Abstract
Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will redistribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.
Highlights
The range of firing rates that a sensory neuron can maintain is limited by biophysical constraints and available metabolic resources
A striking example of such gain modulation at the single cell level has been shown in the fly H1 neuron (Brenner et al, 2000)
We derive a prescription for the voltage dynamics of leaky integrateand-fire (LIF) neurons performing a greedy minimization of the objective function, E
Summary
The range of firing rates that a sensory neuron can maintain is limited by biophysical constraints and available metabolic resources. The time scale of this adaptation was chosen to be long enough to cover several visual patterns When subjected to this spike-time dependent adaptation, the responses became strongly history dependent, resulting in a highly inaccurate decoding (Figure 1b, 3rd row). This would suggest that activity in downstream areas and perceptual interpretations should be based on the current sensory responses, and on the recent history of neural activity (Fairhall et al, 2001; Borst et al, 2005). Recurrent connections can be tuned such that spike-dependent adaptation will not impair the stability of the representation (Figure 1b, bottom row)
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