In the Poisson neuron model, the output is a rate-modulated Poisson process (Snyder and Miller, 1991); the time varying rate parameter r(t) is an instantaneous function G[.] of the stimulus, r(t) = G[s(t)]. In a Poisson neuron, then, r(t) gives the instantaneous firing rate--the instantaneous probability of firing at any instant t--and the output is a stochastic function of the input. In part because of its great simplicity, this model is widely used (usually with the addition of a refractory period), especially in in vivo single unit electrophysiological studies, where s(t) is usually taken to be the value of some sensory stimulus. In the integrate-and-fire neuron model, by contrast, the output is a filtered and thresholded function of the input: the input is passed through a low-pass filter (determined by the membrane time constant τ) and integrated until the membrane potential v(t) reaches threshold θ, at which point v(t) is reset to its initial value. By contrast with the Poisson model, in the integrate-and-fire model the ouput is a deterministic function of the input. Although the integrate-and-fire model is a caricature of real neural dynamics, it captures many of the qualitative features, and is often used as a starting point for conceptualizing the biophysical behavior of single neurons. Here we show how a slightly modified Poisson model can be derived from the integrate-and-fire model with noisy inputs y(t) = s(t) + n(t). In the modified model, the transfer function G[.] is a sigmoid (erf) whose shape is determined by the noise variance σn2. Understanding the equivalence between the dominant in vivo and in vitro simple neuron models may help forge links between the two levels.
[1]
R. Mazo.
On the theory of brownian motion
,
1973
.
[2]
Donald L. Snyder,et al.
Random Point Processes in Time and Space
,
1991
.
[3]
Michael N. Shadlen,et al.
Noise, neural codes and cortical organization
,
1994,
Current Opinion in Neurobiology.
[4]
P. Mazur.
On the theory of brownian motion
,
1959
.
[5]
Daniel J. Amit,et al.
Quantitative Study of Attractor Neural Network Retrieving at Low Spike Rates: I
,
1991
.
[6]
William T Newsome,et al.
Is there a signal in the noise?
,
1995,
Current Opinion in Neurobiology.
[7]
William R. Softky,et al.
Simple codes versus efficient codes
,
1995,
Current Opinion in Neurobiology.
[8]
Barak A. Pearlmutter,et al.
VC Dimension of an Integrate-and-Fire Neuron Model
,
1996,
Neural Computation.
[9]
D. Amit,et al.
Quantitative study of attractor neural networks retrieving at low spike rates: II. Low-rate retrieval in symmetric networks
,
1991
.
[10]
Michael DeWeese,et al.
Optimization Principles for the Neural Code
,
1995,
NIPS.
[11]
William R. Softky,et al.
The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
,
1993,
The Journal of neuroscience : the official journal of the Society for Neuroscience.