1,445 research outputs found
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
We study the collective dynamics of a Leaky Integrate and Fire network in
which precise relative phase relationship of spikes among neurons are stored,
as attractors of the dynamics, and selectively replayed at differentctime
scales. Using an STDP-based learning process, we store in the connectivity
several phase-coded spike patterns, and we find that, depending on the
excitability of the network, different working regimes are possible, with
transient or persistent replay activity induced by a brief signal. We introduce
an order parameter to evaluate the similarity between stored and recalled
phase-coded pattern, and measure the storage capacity. Modulation of spiking
thresholds during replay changes the frequency of the collective oscillation or
the number of spikes per cycle, keeping preserved the phases relationship. This
allows a coding scheme in which phase, rate and frequency are dissociable.
Robustness with respect to noise and heterogeneity of neurons parameters is
studied, showing that, since dynamics is a retrieval process, neurons preserve
stablecprecise phase relationship among units, keeping a unique frequency of
oscillation, even in noisy conditions and with heterogeneity of internal
parameters of the units
The influence of dopamine on prediction, action and learning
In this thesis I explore functions of the neuromodulator dopamine in the context
of autonomous learning and behaviour. I first investigate dopaminergic influence
within a simulated agent-based model, demonstrating how modulation of
synaptic plasticity can enable reward-mediated learning that is both adaptive and
self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment
interaction and consequently suggest roles for both complex spontaneous
neuronal activity and specific neuroanatomy in the expression of early, exploratory
behaviour. I then show how the observed response of dopamine neurons
in the mammalian basal ganglia may also be modelled by similar processes involving
dopaminergic neuromodulation and cortical spike-pattern representation within
an architecture of counteracting excitatory and inhibitory neural pathways, reflecting
gross mammalian neuroanatomy. Significantly, I demonstrate how combined
modulation of synaptic plasticity and neuronal excitability enables specific (timely)
spike-patterns to be recognised and selectively responded to by efferent neural populations,
therefore providing a novel spike-timing based implementation of the hypothetical
‘serial-compound’ representation suggested by temporal difference learning.
I subsequently discuss more recent work, focused upon modelling those complex
spike-patterns observed in cortex. Here, I describe neural features likely to contribute
to the expression of such activity and subsequently present novel simulation
software allowing for interactive exploration of these factors, in a more comprehensive
neural model that implements both dynamical synapses and dopaminergic
neuromodulation. I conclude by describing how the work presented ultimately suggests
an integrated theory of autonomous learning, in which direct coupling of agent
and environment supports a predictive coding mechanism, bootstrapped in early
development by a more fundamental process of trial-and-error learning
Dynamic Control of Network Level Information Processing through Cholinergic Modulation
Acetylcholine (ACh) release is a prominent neurochemical marker of arousal state
within the brain. Changes in ACh are associated with changes in neural activity and
information processing, though its exact role and the mechanisms through which it
acts are unknown. Here I show that the dynamic changes in ACh levels that are
associated with arousal state control informational processing functions of networks
through its effects on the degree of Spike-Frequency Adaptation (SFA), an activity
dependent decrease in excitability, synchronizability, and neuronal resonance displayed
by single cells. Using numerical modeling I develop mechanistic explanations
for how control of these properties shift network activity from a stable high frequency
spiking pattern to a traveling wave of activity. This transition mimics the change
in brain dynamics seen between high ACh states, such as waking and Rapid Eye
Movement (REM) sleep, and low ACh states such as Non-REM (NREM) sleep. A
corresponding, and related, transition in network level memory recall is also occurs
as ACh modulates neuronal SFA. When ACh is at its highest levels (waking) all
memories are stably recalled, as ACh is decreased (REM) in the model weakly encoded
memories destabilize while strong memories remain stable. In levels of ACh
that match Slow Wave Sleep (SWS), no encoded memories are stably recalled. This
results from a competition between SFA and excitatory input strength and provides
a mechanism for neural networks to control the representation of underlying synaptic
information. Finally I show that during the low ACh conditions, oscillatory conditions
allow for external inputs to be properly stored in and recalled from synaptic weights. Taken together this work demonstrates that dynamic neuromodulation is
critical for the regulation of information processing tasks in neural networks. These
results suggest that ACh is capable of switching networks between two distinct information
processing modes. Rate coding of information is facilitated during high
ACh conditions and phase coding of information is facilitated during low ACh conditions.
Finally I propose that ACh levels control whether a network is in one of
three functional states: (High ACh; Active waking) optimized for encoding of new
information or the stable representation of relevant memories, (Mid ACh; resting
state or REM) optimized for encoding connections between currently stored memories
or searching the catalog of stored memories, and (Low ACh; NREM) optimized
for renormalization of synaptic strength and memory consolidation. This work provides
a mechanistic insight into the role of dynamic changes in ACh levels for the
encoding, consolidation, and maintenance of memories within the brain.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147503/1/roachjp_1.pd
Neuromorphic Engineering Editors' Pick 2021
This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors
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