled as Leaky Integrate-and-Fire neurons, tuned to encode either joint position or velocity across the entire working range with high accuracy. As in known velocity-encoding antennal mechanosensory interneurons, spike rate increases linearly with angular velocity. Building on distributed position/velocity encoding, the second part of the study introduces second- and third-order interneurons. We demonstrate that simple combinations of two or three position/velocity inputs from disjunct arrays can encode high-order movement information about step cycle phases and converge to encode overall body posture. Author summary When stick insects climb through a bramble bush at night, they successfully navigate through highly complex terrain with little more sensory information than touch and proprioception of their own body posture and movement. To achieve this, their central nervous system needs to monitor the position and motion of all limbs, and infer information about whole-body movement from integration in a multi-layer neural network. Although the encoding properties of some proprioceptive inputs to this network are known, the integration and processing of distributed proprioceptive information is poorly understood. Here, we use a computational model of a spiking neural network to simulate peripheral encoding of 6 × 3 joint angles and angular velocities. The second part of the study explores how higher-order information can be integrated across multiple joints and limbs. For evaluation, we use experimental data from unrestrained walking and climbing stick insects. Spiking neurons model the key response properties known from their real biological counterparts. In particular, we show that the first integration layer of the model is able to encode joint angle and velocity both linearly and accurately from an array of phasic-tonic input elements. The model is simple, accurate and based, where possible, on biological evidence. ### Competing Interest Statement The authors have declared no competing interest.
#neuroscience#proprioception#insects
Public
Fabrizio Musacchio

Brody and #Hopfield (2003) showed how networks of #SpikingNeurons (#SNN) can be used to process temporal information based on computations on the timing of spikes rather than the rate of spikes. This is particularly relevant in the context of #OlfactoryProcessing, where the timing of spikes in the olfactory bulb is crucial for encoding odor information. Here is a quick tutorial, that recapitulates the main concepts of that network using #NEST #simulator:

🌍 fabriziomusacchio.com/blog/202

Spike times (top) and spike frequency (bottom) of the network (1000 neurons). The spike raster plot shows the spike times of individual neurons over time, while the histogram displays the spiking rate as a function of time. The alternating current generator drives oscillations in the input currents, leading to synchronized spiking activity in the network. The noise generator introduces synaptic noise, adding variability to the spiking patterns. The network exhibits complex dynamics that can be further analyzed to understand the principles of spike-timing-based computation.
#CompNeuro
Public
Laurent Perrinet

Special Session on #SpikingNeuralNetworks and #Neuromorphic Computing at the 33rd International Conference on Artificial Neural Networks (ICANN) 2024 - Call for Papers

Sep 17 - 20, Lugano, Switzerland

The special session invites contributions on recent advances in spiking neural networks. Spiking neural networks have gained substantial attention recently as a candidate for low latency and low power AI substrate, with implementations being explored in neuromorphic hardware. This special session aims to bring together practitioners interested in efficient learning algorithms, data representations, and applications.

ORGANIZERS:

  • Sander Bohté (CWI Amsterdam, Netherlands)
  • Sebastian Otte (University of Lübeck, Germany)

Find more details at: e-nns.org/wp-content/uploads/2

Special Session on Spiking Neural Networks and Neuromorphic Computing at the 33rd International Conference on Artificial Neural Networks (ICANN2024) Sep 17 - 20, Lugano
#icann#enns#ai
Public
Dan Goodman

Many thanks to all who sent abstracts for the SNUFA SNN workshop. We will be sending every participant a random sample of abstracts to vote on to help decide what should be a talk/poster. If you want to take part, register today (free).

eventbrite.co.uk/e/snufa-2023-

More info on the workshop at:

snufa.net/2023/

EventbriteSNUFA 2023Online computational neuroscience and neuromorphic engineering workshop
#SpikingNeuralNetworks#compneuro#snn
Public
Dan Goodman

For all you lovers of #SpikingNeuralNetworks out there, make sure to put November 7-8 in your diary and submit a 300 word abstract to SNUFA 2023:

snufa.net/2023

Invited speakers this year include:
⭐ Rodolphe Sepulchre
⭐ Melika Payvand
⭐ Gabriel Ocker
⭐ Jeff Krichmar

In previous years we usually get over 700 participants, and about 200 live at each talk. Not to mention hundreds to thousands of views after the event on Youtube. Best of all - it's all free so there's really no reason you shouldn't already be writing your abstract.

And if you're still not convinced, check out previous years' talks and our monthly seminar series on my Youtube channel: youtube.com/@neuralreckoning

What are you waiting for, get on over there:

snufa.net/2023

SNUFASNUFA 2023Spiking Neural networks as Universal Function Approximators
#neuroscience#compneuro#snn
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