Master/D opdracht (25 weken, 1000 uur)
(Onderdelen van deze opdracht kunnen als Bachelor opdracht worden uitgevoerd)
Background and problem statement
In vivo, consolidation of declarative memory can be subdivided into two specific processes 1. Rapidly after learning, memories are temporary stored in hippocampus, a process generally referred to as synaptic consolidation. During the second phase, systems consolidation, memories are slowly transferred to the neo-cortex. This stage of memory encoding probably requires repeated activation of cortical areas by the hippocampus 2-5. Long term potentiation (LTP) is generally assumed to be the underlying process in synaptic consolidation 6. However, whether and to what extent the signs of reactivation play a functional role in consolidating respective neural memory representations or merely reflect use-dependent phenomena of inert neural activity is presently not clear 7. At present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered 8 and the ‘algorithms’ that create memory traces remain unclear, forming a major obstacle in the search for memory traces.
Studies of artificial neural networks suggested that the stabilization of reverberating neural activity underlying short-term memory produces long-term memory 9,10. Later basic theories proposed that activity patterns in artificial recurrent excitatory networks are dictated by attractors, local minima in the energy landscape that are associated with certain activation patterns. External input substantially changes the set of attractors of a network 11, and thus the palette of activation patterns, which may reflect memory traces. However, these theories cannot be straightforwardly validated in biological networks, because simultaneous activity of multiple neurons is difficult to record in vivo and consequently, it is hard to provide accurate estimates of the synaptic coupling in vivo.
Dissociated cortical neurons cultured on multi electrode arrays provide a useful platform to study network aspects of neuronal tissue, including memory. A week after seeding cultures become spontaneously active, to reach a mature state after ~3 weeks 12,13. Beyond three weeks, activity patterns and connectivity stabilize, but a slow drift of observed activity patterns (on timescales of hours to days) remains 13. Activity patterns are determined by a certain connectivity, and conversely, activity patterns affect connectivity through plasticity mechanisms like STDP. Networks develop an activity-connectivity balance, where activity patterns support current connectivity 14, connectivity appears relatively constant, and activity may fluctuate within a fixed set of possible patterns. Responses to electrical stimulation usually differ from spontaneously occurring patterns and therefore disturb the activity connectivity balance, yielding a change in connectivity.
We hypothesize that networks will develop a new activity-connectivity equilibrium, such that the new spontaneous activity patterns include the network response to the applied stimulus. If so, first application of a certain stimulus should activate new patterns, and induce connectivity changes. Repeated application of that same stimulus should not lead to further connectivity changes as the stimulus response becomes part of the spontaneous repertoire, and imposes no further drive away from the equilibrium. This reasoning provides a tool to verify the hypothesis without a priori knowledge of the actual encoding of memory traces.
If the theory above holds, responses to an external stimulus, that are initially different from spontaneously occurring patterns, should become integrated in the repertoire of spontaneously occurring patterns. This will be validated using adequate measures of similarity between spatiotemporal patterns, e.g. similarity index 15or edit distance16. For this project new data will be acquired, but also existing results will be reanalyzed to answer the following questions:
oHow different are initial stimulus responses from spontaneous patterns?
oDo stimulus response patterns change during repeated application of that stimulus?
oDoes the similarity between the stimulus response and spontaneously occurring patterns increase during repeated stimulation?
oOn what time scale do these changes occur?
oDoes the system reach an equilibrium, where repeated stimulation no longer affects connectivity?
Gezocht wordt een student met affiniteit voor biomedische signaalverwerking en celbiologisch labwerk. De ontwikkelomgeving is Matlab (en mogelijk Labview). Verdere detaillering van de opdracht zal in overleg tussen student en begeleider plaats vinden.
•Backgroud literature reading
Principal Investigator track
Dr ir Joost le Feber
Supervision and info
Dr.ir J. le Feber
1 Squire, L. R., Cohen, N. J. & Nadel, L. in Memory consolidation (eds H Weingartner & E Parker) 185–210 (Lawrence Erlbaum, 1984).
2 Sutherland, G. R. & McNaughton, B. Memory trace reactivation in hippocampal and neocortical neuronal ensembles. Curr. Opin.Neurobiol. 10, 180-186 (2000).
3 Karlsson, M. P. & Frank, L. M. Awake replay of remote experiences in the hippocampus. Nat Neurosci 12, 913-918, doi:http://www.nature.com/neuro/journal/v12/n7/suppinfo/nn.2344_S1.html (2009).
4 Nakashiba, T., Buhl, D. L., McHugh, T. J. & Tonegawa, S. Hippocampal CA3 output is crucial for ripple-associated reactivation and consolidation of memory. Neuron 62, 781-787 (2009).
5 Frankland, P. W. & Bontempi, B. - The organization of recent and remote memories. - Nat Rev Neurosci. 6, 119-130 (2005).
6 Bramham, C. R. & Messaoudi, E. BDNF function in adult synaptic plasticity: The synaptic consolidation hy-pothesis. Progr Neurobiol 76, 99-125 (2005).
7 Gais, S. & Born, J. Declarative memory consolidation: Mechanisms acting during human sleep. Learning & Memory 11, 679-685, doi:10.1101/lm.80504 (2004).
8 Nadel, L., Winocur, G., Ryan, L. & Moscivitch, M. Systems consolidation and hippocampus: two views. Debates Neurosci 1, 55-66 (2007).
9 R. W. Gerard. Psychology and Psychiatry. Am. J. Psychiatry 106, 161-173 (1949).
10 Hebb, D. O. The Organization of Behavior. (Wiley, 1949).
11 Amit, D. J. Modeling brain function, the world of attractor networks. (Cambridge University Press, 1989).
12 van Pelt, J., Wolters, P. S., Corner, M. A., Rutten, W. L. C. & Ramakers, G. J. Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks. IEEE Trans Biomed Eng. 51, 2051-2062 (2004).
13 Stegenga, J., le Feber, J., Marani, E. & Rutten, W. L. C. Analysis of cultured neuronal networks using intra-burst firing characteristics. IEEE Trans Biomed Eng. 55, 1382-1390 (2008).
14 le Feber, J., Stegenga, J. & Rutten, W. L. C. The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons. PLoS ONE 5, e8871 (2010).
15 le Feber, J. et al. Conditional firing probabilities in cultured neuronal networks: a stable underlying structure in widely varying spontaneous activity patterns. J. Neural Eng. 4, 54-67 (2007).
16 Navarro, G. A guided tour to approximate string matching. ACM Computing Surveys 33, 31-88, doi:10.1145/375360.375365 (2001).