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@INPROCEEDINGS{Bencheikh:1037898,
      author       = {Bencheikh, Wadjih and Neftci, Emre and Bouhadjar, Younes},
      title        = {{T}raining {S}piking {N}eural {N}etworks to emulate
                      brain-like activity for optimal efficiency},
      reportid     = {FZJ-2025-01036},
      year         = {2024},
      abstract     = {Unlike neurons in Artificial Neural Networks (ANNs),
                      biological neurons operate in continuous real-timeand thus
                      possess the ability to represent timing information. They
                      use tiny pulses of voltages to communicatewith each other,
                      called spikes. The spikes are generated scarcely and at
                      precise times, especially in earlysensory regions,
                      contributing to the energy efficiency of brain circuitry.
                      Another feature of biologicalneurons is that are equipped
                      with an intrinsic memory of hundreds of milliseconds, which
                      keeps track of recentactivity. These operations have
                      inspired the development of a novel kind of neural network
                      (NNs) known asspiking NNs (SNNs). In this study, we aim to
                      identify the most effective combination of neuron types
                      andlearning methods in SNNs that yield high accuracy while
                      minimizing the firing rate of neurons. We train andanalyze
                      the resulting spiking activity of both a feedforward and a
                      recurrent network of Adaptive LIF (adLIF)neurons. Training
                      of the feedforward connections between the layers includes
                      training both the delays andweights. We implemented the
                      delays using 1D Dilated Convolution with Learnable Spacings
                      (DLSC) . Thetraining process utilizes Back-Propagation
                      Through Time (BPTT) with surrogate gradients. We employ
                      aregularization function to control the population firing
                      rate. This function penalizes deviations from a desiredrange
                      of neuronal activity, comprising terms for hypoactivity
                      (excessively low firing rates) and hyperactivity(excessively
                      high firing rates). Optimal hyperparameters are chosen based
                      on the average highest accuracy on5 different network
                      realizations. The hyperparameters include: dropout rate,
                      connectivity type (feedforwardor recurrent), regularization
                      parameters, maximum delay, and hidden size of the layer.
                      Note that we strictlyconstrain the range of values of the
                      maximum allowed firing rate to reach a regime of high
                      sparsity. We evaluateour network performance on classifying
                      digits from the Spiking Heidelberg Dataset (SHD). The
                      dataset waspostprocessed using a temporal bin of 10ms and
                      the training consists of 50 epochs. Our analysis shows that
                      therecurrent network including trainable delays in the
                      feedforward connections demonstrates the highest accuracyand
                      the lowest firing rate (accuracy = $91.05\%,$ firing rate =
                      1.02Hz), where the firing rate is computed as theaverage of
                      the population mean spikes count per second. The
                      corresponding spiking activity exhibits spikingbursts. This
                      is problematic as this limits the use of spatiotemporal
                      patterns, increases network latency in termsof information
                      processing, and is hardly represented by networks
                      implemented on neuromorphic hardware. Toreduce bursting
                      neurons, we incorporate a refractory period which resulted
                      in a firing rate of 1.56Hz. However,this came at the cost of
                      a drop in accuracy $(88.82\%).$ Future work will assess the
                      reasons behind this latter lossand devise alternative
                      implementation methods and novel learning methodologies.
                      Finally, we employed SpikePattern Detection and Evaluation
                      (SPADE) in our analysis. Despite our efforts, we have not
                      yet identifiedsignificant recurrent spatiotemporal patterns
                      within the spiking activity of the network. It remains to be
                      shownin a future study whether the representations and
                      patterns developed by SNNs trained by means of the
                      BPTTalgorithm resemble those observed in biological neural
                      networks.},
      month         = {Apr},
      date          = {2024-04-23},
      organization  = {Neuro-Inspired Computing Elements, La
                       Jolla, California (USA), 23 Apr 2024 -
                       26 Apr 2024},
      subtyp        = {After Call},
      cin          = {PGI-15 / PGI-7},
      cid          = {I:(DE-Juel1)PGI-15-20210701 / I:(DE-Juel1)PGI-7-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / BMBF
                      16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0399)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1037898},
}