Molecular Biology Faculty
Michael J. Berry II
and princeton neuroscience institute
A54 PNI - Neuroscience Institute
|A86 PNI - Neuroscience Institute|
|Lab (609) 258-2864|
Rebecca Khaitman heller
Neural computation in the retina
The brain is an organ of bewildering complexity. Ongoing research has identified a lengthening list of biological molecules and structures involved in neural signaling and of interactions among them. Yet, we do not understand what most of this biological circuitry "does" in the context of the intact brain. To this end, my laboratory studies the function of the vertebrate retina – a system of neurons simple enough to be approached quantitatively, but complex enough to hold many mysteries.
The retina is a thin sheet of neural tissue in the back of the eye where vision begins. The optics of the eye focus light down onto the plane of the retina, where it is absorbed by a layer of photoreceptor cells and converted into a neural signal. This signal flows through a layer of bipolar neurons to the final layer of ganglion cells, which send brief electrical pulses, known as spikes, down the optic nerve to the central brain. In addition, classes of interneurons called horizontal cells and amacrine cells spread signals laterally within the plane of the retina.
How does the activity of neurons in the retina represent the visual world? One can think of the layer of photoreceptors as the biological equivalent of a CCD camera, where each photoreceptor is a pixel in the retinal image and the voltage across its cell membrane encodes the intensity of light falling on it at each moment. However, the layer of ganglion cells is quite different: each cell receives input from an overlapping set of many photoreceptors and delivers its output in the form of a sparse sequence of spikes. While rod photoreceptors are all nominally identical in their responses to light, the ganglion cells are functionally diverse, extracting a great variety of different visual features from each point in an image. Thus, the manner in which visual information is encoded by the nervous system changes dramatically in these first few processing steps.
We would like to be able to read the visual messages encoded by ganglion cell spikes and perform visual discriminations as well as the brain. We also want to describe mathematically the computations carried out by the retina and say what was behaviorally important about those particular computations.
Experimentally, a flat array of 60 electrodes records spikes simultaneously from up to 100 retinal ganglion cells. Using a dense grid of electrodes and an algorithm that identifies ganglion cell signals arising on multiple electrodes, we have been able to record from all of the ganglion cells in a patch of the retina – a feat that has not been achieved in any other neural circuit. Visual stimuli are generated with a computer monitor and focused onto the plane of the retina, allowing a great variety of stimuli, including natural movie clips, to be presented. Together, these techniques enable us to control the input to the retinal circuit precisely and measure all of its relevant output.
Theoretically, we use information theory to quantify how much visual information ganglion cells can encode and to evaluate different versions of their neural code. In particular, we have been developing a framework for analyzing the way in which pairs and larger populations of neurons jointly encode visual stimuli.
So far, we have found that during natural visual conditions, retinal ganglion cells fire infrequent bursts of spikes with millisecond timing precision and are completely silent otherwise. As a result, every spike is highly informative – carrying ~4 bits of visual information. Within the population, however, ganglion cells share significant information with ~100 nearby cells, such that the entire code is highly redundant. This finding challenges theories of coding efficiency, which have been applied to the retina for many decades. We are trying to understand the role of retinal redundancy by exploring optimization and design principles, such as robustness to noise, evolutionary flexibility, and fast transmission of analog stimulus features, that are best achieved by combinatorial neural codes.
The natural visual world contains correlations and patterns on a wide variety of spatial and temporal scales. These correlations allow the brain to make accurate predictions about stimuli in the near future. The retina exhibits several such abilities: 1) anticipating the location of a smoothly moving object several hundred milliseconds into the future; 2) recognizing temporally periodic patterns and firing correctly-timed spikes in response to a stimulus "omission"; 3) adjusting its gain and filter characteristics after sudden changes in the contrast or spatial scale of visual images, thus better fitting expected stimuli into the limited dynamic range available in its output. Ongoing projects in the lab seek to explore the limits of the retina's ability to recognize patterns in the visual world as well as to understand the cellular and circuit mechanisms that make these predictions possible.
The ability to recognize patterns in the external world and use those patterns to make predictions is central to brain function. A description of the computations carried out by the retina is fundamentally interesting and promises to inform our understanding of many other parts of the brain.
Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry MJ 2nd. (2014) Searching for collective behavior in a large network of sensory neurons. PLoS Comput Biol. 10: e1003408. Pubmed
Chen EY, Marre O, Fisher C,...Berry MJ 2nd. (2013) Alert response to motion onset in the retina. J Neurosci. 33: 120-32. Pubmed
Schwartz G, Macke J, Amodei D, Tang H, Berry MJ 2nd. (2012) Low error discrimination using a correlated population code. J Neurophysiol. 108: 1069-88. Pubmed
Nguyen TD, Deshmukh N, Nagarah JM,...Berry MJ, McAlpine MC. (2012) Piezoelectric nanoribbons for monitoring cellular deformations. Nat Nanotechnol. 7: 587-93. Pubmed
Marre O, Amodei D, Deshmukh N,...Berry MJ 2nd. (2012) Mapping a complete neural population in the retina. J Neurosci. 32: 14859-73. Pubmed
Soo FS, Schwartz GW, Sadeghi K, Berry MJ 2nd. (2011) Fine spatial information represented in a population of retinal ganglion cells. J Neurosci. 31: 2145-55. PubMed
Gao J, Schwartz G, Berry MJ 2nd, Holmes P. (2009) An oscillatory circuit underlying the detection of disruptions in temporally-periodic patterns. Network. 20: 106-35. PubMed
Schwartz G, Berry MJ 2nd. (2008) Sophisticated temporal pattern recognition in retinal ganglion cells. J Neurophysiol. 99: 1787-98. PubMed
Segev R, Schneidman E, Goodhouse J, Berry MJ 2nd. (2007) Role of eye movements in the retinal code for a size discrimination task. J Neurophysiol. 98: 1380-91. PubMed
Schwartz G, Taylor S, Fisher C, Harris R, Berry MJ 2nd. (2007) Synchronized firing among retinal ganglion cells signals motion reversal. Neuron. 55: 958-69. Pubmed
Schwartz G, Harris R, Shrom D, Berry MJ 2nd. (2007) Detection and prediction of periodic patterns by the retina. Nat Neurosci. 10: 552-54. PubMed
Schneidman E, Berry MJ, Segev R, Bialek W. (2006) Weak pairwise correlations imply strongly correlated network states in a neural population. Nature. 440: 1007-12. PubMed
Segev R, Puchalla J, Berry MJ. (2006) The functional organization of ganglion cells in the salamander retina. J Neurophysiol. 95: 2277-92. PubMed
Puchalla JL, Schneidman E, Harris RA, Berry MJ. (2005) Redundancy in the population code of the retina. Neuron. 46: 493-504. PubMed
Segev R, Goodhouse J, Puchalla J, Berry MJ, 2nd. (2004) Recording spikes from a large fraction of the ganglion cells in a retinal patch. Nat Neurosci. 7: 1154-61. PubMed
Koch K, McLean J, Berry M, Sterling P, Balasubramanian V, Freed MA. (2004) Efficiency of information transmission by retinal ganglion cells. Curr Biol. 14: 1523-30. PubMed
Schneidman E, Still S, Berry MJ, 2nd, Bialek W. (2003) Network information and connected correlations. Phys Rev Lett. 91: 238701. PubMed
Schneidman E, Bialek W, Berry MJ, 2nd. (2003) Synergy, redundancy, and independence in population codes. J Neurosci. 23: 11539-53. PubMed
Balasubramanian V, Berry MJ, 2nd (2002). A test of metabolically efficient coding in the retina. Network. 13: 531-52. PubMed
Balasubramanian V, Kimber D, Berry MJ, 2nd. (2001) Metabolically efficient information processing. Neural Comput. 13: 799-815. PubMed
Meister M, Berry MJ, 2nd. (1999) The neural code of the retina. Neuron. 22: 435-50. PubMed
Berry MJ, 2nd, Brivanlou IH, Jordan TA, Meister M. (1999) Anticipation of moving stimuli by the retina. Nature. 398: 334-38. PubMed
Berry MJ, 2nd, Meister M. (1998) Refractoriness and neural precision. J Neurosci. 18: 2200-11. PubMed
Smirnakis SM, Berry MJ, Warland DK, Bialek W, Meister M. (1997) Adaptation of retinal processing to image contrast and spatial scale. Nature. 386: 69-73. PubMed
Berry MJ, Warland DK, Meister M. (1997) The structure and precision of retinal spike trains. Proc Natl Acad Sci. 94: 5411-16. PubMed