Integrate-and-Fire Neurons

Encoding with Integrate-and-Fire (IAF) Neurons

  • IAF Encoding (iaf_encode.m) [2] [11]:

    Encodes a time-varying signal using an IAF neuron. Leaky and ideal neuron models are supported. In addition, IAF neuron with random (Gaussian) thresholds is also supported. A signal can be encoded by a single IAF encoder (Single-Input Single-Output Encoding), as shown in the figure below, or it can be encoded by a population of IAF neurons (Single-Input Multi-Output Encoding).

    _images/tem-iaf-rt.png
  • ON-OFF IAF Encoding (iaf_encode_ideal_on_off.m) [12]:

    Encodes a time-varying signal using an ON-OFF IAF neuron pair. Only ideal IAF neuron models are supported.

    _images/tem-iaf-coupled.png

Decoding for Signal Encoded with Single-Input Single-Output IAF neuron

  • Decoding (iaf_decode.m) [1]:

    Reconstructs a bandlimited signal encoded by an IAF neuron using sinc kernels.

    _images/tdm-sinc.png
  • Decoding using Fast Approximation Method (iaf_decode_fast.m) [4]:

    Reconstructs a bandlimited signal encoded by an IAF neuron using a fast approximation method.

    _images/tdm-fast.png
  • Decoding using Spline Interpolation (consistent_decoding_LIF.m) [12]:

    Reconstructs a finite-energy signal encoded by an Leaky-Integrate-and-Fire (LIF) neuron. It uses spline interpolation algorithm.

    _images/tdm-spline.png
  • Decoding using Smoothing Spline (LIF_decode_S1.m, LIF_decode_S2.m) [11]:

    Reconstructs a signal in Sobolev space S_1 or S_2 encoded by a LIF neuron using smoothing splines. Signals encoded by a LIF with random threshold should be decoded using this function.

    _images/tdm-spline-smoothing.png

Decoding for Signal Encoded with Single-Input Multiple-Output IAF neurons

  • Decoding for ON-OFF IAF (consistent_decoding_IF_ONOFF.m) [12]:

    Reconstructs a finite energy signal encoded by ON-OFF IAF neuron pair. The reconstruction is performed using spline interpolation method.

    _images/tdm-spline-mimo.png
  • Population Decoding (iaf_decode_pop.m) [7]:

    Reconstructs a bandlimited signal encoded by an ensemble of IAF neurons using sinc kernels.

    _images/tdm-sinc-miso.png
  • Population Decoding using Smoothing Splines (LIF_pop_decode_S1.m, LIF_pop_decode_S2.m) [11]:

    Reconstructs a signal in Sobolev Space S_1 or S_2 encoded by a population of LIF neurons. The reconstruction uses smoothing spline method in the RKHS. Signals encoded by population of LIF with random threshold should be decoded using this function.

    _images/tdm-spline-smoothing-mimo.png

Decoding for Signals Encoded with Multiple-Input Multiple-Output IAF neurons

  • Population Decoding using Spline Interpolation (consistent_decoding_IF_MIMO.m) [12]:

    Reconstructs multiple finite energy signals encoded by a population of ideal IAF neurons in Multiple-Input Multiple Output setting. The reconstruction uses spline interpolation method.

    _images/tdm-spline-mimo.png