42 research outputs found
A Pulse-Gated, Predictive Neural Circuit
Recent evidence suggests that neural information is encoded in packets and
may be flexibly routed from region to region. We have hypothesized that neural
circuits are split into sub-circuits where one sub-circuit controls information
propagation via pulse gating and a second sub-circuit processes graded
information under the control of the first sub-circuit. Using an explicit
pulse-gating mechanism, we have been able to show how information may be
processed by such pulse-controlled circuits and also how, by allowing the
information processing circuit to interact with the gating circuit, decisions
can be made. Here, we demonstrate how Hebbian plasticity may be used to
supplement our pulse-gated information processing framework by implementing a
machine learning algorithm. The resulting neural circuit has a number of
structures that are similar to biological neural systems, including a layered
structure and information propagation driven by oscillatory gating with a
complex frequency spectrum.Comment: This invited paper was presented at the 50th Asilomar Conference on
Signals, Systems and Computer
Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains
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. Synfire chains are one of the main theoretical constructs that have
been appealed to to describe coherent spiking 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 their 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, then reads in streaming input, processes the input, then makes a
decision based on the processed information and shuts itself down
Mapping Functional Connectivity between Neuronal Ensembles with Larval Zebrafish Transgenic for a Ratiometric Calcium Indicator
The ability to map functional connectivity is necessary for the study of the flow of activity in neuronal circuits. Optical imaging of calcium indicators, including FRET- based genetically encoded indicators and extrinsic dyes, is an important adjunct to electrophysiology and is widely used to visualize neuronal activity. However, techniques for mapping functional connectivities with calcium imaging data have been lacking. We present a procedure to compute reduced functional couplings between neuronal ensembles undergoing seizure activity from ratiometric calcium imaging data in three steps: 1) calculation of calcium concentrations and neuronal firing rates from ratiometric data; 2) identification of putative neuronal populations from spatio-temporal timeseries of neural bursting activity; and then, 3) derivation of reduced connectivity matrices that represent neuronal population interactions. We apply our method to the larval zebrafish central nervous system undergoing chemoconvulsant induced seizures. These seizures generate propagating, central nervous system-wide neural activity from which population connectivities may be calculated. This automatic functional connectivity mapping procedure provides a practical and user-independent means for summarizing the flow of activity between neuronal ensembles
On nonlinear transformations in quantum computation
While quantum computers are naturally well-suited to implementing linear
operations, it is less clear how to implement nonlinear operations on quantum
computers. However, nonlinear subroutines may prove key to a range of
applications of quantum computing from solving nonlinear equations to data
processing and quantum machine learning. Here we develop a series of basic
subroutines for implementing nonlinear transformations of input quantum states.
Our algorithms are framed around the concept of a weighted state, a
mathematical entity describing the output of an operational procedure involving
both quantum circuits and classical post-processing.Comment: 10 pages, 9 figures in the main text. 14 pages, 6 figures in the
Appendice