9,915 research outputs found
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
Spike frequency adaptation affects the synchronization properties of networks of cortical oscillators
Oscillations in many regions of the cortex have common temporal characteristics with dominant frequencies centered around the 40 Hz (gamma) frequency range and the 5–10 Hz (theta) frequency range. Experimental results also reveal spatially synchronous oscillations, which are stimulus dependent (Gray&Singer, 1987;Gray, König, Engel, & Singer, 1989; Engel, König, Kreiter, Schillen, & Singer, 1992). This rhythmic activity suggests that the coherence of neural populations is a crucial feature of cortical dynamics (Gray, 1994). Using both simulations and a theoretical coupled oscillator approach, we demonstrate that the spike frequency adaptation seen in many pyramidal cells plays a subtle but important role in the dynamics of cortical networks. Without adaptation, excitatory connections among model pyramidal cells are desynchronizing. However, the slow processes associated with adaptation encourage stable synchronous behavior
Event Timing in Associative Learning
Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning); if, on the other hand the odour follows the shock during training, it is approached later on (relief learning). During training, an odour-induced Ca++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems
Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb
The reshaping and decorrelation of similar activity patterns by neuronal
networks can enhance their discriminability, storage, and retrieval. How can
such networks learn to decorrelate new complex patterns, as they arise in the
olfactory system? Using a computational network model for the dominant neural
populations of the olfactory bulb we show that fundamental aspects of the adult
neurogenesis observed in the olfactory bulb -- the persistent addition of new
inhibitory granule cells to the network, their activity-dependent survival, and
the reciprocal character of their synapses with the principal mitral cells --
are sufficient to restructure the network and to alter its encoding of odor
stimuli adaptively so as to reduce the correlations between the bulbar
representations of similar stimuli. The decorrelation is quite robust with
respect to various types of perturbations of the reciprocity. The model
parsimoniously captures the experimentally observed role of neurogenesis in
perceptual learning and the enhanced response of young granule cells to novel
stimuli. Moreover, it makes specific predictions for the type of odor
enrichment that should be effective in enhancing the ability of animals to
discriminate similar odor mixtures
Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe
Recordings from neurons in the insects' olfactory primary processing center,
the antennal lobe (AL), reveal that the AL is able to process the input from
chemical receptors into distinct neural activity patterns, called olfactory
neural codes. These exciting results show the importance of neural codes and
their relation to perception. The next challenge is to \emph{model the
dynamics} of neural codes. In our study, we perform multichannel recordings
from the projection neurons in the AL driven by different odorants. We then
derive a neural network from the electrophysiological data. The network
consists of lateral-inhibitory neurons and excitatory neurons, and is capable
of producing unique olfactory neural codes for the tested odorants.
Specifically, we (i) design a projection, an odor space, for the neural
recording from the AL, which discriminates between distinct odorants
trajectories (ii) characterize scent recognition, i.e., decision-making based
on olfactory signals and (iii) infer the wiring of the neural circuit, the
connectome of the AL. We show that the constructed model is consistent with
biological observations, such as contrast enhancement and robustness to noise.
The study answers a key biological question in identifying how lateral
inhibitory neurons can be wired to excitatory neurons to permit robust activity
patterns
A continuous model of ant foraging with pheromones and trail formation
We propose and numerically analyze a PDE model of ant foraging behavior. Ant
foraging is a prime example of individuals following simple behavioral rules
based on local information producing complex, organized and ``intelligent''
strategies at the population level. One of its main aspects is the widespread
use of pheromones, which are chemical compounds laid by the ants used to
attract other ants to a food source. In this work, we consider a continuous
description of a population of ants and simulate numerically the foraging
behavior using a system of PDEs of chemotaxis type. We show that, numerically,
this system accurately reproduces observed foraging behavior, such as trail
formation and efficient removal of food sources.Comment: Conference proceeding
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Neural simulation of a system that learns representations of sensory experience
The pyriform cortex forms stable representations of smells to allow their subsequent recognition. Clustering systems are shown to perform a similar function, so they provide a guide to understanding the operation of the pyriform. A neural model of a sample of pyriform cortex was built that adheres to most known biological constraints, including learning by long-term potentiation. Results of early simulations suggest some interesting properties. The effort has implications for the knowledge representations used in artificial intelligence work
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