Biomedical subjects

Carlos D Brody

Publications and source records attributed to Carlos D Brody.

6 recordsLinked to original sources

PubMed2003

Simple networks for spike-timing-based computation, with application to olfactory processing.

Spike synchronization across neurons can be selective for the situation where neurons are driven at similar firing rates, a "many are equal" computation. This can be achieved in the absence of synaptic interactions between neurons, through phase locking to a common underlying oscillatory potential. Based on this principle, we instantiate an algorithm for robust odor recognition into a model network of spiking neurons whose main features are taken from known properties of biological olfactory systems. Here, recognition of odors is signaled by spike synchronization of specific subsets of "mitral cells." This synchronization is highly odor selective and invariant to a wide range of odor concentrations. It is also robust to the presence of strong distractor odors, thus allowing odor segmentation within complex olfactory scenes. Information about odors is encoded in both the identity of glomeruli activated above threshold (1 bit of information per glomerulus) and in the analog degree of activation of the glomeruli (approximately 3 bits per glomerulus).

Action Potentials↗

PubMed2003

Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations.

Persistent neural activity is observed in many systems, and is thought to be a neural substrate for holding memories over time delays of a few seconds. Recent work has addressed two issues. First, how can networks of neurons robustly hold such an active memory? Computer systems obtain significant robustness to noise by approximating analogue quantities with discrete digital representations. In a similar manner, theoretical models of persistent activity in spiking neurons have shown that the most robust and stable way to store the short-term memory of a continuous parameter is to approximate it with a discrete representation. This general idea applies very broadly to mechanisms that range from biochemical networks to single cells and to large circuits of neurons. Second, why is it commonly observed that persistent activity in the cortex can be strongly time-varying? This observation is almost ubiquitous, and therefore must be taken into account in our models and our understanding of how short-term memories are held in the cortex.

Animals↗

PubMed2003

Separating objects and 'neural' computation.

This article demonstrates how that the effectiveness of nervous system in doing the computations essential to an organism can be based on using algorithms that are readily implemented by nervous system 'device biophysics'. Collective effects and collective algorithms that exploit their dynamics provide powerful potential for useful neuronal computations.

Algorithms↗

PubMed2002

Analysing neuronal correlates of the comparison of two sequentially presented sensory stimuli.

In a typical sequential sensory discrimination task, subjects are required to make a decision based on comparing a sensory stimulus against the memory trace left by a previous stimulus. What is the neuronal substrate for such comparisons and the resulting decisions? This question was studied by recording neuronal responses in a variety of cortical areas of awake monkeys (Macaca mulatta), trained to carry out a vibrotactile sequential discrimination task. We describe methods to analyse responses obtained during the comparison and decision phases of the task, and describe the resulting findings from recordings in secondary somatosensory cortical area (S2). A subset of neurons in S2 become highly correlated with the monkey's decision in the task.

Animals↗

PubMed2002

From sensation to action.

Key to understanding somatosensation is the form of how the mechanical stimuli are represented in the evoked neuronal activity of the brain. Here, we focus on studies that address the question of which components of the evoked neuronal activity in the somatosensory system represent the stimulus features. We review experiments that probe whether these neuronal representations are essential to somatosensation. We also discuss recent results that suggest how the somatosensory stimuli are represented in the brain during short-term memory. Finally, we review data that show the neuronal correlates of a decision during somatosensory perception.

Animals↗

PubMed2002

Neuronal correlates of decision-making in secondary somatosensory cortex.

The ability to discriminate between two sequential stimuli requires evaluation of current sensory information in reference to stored information. Where and how does this evaluation occur? We trained monkeys to compare two mechanical vibrations applied sequentially to the fingertips and to report which of the two had the higher frequency. We recorded single neurons in secondary somatosensory cortex (S2) while the monkeys performed the task. During the first stimulus period, the firing rate of S2 neurons encoded the stimulus frequency. During the second stimulus period, however, some S2 neurons did not merely encode the stimulus frequency. The responses of these neurons were a function of both the remembered (first) and current (second) stimulus. Moreover, a few hundred milliseconds after the presentation of the second stimulus, these responses were correlated with the monkey's decision. This suggests that some S2 neurons may combine past and present sensory information for decision-making.

Animals↗