55,077 research outputs found
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using
mixed-signal analog/digital circuits represent a promising technology for
closed-loop real-time processing of biosignals. As in biology, to minimize
power consumption, the silicon neurons' circuits are configured to fire with a
limited dynamic range and with maximum firing rates restricted to a few tens or
hundreds of Herz.
However, biosignals can have a very large dynamic range, so encoding them
into spikes without saturating the neuron outputs represents an open challenge.
In this work, we present a biologically-inspired strategy for compressing
this high-dynamic range in SNN architectures, using three adaptation mechanisms
ubiquitous in the brain: spike-frequency adaptation at the single neuron level,
feed-forward inhibitory connections from neurons belonging to the input layer,
and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons
in the output layer.
We apply this strategy to input biosignals encoded using both an asynchronous
delta modulation method and an energy-based pulse-frequency modulation method.
We validate this approach in silico, simulating a simple network applied to a
gesture classification task from surface EMG recordings.Comment: 5 pages, 7 figures, to be published in IEEE BioCAS 2023 Proceeding
Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance
Neuromodulatory receptors in presynaptic position have the ability to
suppress synaptic transmission for seconds to minutes when fully engaged. This
effectively alters the synaptic strength of a connection. Much work on
neuromodulation has rested on the assumption that these effects are uniform at
every neuron. However, there is considerable evidence to suggest that
presynaptic regulation may be in effect synapse-specific. This would define a
second "weight modulation" matrix, which reflects presynaptic receptor efficacy
at a given site. Here we explore functional consequences of this hypothesis. By
analyzing and comparing the weight matrices of networks trained on different
aspects of a task, we identify the potential for a low complexity "modulation
matrix", which allows to switch between differently trained subtasks while
retaining general performance characteristics for the task. This means that a
given network can adapt itself to different task demands by regulating its
release of neuromodulators. Specifically, we suggest that (a) a network can
provide optimized responses for related classification tasks without the need
to train entirely separate networks and (b) a network can blend a "memory mode"
which aims at reproducing memorized patterns and a "novelty mode" which aims to
facilitate classification of new patterns. We relate this work to the known
effects of neuromodulators on brain-state dependent processing.Comment: 6 pages, 13 figure
Toward a dynamical systems analysis of neuromodulation
This work presents some first steps toward a
more thorough understanding of the control systems
employed in evolutionary robotics. In order
to choose an appropriate architecture or to construct
an effective novel control system we need
insights into what makes control systems successful,
robust, evolvable, etc. Here we present analysis
intended to shed light on this type of question
as it applies to a novel class of artificial neural
networks that include a neuromodulatory mechanism:
GasNets.
We begin by instantiating a particular GasNet
subcircuit responsible for tuneable pattern generation
and thought to underpin the attractive
property of “temporal adaptivity”. Rather than
work within the GasNet formalism, we develop an
extension of the well-known FitzHugh-Nagumo
equations. The continuous nature of our model
allows us to conduct a thorough dynamical systems
analysis and to draw parallels between this
subcircuit and beating/bursting phenomena reported
in the neuroscience literature.
We then proceed to explore the effects of different
types of parameter modulation on the system
dynamics. We conclude that while there are
key differences between the gain modulation used
in the GasNet and alternative schemes (including
threshold modulation of more traditional synaptic
input), both approaches are able to produce
tuneable pattern generation. While it appears, at
least in this study, that the GasNet’s gain modulation
may not be crucial to pattern generation ,
we go on to suggest some possible advantages it
could confer
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit
The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit's connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells. (C) 2009 Elsevier Ltd. All rights reserved
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