28 research outputs found
Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability
The transformation of synaptic input into patterns of spike output is a
fundamental operation that is determined by the particular complement of ion
channels that a neuron expresses. Although it is well established that
individual ion channel proteins make stochastic transitions between conducting
and non-conducting states, most models of synaptic integration are
deterministic, and relatively little is known about the functional consequences
of interactions between stochastically gating ion channels. Here, we show that a
model of stellate neurons from layer II of the medial entorhinal cortex
implemented with either stochastic or deterministically gating ion channels can
reproduce the resting membrane properties of stellate neurons, but only the
stochastic version of the model can fully account for perithreshold membrane
potential fluctuations and clustered patterns of spike output that are recorded
from stellate neurons during depolarized states. We demonstrate that the
stochastic model implements an example of a general mechanism for patterning of
neuronal output through activity-dependent changes in the probability of spike
firing. Unlike deterministic mechanisms that generate spike patterns through
slow changes in the state of model parameters, this general stochastic mechanism
does not require retention of information beyond the duration of a single spike
and its associated afterhyperpolarization. Instead, clustered patterns of spikes
emerge in the stochastic model of stellate neurons as a result of a transient
increase in firing probability driven by activation of HCN channels during
recovery from the spike afterhyperpolarization. Using this model, we infer
conditions in which stochastic ion channel gating may influence firing patterns
in vivo and predict consequences of modifications of HCN
channel function for in vivo firing patterns
fMRI Evidence for a Dual Process Account of the Speed-Accuracy Tradeoff in Decision-Making
Background: The speed and accuracy of decision-making have a well-known trading relationship: hasty decisions are more prone to errors while careful, accurate judgments take more time. Despite the pervasiveness of this speed-accuracy tradeoff (SAT) in decision-making, its neural basis is still unknown. Methodology/Principal Findings: Using functional magnetic resonance imaging (fMRI) we show that emphasizing the speed of a perceptual decision at the expense of its accuracy lowers the amount of evidence-related activity in lateral prefrontal cortex. Moreover, this speed-accuracy difference in lateral prefrontal cortex activity correlates with the speedaccuracy difference in the decision criterion metric of signal detection theory. We also show that the same instructions increase baseline activity in a dorso-medial cortical area involved in the internal generation of actions. Conclusions/Significance: These findings suggest that the SAT is neurally implemented by modulating not only the amount of externally-derived sensory evidence used to make a decision, but also the internal urge to make a response. We propose that these processes combine to control the temporal dynamics of the speed-accuracy trade-off in decisionmaking
Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI
Current computational accounts posit that, in simple binary choices, humans accumulate
evidence in favour of the different alternatives before committing to a decision. Neural
correlates of this accumulating activity have been found during perceptual decisions in
parietal and prefrontal cortex; however the source of such activity in value-based choices
remains unknown. Here we use simultaneous EEG–fMRI and computational modelling to
identify EEG signals reflecting an accumulation process and demonstrate that the within- and
across-trial variability in these signals explains fMRI responses in posterior-medial frontal
cortex. Consistent with its role in integrating the evidence prior to reaching a decision, this
region also exhibits task-dependent coupling with the ventromedial prefrontal cortex and
the striatum, brain areas known to encode the subjective value of the decision alternatives.
These results further endorse the proposition of an evidence accumulation process
during value-based decisions in humans and implicate the posterior-medial frontal cortex in
this process
The Neural Representation of Prospective Choice during Spatial Planning and Decisions
We are remarkably adept at inferring the consequences of our actions, yet the neuronal mechanisms that allow us to plan a sequence of novel choices remain unclear. We used functional magnetic resonance imaging (fMRI) to investigate how the human brain plans the shortest path to a goal in novel mazes with one (shallow maze) or two (deep maze) choice points. We observed two distinct anterior prefrontal responses to demanding choices at the second choice point: one in rostrodorsal medial prefrontal cortex (rd-mPFC)/superior frontal gyrus (SFG) that was also sensitive to (deactivated by) demanding initial choices and another in lateral frontopolar cortex (lFPC), which was only engaged by demanding choices at the second choice point. Furthermore, we identified hippocampal responses during planning that correlated with subsequent choice accuracy and response time, particularly in mazes affording sequential choices. Psychophysiological interaction (PPI) analyses showed that coupling between the hippocampus and rd-mPFC increases during sequential (deep versus shallow) planning and is higher before correct versus incorrect choices. In short, using a naturalistic spatial planning paradigm, we reveal how the human brain represents sequential choices during planning without extensive training. Our data highlight a network centred on the cortical midline and hippocampus that allows us to make prospective choices while maintaining initial choices during planning in novel environments
The Timing of the Cognitive Cycle
We propose that human cognition consists of cascading cycles of recurring brain
events. Each cognitive cycle senses the current situation, interprets it with
reference to ongoing goals, and then selects an internal or external action in
response. While most aspects of the cognitive cycle are unconscious, each cycle
also yields a momentary “ignition” of conscious broadcasting.
Neuroscientists have independently proposed ideas similar to the cognitive
cycle, the fundamental hypothesis of the LIDA model of cognition. High-level
cognition, such as deliberation, planning, etc., is typically enabled by
multiple cognitive cycles. In this paper we describe a timing model LIDA's
cognitive cycle. Based on empirical and simulation data we propose that an
initial phase of perception (stimulus recognition) occurs 80–100 ms from
stimulus onset under optimal conditions. It is followed by a conscious episode
(broadcast) 200–280 ms after stimulus onset, and an action selection phase
60–110 ms from the start of the conscious phase. One cognitive cycle would
therefore take 260–390 ms. The LIDA timing model is consistent with brain
evidence indicating a fundamental role for a theta-gamma wave, spreading forward
from sensory cortices to rostral corticothalamic regions. This posteriofrontal
theta-gamma wave may be experienced as a conscious perceptual event starting at
200–280 ms post stimulus. The action selection component of the cycle is
proposed to involve frontal, striatal and cerebellar regions. Thus the cycle is
inherently recurrent, as the anatomy of the thalamocortical system suggests. The
LIDA model fits a large body of cognitive and neuroscientific evidence. Finally,
we describe two LIDA-based software agents: the LIDA Reaction Time agent that
simulates human performance in a simple reaction time task, and the LIDA Allport
agent which models phenomenal simultaneity within timeframes comparable to human
subjects. While there are many models of reaction time performance, these
results fall naturally out of a biologically and computationally plausible
cognitive architecture
The Mexican exception: patents and innovation policy in a non-conformist and reluctant middle income country
This article analyzes patent and innovation policies in Mexico. Unlike many developing countries, Mexico has enthusiastically embraced external pressures for stronger patent protection. Yet, also unlike other countries, Mexico has not complemented changes to its patent regime with measures to buttress science, technology and innovative (STI) capabilities. To explain this atypical trajectory, I focus on the shape of political coalitions in the areas of patents and STI policies. The early adoption of a strong patent regime, combined with liberalization and internationalization of the economy, consolidated a coalition based on a low-technological form of integration into the global economy, and the same processes withered away the coalition that might have pushed for an alternative project. Understanding the political underpinnings of Mexico's behavior sheds light on the conditions under which middle-income developing countries may engage in issue leadership and join with other developing countries to shape the international economic architecture