In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems:“First Consecutive Spikes Counting” and “Total Spikes Counting”, which model temporalcoding and ratecoding aspects of temporaltospatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(logT) neurons and terminating in time T+ 1. We also prove that these bounds are tight.
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Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit
In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting" and "Total Spikes Counting", which model temporalcoding and ratecoding aspects of temporaltospatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(log T) neurons and terminating in time T+1. We also prove that these bounds are tight.
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 Award ID(s):
 1810758
 NSFPAR ID:
 10228798
 Date Published:
 Journal Name:
 33rd International Symposium on Distributed Computing (DISC)
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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