Dr Jesper Sjostrom
How does the visual cortex learn to see and interpret visual information from the outside world? How is information stored in the brain? After all, the brain has no central processing unit to control actions and no memory storage banks to keep track of information. There is only a vast network of interconnected neurons.
Today, there is good evidence supporting the notion that learning and memory occur at least in part at the synaptic connections that exist between neurons of the brain. The idea is that events in the outside world cause particular patterns of activity in subpopulations of neurons in the cortex. In turn, these activity patterns bring about changes in connective strengths among these neurons. Such changes, which are known as synaptic plasticity, are a means of storing information in neuronal circuits, and the particulars of the activity patterns that determine synaptic plasticity are known as cellular learning rules. The storage of information via changes in connective strength thus result in re-wiring of cortical sub-networks, in turn leading to particular connectivity motifs that subserve certain computational features and that in effect store specific bits of information. Consequently, there exist close links between spatio-temporal activity patterns in neuronal assemblies, connectivity motifs, and the cortical computatations that bring about detection and recall.
To understand how the brain works, it is therefore essential to know the properties and the mechanistic underpinnings of cellular learning rules, as well as their functional impact in the intact brain. In addition, we need to know the connectivity patterns that ensue from particular learning rules and how these are shaped by stimuli in the outside world. Only then can the impact on brain functioning of drugs such as marijuana, or pathology such as epilepsy, be fully appreciated.
Our starting point is Spike-Timing-Dependent Plasticity (STDP) in visual cortical microcircuits. In the STDP learning paradigm, whether synaptic strengthening and weakening is brought about depends critically on the relative millisecond timing of spiking activity in connected pairs of cells. Although the precise outcome depends on the brain region that is investigated, typically pre before postsynaptic spiking activity repeated within a couple of tens of milliseconds results in synaptic strengthening, whereas the opposite temporal order evokes weakening of synaptic connections. Because spikes are in some sense the atoms of neuronal activity, the STDP paradigm provides exquisite experimental control, which is a first critical step toward dissecting the complexities of neocortical learning rules. However, our research is not limited to the mechanisms and impact of STDP; indeed, several forms of plasticity depend critically on local dendritic spikes in the absence of the more global action potentials that underlie STDP.
To understand synaptic plasticity in visual cortical circuits, my lab employs several state-of-the-art approaches: quadruple whole-cell recordings, two-photon laser scanning microscopy, two-photon glutamate uncaging, and computer simulations.
The impact of cellular learning rules on information storage and on the organization of functional connectivity in neocortical circuits
experience-dependent cortical plasticity, circuits underlying sensory processing and behaviour
Prof Angus Silver; Dr Thomas Mrsic-Flogel; Ms Katya Woollett; Prof Michael Hausser
- Costa RP, Sjöström PJ, van Rossum MC (2013). Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits.. Front Comput Neurosci, 7, 75 - . doi:10.3389/fncom.2013.00075
- Blackman AV, Abrahamsson T, Costa RP, Lalanne T, Sjöström PJ (2013). Target-cell-specific short-term plasticity in local circuits.. Front Synaptic Neurosci, 5, 11 - . doi:10.3389/fnsyn.2013.00011
- Costa RP, Watt AJ, Sjöström PJ (2013). How to train a neuron.. Elife, 2, e00491 - . doi:10.7554/eLife.00491
- Buchanan KA, Blackman AV, Moreau AW, Elgar D, Costa RP, Lalanne T, Tudor Jones AA, Oyrer J, Sjostrom PJ (2012). Target-Specific Expression of Presynaptic NMDA Receptors in Neocortical Microcircuits. Neuron, 75, 451 - 466. doi:10.1016/j.neuron.2012.06.017
- Ko H, Hofer SB, Pichler B, Buchanan KA, Sjöström PJ, Mrsic-Flogel TD (2011). Functional specificity of local synaptic connections in neocortical networks.. Nature, 473(7345), 87 - 91. doi:10.1038/nature09880
- Markram H, Gerstner W, Sjöström PJ (2011). A history of spike-timing-dependent plasticity.. Front Synaptic Neurosci, 3, 4 - . doi:10.3389/fnsyn.2011.00004
- Costa RP, Sjöström PJ (2011). One cell to rule them all, and in the dendrites bind them.. Front Synaptic Neurosci, 3, 5 - . doi:10.3389/fnsyn.2011.00005
- Watt AJ, Cuntz H, Mori M, Nusser Z, Sjostrom PJ, Hausser M (2010). Traveling waves in developing cerebellar cortex mediated by asymmetrical Purkinje cell connectivity. NEUROSCIENCE RESEARCH, 68, E37 - E37. doi:10.1016/j.neures.2010.07.408
- Watt AJ, Cuntz H, Mori M, Nusser Z, Sjostrom PJ, Hausser M (2009). Traveling waves in developing cerebellar cortex mediated by asymmetrical Purkinje cell connectivity. Nature Neuroscience, 12(4), 463 - 473.
- Buchanan KA, Sjöström PJ (2009). A piece of the neocortical puzzle: the pyramid-Martinotti cell reciprocating principle.. J Physiol, 587(Pt 22), 5301 - 5302. doi:10.1113/jphysiol.2009.182980
- Sjöström PJ, Rancz EA, Roth A, Häusser M (2008). Dendritic Excitability and Synaptic Plasticity. Physiological Reviews, 88, 769 - 840. doi:10.1152/physrev.00016.2007
- Sjostrom PT, G G Nelson SB (2007). Multiple forms of long-term plasticity at unitary neocortical layer 5 synapses. Neuropharmacology, 52, 176 - 184.
- Sjostrom PJ, Turrigiano GG, Nelson SB (2007). Multiple forms of long-term plasticity at unitary neocortical layer 5 synapses (vol 52, pg 176, 2007). NEUROPHARMACOLOGY, 52(4), 1197 - 1197. doi:10.1016/j.neuropharm.2006.12.006
- Varshney LR, Sjöström PJ, Chklovskii DB (2006). Optimal information storage in noisy synapses under resource constraints.. Neuron, 52(3), 409 - 423. doi:10.1016/j.neuron.2006.10.017
- Duguid I, Sjostrom PJ (2006). Novel presynaptic mechanisms for coincidence detection in synaptic plasticity. Current Opinion in Neurobiology, 16(3), 312 - 322.
- Sjostrom PJ, Hausser M (2006). A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons. Neuron, 51(2), 227 - 238. doi:10.1016/j.neuron.2006.06.017
- Varshney LR, Sjostrom PJ, Chklovskii DB (2006). Optimal Information Storage in Noisy Synapses under Resource Constraints. Neuron, 52(3), 409 - 423. doi:10.1016/jneuron.2006.10.017
- Song S, Sjostrom PJ, Reigl M, Nelson S, Chklovskii DB (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits (vol 3, pg e68, 2005). PLOS BIOL, 3(10), 1838 - 1838. doi:10.1371/journal.pbio.0030350
- Song S, Sjostrom PJ, Reigl M, Nelson S, Chklovskii DB (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biology, 3(3), e68 - .
- Sjostrom PJ, Turrigiano GG, Nelson SB (2004). Endocannabinoid-dependent neocortical layer-5 LTD in the absence of postsynaptic spiking. Journal of Neurophysiology, 92(6), 3338 - 3343.
- Watt AJ, Sjostrom PJ, Häusser M, Nelson SB, Turrigiano GG (2004). A proportional but slower NMDA potentiation follows AMPA potentiation in LTP.. Nature Neuroscience, 7(5), 518 - 524.
- Sjostrom PJ, Turrigiano GG, Nelson SB (2003). Neocortical LTD via coincident activation of presynaptic NMDA and cannabinoid receptors. Neuron, 39(4), 641 - 654. doi:10.1016/S0896-6273(03)00476-8
- Sjostrom PJ, Nelson SB (2002). Spike timing, calcium signals and synaptic plasticity. Current Opinion in Neurobiology, 12(3), 305 - 314.
- Sjostrom P, Nelson SB, Turrigiano GG (2002). Rate and timing in cortical synaptic plasticity. Philosophical Transactions of the Royal Society B: Biological Sciences, 357(1428), 1851 - 1857.
- Sjostrom PJ, Turrigiano GG, Nelson SB (2001). Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32(6), 1149 - 1164. doi:10.1016/S0896-6273(01)00542-6
- Sjostrom PJ, Frydel BR, Wahlberg LU (1999). Artificial neural network-aided image analysis system for cell counting. Cytometry Part A, 36(1), 18 - 26.