24,660 research outputs found
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 temporal-coding and rate-coding aspects of temporal-to-spatial 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
An Analog VLSI Model of the Fly Elementary Motion Detector
Flies are capable of rapidly detecting and integrating visual motion information in behaviorly-relevant ways. The first stage of visual motion processing in flies is a retinotopic array of functional units known as elementary
motion detectors (EMDs). Several decades ago, Reichardt and
colleagues developed a correlation-based model of motion detection that described the behavior of these neural circuits. We have implemented a variant of this model in a 2.0µm analog CMOS VLSI process. The result is a low-power, continuous-time analog circuit with integrated photoreceptors that responds to motion in real time. The responses of the circuit to drifting sinusoidal gratings qualitatively resemble the temporal frequency response, spatial frequency response, and direction selectivity
of motion-sensitive neurons observed in insects. In addition to its possible engineering applications, the circuit could potentially be used as a building block for constructing hardware models of higher-level insect
motion integration
Analog VLSI model of the fly elementary motion detector
Journal ArticleFlies are capable of rapidly detecting and integrating visual motion information in behaviorly-relevant ways. The first stage of visual motion processing in flies is a retinotopic array of functional units known as elementary motion detectors (EMDs). Several decades ago, Reichardt and colleagues developed a correlation-based model of motion detection that described the behavior of these neural circuits. We have implemented a variant of this model in a 2.0-μm analog CMOS VLSI process. The result is a low-power, continuous-time analog circuit with integrated photoreceptors that responds to motion in real time. The responses of the circuit to drifting sinusoidal gratings qualitatively resemble the temporal frequency response, spatial frequency response, and direction selectivity of motion-sensitive neurons observed in insects. In addition to its possible engineering applications, the circuit could potentially be used as a building block for constructing hardware models of higher-level insect motion integration
Allocentric directional processing in the rodent and human retrosplenial cortex
Copyright © 2014 Knight and Hayman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsHead direction (HD) cells in the rodent brain have been investigated for a number of years, providing us with a detailed understanding of how the rodent brain codes for allocentric direction. Allocentric direction refers to the orientation of the external environment, independent of one’s current (egocentric) orientation. The presence of neural activity related to allocentric directional coding in humans has also been noted but only recently directly tested. Given the current status of both fields, it seems beneficial to draw parallels between this rodent and human research. We therefore discuss how findings from the human retrosplenial cortex (RSC), including its “translational function” (converting egocentric to allocentric information) and ability to code for permanent objects, compare to findings from the rodent RSC. We conclude by suggesting critical future experiments that derive from a cross-species approach to understanding the function of the human RSCPeer reviewedFinal Published versio
Decoding odor quality and intensity in the Drosophila brain
To internally reflect the sensory environment, animals create neural maps encoding the external stimulus space. From that primary neural code relevant information has to be extracted for accurate navigation. We analyzed how different odor features such as hedonic valence and intensity are functionally integrated in the lateral horn (LH) of the vinegar fly, Drosophila melanogaster. We characterized an olfactory-processing pathway, comprised of inhibitory projection neurons (iPNs) that target the LH exclusively, at morphological, functional and behavioral levels. We demonstrate that iPNs are subdivided into two morphological groups encoding positive hedonic valence or intensity information and conveying these features into separate domains in the LH. Silencing iPNs severely diminished flies' attraction behavior. Moreover, functional imaging disclosed a LH region tuned to repulsive odors comprised exclusively of third-order neurons. We provide evidence for a feature-based map in the LH, and elucidate its role as the center for integrating behaviorally relevant olfactory information
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Saccade learning with concurrent cortical and subcortical basal ganglia loops
The Basal Ganglia is a central structure involved in multiple cortical and
subcortical loops. Some of these loops are believed to be responsible for
saccade target selection. We study here how the very specific structural
relationships of these saccadic loops can affect the ability of learning
spatial and feature-based tasks.
We propose a model of saccade generation with reinforcement learning
capabilities based on our previous basal ganglia and superior colliculus
models. It is structured around the interactions of two parallel cortico-basal
loops and one tecto-basal loop. The two cortical loops separately deal with
spatial and non-spatial information to select targets in a concurrent way. The
subcortical loop is used to make the final target selection leading to the
production of the saccade. These different loops may work in concert or disturb
each other regarding reward maximization. Interactions between these loops and
their learning capabilities are tested on different saccade tasks.
The results show the ability of this model to correctly learn basic target
selection based on different criteria (spatial or not). Moreover the model
reproduces and explains training dependent express saccades toward targets
based on a spatial criterion.
Finally, the model predicts that in absence of prefrontal control, the
spatial loop should dominate
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
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