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Detecting and Estimating Signals over Noisy and Unreliable Synapses: Information-Theoretic Analysis

Manwani, Amit and Koch, Christof (2001) Detecting and Estimating Signals over Noisy and Unreliable Synapses: Information-Theoretic Analysis. Neural Computation, 13 (1). pp. 1-33. ISSN 0899-7667. https://resolver.caltech.edu/CaltechAUTHORS:20111109-093159219

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Abstract

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 (synapse, dendrite, soma, axon, and so on) is needed to understand the relationship between noise and information transfer. Here we study the effect of the unreliable, probabilistic nature of synaptic transmission on information transfer in the absence of interaction among presynaptic inputs. We derive 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, and the objective is to estimate the continuous input signal from the postsynaptic voltage. In signal detection, the input is binary, and the presence or absence of a presynaptic action potential is to be detected from the postsynaptic voltage. The efficacy of information transfer in synaptic transmission is characterized by deriving optimal strategies under these two paradigms. On the basis of parameter values derived from neocortex, we find that single cortical synapses cannot transmit information reliably, but redundancy obtained using a small number of multiple synapses leads to a significant improvement in the information capacity of synaptic transmission.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1162/089976601300014619 DOIUNSPECIFIED
http://www.mitpressjournals.org/doi/abs/10.1162/089976601300014619PublisherUNSPECIFIED
Additional Information:© 2000 Massachusetts Institute of Technology. Received September 22, 1999; accepted March 28, 2000. Posted Online March 13, 2006. This research was supported by NSF, NIMH, and the Sloan Center for Theoretical Neuroscience. We thank Fabrizio Gabbiani, Tony Zador, and Peter Steinmetz for illuminating discussions.
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National Institute of Mental Health (NIMH)UNSPECIFIED
Sloan Center for Theoretical NeuroscienceUNSPECIFIED
Issue or Number:1
Record Number:CaltechAUTHORS:20111109-093159219
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20111109-093159219
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27695
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Jan 2012 22:22
Last Modified:03 Oct 2019 03:25

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