Abstract
Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets, i.e., in stereotypical, correlated spiking patterns of neural activity. Due to their relevance to coherent spiking, synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited the classical synfire chain’s ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by streaming input, processes the input, then makes a decision based on the processed information and shuts itself down.
Highlights
Functioning neuronal networks need to store, transmit, integrate and transform their inputs to achieve the neural computation performed by the brain
After providing a more general understanding of graded information propagation than is discussed in our previous work, we demonstrate that, by allowing graded information to interact with neural gating populations, decisions can be made within our pulse-gated information processing framework. We show how this works by implementing a self-contained, spike-based, modular neural circuit that is triggered by an input stream, reads in and processes the input, generates a conditional output based on the processed information, shuts itself off
synfire-gated synfire chains (SGSCs) can be used as building blocks to implement complex information processing algorithms, including sub-circuits responsible for short-term memory, linear maps, and computational logic
Summary
Functioning neuronal networks need to store, transmit, integrate and transform their inputs to achieve the neural computation performed by the brain. How this happens in vivo has not been understood. Numerical studies investigating fundamental computational mechanisms such as information propagation [12,13,14,15] have shown that it is possible to transfer firing rates through feed-forward networks when there is sufficient background activity to keep the network near threshold [16]. Further studies have shown that additional coherent spatio-temporal structures (e.g. hubs or oscillations) can stabilize the propagation of synchronous activity and select specific pathways for signal transmission [17,18,19,20]. How general computation (i.e. a Turing complete framework) can be performed using spikes or firing rates remains an open problem
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