391 research outputs found
Stimulus competition by inhibitory interference
When two stimuli are present in the receptive field of a V4 neuron, the
firing rate response is between the weakest and strongest response elicited by
each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience
19:1736-1753). When attention is directed towards the stimulus eliciting the
strongest response (the preferred stimulus), the response to the pair is
increased, whereas the response decreases when attention is directed to the
other stimulus (the poor stimulus). These experimental results were reproduced
in a model of a V4 neuron under the assumption that attention modulates the
activity of local interneuron networks. The V4 model neuron received
stimulus-specific asynchronous excitation from V2 and synchronous inhibitory
inputs from two local interneuron networks in V4. Each interneuron network was
driven by stimulus-specific excitatory inputs from V2 and was modulated by a
projection from the frontal eye fields. Stimulus competition was present
because of a delay in arrival time of synchronous volleys from each interneuron
network. For small delays, the firing rate was close to the rate elicited by
the preferred stimulus alone, whereas for larger delays it approached the
firing rate of the poor stimulus. When either stimulus was presented alone the
neuron's response was not altered by the change in delay. The model suggests
that top-down attention biases the competition between V2 columns for control
of V4 neurons by changing the relative timing of inhibition rather than by
changes in the degree of synchrony of interneuron networks. The mechanism
proposed here for attentional modulation of firing rate - gain modulation by
inhibitory interference - is likely to have more general applicability to
cortical information processing.Comment: 20 pages, 7 figures, 1 tabl
Inhibitory synchrony as a mechanism for attentional gain modulation
Recordings from area V4 of monkeys have revealed that when the focus of
attention is on a visual stimulus within the receptive field of a cortical
neuron, two distinct changes can occur: The firing rate of the neuron can
change and there can be an increase in the coherence between spikes and the
local field potential in the gamma-frequency range (30-50 Hz). The hypothesis
explored here is that these observed effects of attention could be a
consequence of changes in the synchrony of local interneuron networks. We
performed computer simulations of a Hodgkin-Huxley type neuron driven by a
constant depolarizing current, I, representing visual stimulation and a
modulatory inhibitory input representing the effects of attention via local
interneuron networks. We observed that the neuron's firing rate and the
coherence of its output spike train with the synaptic inputs was modulated by
the degree of synchrony of the inhibitory inputs. The model suggest that the
observed changes in firing rate and coherence of neurons in the visual cortex
could be controlled by top-down inputs that regulated the coherence in the
activity of a local inhibitory network discharging at gamma frequencies.Comment: J.Physiology (Paris) in press, 11 figure
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
Exact neural mass model for synaptic-based working memory
A synaptic theory of Working Memory (WM) has been developed in the last
decade as a possible alternative to the persistent spiking paradigm. In this
context, we have developed a neural mass model able to reproduce exactly the
dynamics of heterogeneous spiking neural networks encompassing realistic
cellular mechanisms for short-term synaptic plasticity. This population model
reproduces the macroscopic dynamics of the network in terms of the firing rate
and the mean membrane potential. The latter quantity allows us to get insight
on Local Field Potential and electroencephalographic signals measured during WM
tasks to characterize the brain activity. More specifically synaptic
facilitation and depression integrate each other to efficiently mimic WM
operations via either synaptic reactivation or persistent activity. Memory
access and loading are associated to stimulus-locked transient oscillations
followed by a steady-state activity in the band, thus resembling
what observed in the cortex during vibrotactile stimuli in humans and object
recognition in monkeys. Memory juggling and competition emerge already by
loading only two items. However more items can be stored in WM by considering
neural architectures composed of multiple excitatory populations and a common
inhibitory pool. Memory capacity depends strongly on the presentation rate of
the items and it maximizes for an optimal frequency range. In particular we
provide an analytic expression for the maximal memory capacity. Furthermore,
the mean membrane potential turns out to be a suitable proxy to measure the
memory load, analogously to event driven potentials in experiments on humans.
Finally we show that the power increases with the number of loaded
items, as reported in many experiments, while and power reveal
non monotonic behaviours.Comment: 47 pages, 14 figure
Multiple forms of working memory emerge from synapse-astrocyte interactions in a neuron-glia network model
Persistent activity in populations of neurons, time-varying activity across a neural population, or activity-silent mechanisms carried out by hidden internal states of the neural population have been proposed as different mechanisms of working memory (WM). Whether these mechanisms could be mutually exclusive or occur in the same neuronal circuit remains, however, elusive, and so do their biophysical underpinnings. While WM is traditionally regarded to depend purely on neuronal mechanisms, cortical networks also include astrocytes that can modulate neural activity. We propose and investigate a network model that includes both neurons and glia and show that glia-synapse interactions can lead to multiple stable states of synaptic transmission. Depending on parameters, these interactions can lead in turn to distinct patterns of network activity that can serve as substrates for WM
Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks
It has recently been discovered that single neuron stimulation can impact
network dynamics in immature and adult neuronal circuits. Here we report a
novel mechanism which can explain in neuronal circuits, at an early stage of
development, the peculiar role played by a few specific neurons in
promoting/arresting the population activity. For this purpose, we consider a
standard neuronal network model, with short-term synaptic plasticity, whose
population activity is characterized by bursting behavior. The addition of
developmentally inspired constraints and correlations in the distribution of
the neuronal connectivities and excitabilities leads to the emergence of
functional hub neurons, whose stimulation/deletion is critical for the network
activity. Functional hubs form a clique, where a precise sequential activation
of the neurons is essential to ignite collective events without any need for a
specific topological architecture. Unsupervised time-lagged firings of
supra-threshold cells, in connection with coordinated entrainments of
near-threshold neurons, are the key ingredients to orchestrateComment: 39 pages, 15 figures, to appear in PLOS Computational Biolog
Physiological role of PRRT2 and its involvement in the pathogenesis of paroxysmal disorders
Mutations in the PRoline-Rich Transmembrane protein 2 gene (PRRT2) underlie a heterogeneous group of paroxysmal disorders encompassing infantile epilepsy, paroxysmal kinesigenic dyskinesia, a combination of these phenotypes and migraine. For the majority of the pathogenic PRRT2 variants, the mutant proteins are not expressed or not correctly targeted to the plasma membrane, resulting in a loss-of function mechanism for PRRT2-related diseases. PRRT2 is a neuron-specific, type II transmembrane protein of 340 amino acids with an important functional role in synapse formation and maintenance, as well as in the regulation of fast neurotransmitter release at both glutamatergic and GABAergic terminals. The PRRT2 knock-out (PRRT2-KO) mouse, in which PRRT2 has been constitutively inactivated, displays alterations in brain structure and a sharp paroxysmal phenotype, reminiscent of the most common clinical manifestations of the human PRRT2-linked diseases. To gain further insights on the pathogenic role of PRRT2 deficiency, I used Multi-Electrode Arrays (MEAs) to characterize neuronal activity generated by primary hippocampal cultures obtained from the PRRT2-KO mouse embryos and to assess the epileptic propensity of cortico-hippocampal slices obtained from the same animal model. This experimental approach revealed a state of heightened spontaneous activity, hyper-synchronization in population bursts of action potentials (APs) and enhanced responsiveness to external stimuli in mutant networks. A complex interplay between (i) a synaptic phenotype, with weakened spontaneous transmission and increased short-term facilitation, and (ii) a marked increase in intrinsic excitability of excitatory neurons as assessed by single-cell electrophysiology, upholds this network phenotype. Furthermore, our group has generated cortical neurons from induced pluripotent stem cells (iPSCs) derived from heterozygous and homozygous siblings carrying the most common C.649dupC mutation. Patch-clamp recordings in neurons from homozygous patients showed an increased Na+ current that was fully rescued by expression of exogenous wild-type PRRT2. A strikingly similar electrophysiological phenotype was observed in excitatory primary cortical neurons from the PRRT2-KO mouse, which was accompanied by an increased length of the axon initial segment (AIS). At the network level, mutant cortical neurons grown on MEAs also displayed a state of spontaneous and evoked hyper-excitability and elevated propensity to synchronize their activity in network bursting events
- …