Biomedical subjects

Sheila Nirenberg

Publications and source records attributed to Sheila Nirenberg.

5 recordsLinked to original sources

PubMed2005

Synergy, redundancy, and independence in population codes, revisited.

Decoding the activity of a population of neurons is a fundamental problem in neuroscience. A key aspect of this problem is determining whether correlations in the activity, i.e., noise correlations, are important. If they are important, then the decoding problem is high dimensional: decoding algorithms must take the correlational structure in the activity into account. If they are not important, or if they play a minor role, then the decoding problem can be reduced to lower dimension and thus made more tractable. The issue of whether correlations are important has been a subject of heated debate. The debate centers around the validity of the measures used to address it. Here, we evaluate three of the most commonly used ones: synergy, DeltaI(shuffled), and DeltaI. We show that synergy and DeltaI(shuffled) are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, DeltaI is not confounded. It is zero only when correlations are not important for decoding and positive only when they are; that is, it is zero only when one can decode exactly as well using a decoder that ignores correlations as one can using a decoder that does not, and it is positive only when one cannot decode as well. Finally, we show that DeltaI has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.

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PubMed2004

Selective ablation of a class of amacrine cells alters spatial processing in the retina.

Several recent studies have suggested that the spatial tuning of retinal ganglion cells may be a more complex process than previously thought. The working hypothesis for many years was that the tuning was shaped by operations performed in the first synaptic layer of the retina, but recent work shows that operations in the second synaptic layer, involving amacrine cells, also play a significant role (Cook and McReynolds, 1998; Taylor, 1999; Flores-Herr et al., 2001). Although it is clear that amacrine cells are involved, the precise roles of the different amacrine subtypes in the many aspects of spatial tuning have not yet been established. Here we used a cell class ablation method to remove one subtype, the neuropeptide Y-expressing cells (NPY cells), and tapped into a part of the circuitry that tunes ganglion cells toward large spatial patterns (low spatial frequencies). When the subtype was ablated, ganglion cells tuned toward low spatial frequencies, both ON- and OFF-type cells, lost this preferential tuning. The effect was specific because ablation of another amacrine subtype did not produce it. Further analysis showed that the change in tuning was attributable to a decrease in the receptive field surround size of the ganglion cell. Other parameters, such as the size, strength, and asymmetry of the center and the strength of the surround, were not statistically significantly affected. These results thus show a mechanism for tuning cells to low spatial frequencies; an operation in the second synaptic layer, mediated by NPY cells, extends the surround of the ganglion cell.

Amacrine Cells↗

PubMed2004

Computing and stability in cortical networks.

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks-networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows "dynamical stabilization" of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.

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PubMed2003

Decoding neuronal spike trains: how important are correlations?

It has been known for >30 years that neuronal spike trains exhibit correlations, that is, the occurrence of a spike at one time is not independent of the occurrence of spikes at other times, both within spike trains from single neurons and across spike trains from multiple neurons. The presence of these correlations has led to the proposal that they might form a key element of the neural code. Specifically, they might act as an extra channel for information, carrying messages about events in the outside world that are not carried by other aspects of the spike trains, such as firing rate. Currently, there is no general consensus about whether this proposal applies to real spike trains in the nervous system. This is largely because it has been hard to separate information carried in correlations from that not carried in correlations. Here we propose a framework for performing this separation. Specifically, we derive an information-theoretic cost function that measures how much harder it is to decode neuronal responses when correlations are ignored than when they are taken into account. This cost function can be readily applied to real neuronal data.

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PubMed2003

Classification of retinal ganglion cells: a statistical approach.

Numerous studies have shown that retinal ganglion cells exhibit an array of responses to visual stimuli. This has led to the idea that these cells can be sorted into distinct physiological classes, such as linear versus nonlinear or on versus off. Although many classification schemes are widely accepted, few studies have provided statistical support to favor one scheme over another. Here we test whether some of the most widely used classification schemes can be statistically verified, using the mouse retina as the model system. We used a cluster analysis approach and focused on 4 standard response parameters: 1) response latency, 2) response duration, 3) relative amplitude of the on and off responses, and 4) degree of nonlinearity in the stimulus-to-response transformation. For each parameter, we plotted its distribution and tested quantitatively, using a bootstrap method, whether it divided into distinct clusters. Our analysis showed that mouse ganglion cells clustered into several groups based on response latency, duration, and relative amplitude of the on and off responses, but did not cluster into more than one group based on degree of nonlinearity-the latter formed a single, large, continuous group. Thus while some well-known schemes for classifying ganglion cells could be statistically verified, others could not. Knowledge of which schemes can be confirmed is important for building models of how retinal output is processed and how retinal circuits are built. Finally, this cluster analysis approach is general and can be used to test other classification proposals as well, both physiological and anatomical.

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