<p>Shown are STRFs learned by optimizing (A) the sustained objective function for and (B) the sparsity objective function . The examples shown here were drawn at random from ensembles of 400 neurons. The sustained STRFs are shown in order of decreasing contribution to the overall objective function whereas the sparse STRFs are shown randomly ordered. Each spectro-temporal patch spans 0–250 ms in time and 62.5–4000 Hz in frequency. For these examples the dynamic range of the STRFs was compressed using a nonlinearity.</p
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly o...
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron mo...
Information transfer in neurons takes place through action potentials (spikes) which are metabolical...
<p>The examples shown here were drawn at random from an ensemble of 400 neurons, and the STRFs are s...
<p><b>.</b> In panels (A), (B), (C) and (E), the histograms show the distribution of model parameter...
<p>Each row corresponds to one neuron. (A) BT neuron STRF and (B) corresponding nonlinearity. Dashed...
<p><b>A, B</b>: Visualization of hidden structure in the spike inputs shown in D, E: Each row in pa...
<p>Each numbered row is an example neuron. (A) STRFs (top) and nonlinearities (bottom) for the LN mo...
A-C: Generative fields learned from the spectrograms of the natural sound data. A-B: The vertical ax...
The dynamic range of stimulus processing in living organisms is much larger than a single neural net...
<p><b>(A)</b> Alternate network schematic with hypercolumns (large black circles), along with their ...
In this work we study the detection of weak stimuli by spiking (integrate-and-fire) neurons in the p...
<p><b>A</b>: Illustration of the network architecture. A WTA circuit consisting of ten neurons <b>z<...
Nervous systems tune themselves to the statistical structure of the stimuli they encounter. This sen...
<p><b>A.</b> STRF weight matrices (<i>H</i>) for the FIR, factorized, and parameterized models fit t...
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly o...
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron mo...
Information transfer in neurons takes place through action potentials (spikes) which are metabolical...
<p>The examples shown here were drawn at random from an ensemble of 400 neurons, and the STRFs are s...
<p><b>.</b> In panels (A), (B), (C) and (E), the histograms show the distribution of model parameter...
<p>Each row corresponds to one neuron. (A) BT neuron STRF and (B) corresponding nonlinearity. Dashed...
<p><b>A, B</b>: Visualization of hidden structure in the spike inputs shown in D, E: Each row in pa...
<p>Each numbered row is an example neuron. (A) STRFs (top) and nonlinearities (bottom) for the LN mo...
A-C: Generative fields learned from the spectrograms of the natural sound data. A-B: The vertical ax...
The dynamic range of stimulus processing in living organisms is much larger than a single neural net...
<p><b>(A)</b> Alternate network schematic with hypercolumns (large black circles), along with their ...
In this work we study the detection of weak stimuli by spiking (integrate-and-fire) neurons in the p...
<p><b>A</b>: Illustration of the network architecture. A WTA circuit consisting of ten neurons <b>z<...
Nervous systems tune themselves to the statistical structure of the stimuli they encounter. This sen...
<p><b>A.</b> STRF weight matrices (<i>H</i>) for the FIR, factorized, and parameterized models fit t...
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly o...
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron mo...
Information transfer in neurons takes place through action potentials (spikes) which are metabolical...