. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand ...

We have documented previously a close relationship between neuronal activity in the middle temporal visual area (MT or V5) and behavioral judgments of motion (Newsome et al., 1989; Salzman et al., 1990; Britten et al., 1992; Britten et al., 1996). We have now used numerical simulations to try to understand how neural signals in area MT support psychophysical decisions. We developed a model that pools neuronal responses drawn from our physiological data set and compares average responses in different pools to produce psychophysical decisions. The structure of the model allows us to assess the relationship between “neuronal ” input signals and simulated psychophysical performance using the same methods we have applied to real experimental data. We sought to reconcile three experimental observations: psychophysical performance (threshold sensitivity to motion

I discuss the construction of models that describe the firing rates of excitatory and inhibitory neurons in biological neural networks. A model is presented that incorporates both slow linear and fast nonlinear inhibition. With the appropriate excitatory-to-excitatory couplings this model can act as an associative memory in which pattern recognition is signalled by resonant firing behavior. Stored memories are represented by fixed points of the excitatory and fast inhibitory dynamics. After memory recovery, slow inhibition returns the system to the silent, resting state. Published in Benhar, O., Bosio, C., Del Giudice, P. and Tabet, E., eds. Neural Networks: From Biology to High- Energy Physics (ETS Editrice, Pisa, 1991) pp. 179-196. 1. INTRODUCTION The model neural networks used for pattern recognition and data analysis are inspired by, but only impressionistically related to, their biological counterparts. A central issue in the biological study of neural networks is whether the in...

. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand...

this paper, we study information transmission in multilayer architectures in which computation is distributed and activity needs to propagate through many layers. We show that, in the presence of a noisy background current, firing rates propagate rapidly and linearly through a deeply layered network. The noise is essential but does not lead to deterioration of the propagated activity. The efficiency of the rate coding is improved by combining it with a population code. We propose that the resulting signal coding is a realistic framework for sensory computation