Detecting and estimating signals over noisy and unreliable synapses: Information-theoretic analysis

The temporal precision with which neurons respond to synaptic inputs has a direct bearing on the nature of the neural code. A characterization of the neuronal noise sources associated with different sub-cellular components (s ynapse, dendrite, soma, axon, and so on) is needed to understand the relati onship between noise and information transfer. Here we study the effect of the unreliable, probabilistic nature of synaptic transmission on informatio n transfer in the absence of interaction among presynaptic inputs. We deriv e theoretical lower bounds on the capacity of a simple model of a cortical synapse under two different paradigms. In signal estimation, the signal is assumed to be encoded in the mean firing rate of the presynaptic neuron, an d the objective is to estimate the continuous input signal from the postsyn aptic voltage. In signal detection, the input is binary, and the presence o r absence of a presynaptic action potential is to be detected from the post synaptic voltage. The efficacy of information transfer in synaptic transmis sion is characterized by deriving optimal strategies under these two paradi gms. On the basis of parameter values derived from neocortex, we find that single cortical synapses cannot transmit information reliably, but redundan cy obtained using a small number of multiple synapses leads to a significan t improvement in the information capacity of synaptic transmission.