cross-correlations between nonresponsive neurons were not altered (Fig. 4E, left), they were
increased between photostimulated neurons and
remained stable the next day (Fig. 4E, right).
Thus, optogenetic activation of identified neurons enhanced their local functional connections
for at least 1 day (Fig. 4F).
Recalled ensembles shared similar characteristics—
such as number of neurons and spatial distribution—
with ongoing ensembles (fig. S7), but the mean distance between active neurons was shorter (fig.
S7D), which indicates that the effect of the photostimulation is local. Recalled ensembles often
had neurons that did not belong to ongoing ensembles (fig. S7, D and E), demonstrating that
recalled ensembles are indeed novel and not just
dormant preexisting ensembles. However, given
that cortical connections are likely not in a tabula
rasa state, we expect that imprinted ensembles
may recruit segments of physiologically relevant
circuit motifs (Fig. 4F).
Previously, electrical or optogenetic stimulation
(25) has been used to show that coactivation of
neuronal groups can produce physiologically
relevant behaviors (13, 26). Here, we show the
possibility of training individual neurons to build
artificial neuronal ensembles (13), which then
become spontaneously active (Fig. 4D, right). Our
results are consistent with the finding that neurons
responding to similar visual stimuli have a higher
interconnectivity (27), as well as with the similarity
between visually evoked and spontaneous ensembles (9). In both cases, recurrent coactivation
of a neuronal group would enhance functional
connectivity, imprinting ensembles into the
More than 60 years ago, Hebb proposed that
repeated coactivation of a group of neurons
might create a memory trace through enhancement of synaptic connections (12). Because of
technical limitations, this hypothesis has been
difficult to test with single-cell resolution in
awake animals. By combining novel imaging
and photostimulation techniques (14, 15) and
analytical tools (19), our work can be interpreted
as a confirmation of the Hebbian postulate and
as a demonstration that cortical microcircuits can
perform pattern completion.
REFERENCES AND NOTES
1. B. M. Kampa, M. M. Roth, W. Göbel, F. Helmchen, Front. Neural
Circuits 5, 18 (2011).
2. K. Ohki, S. Chung, Y. H. Ch’ng, P. Kara, R. C. Reid, Nature 433,
3. J. Sawinski et al., Proc. Natl. Acad. Sci. U.S.A. 106,
4. M. M. Churchland et al., Nature 487, 51–56 (2012).
5. A. J. Peters, S. X. Chen, T. Komiyama, Nature 510, 263–267
6. V. Y. Cao et al., Neuron 86, 1385–1392 (2015).
7. V. B. Mountcastle, Brain 120, 701–722 (1997).
8. R. Yuste, Nat. Rev. Neurosci. 16, 487–497 (2015).
9. J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid, R. Yuste, Proc. Natl.
Acad. Sci. U.S.A. 111, E4053–E4061 (2014).
10. T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, A. Arieli,
Nature 425, 954–956 (2003).
11. A. Luczak, P. Barthó, K. D. Harris, Neuron 62, 413–425 (2009).
12. D. O. Hebb, The Organization of Behavior: A Neuropsychological
Theory (Wiley, 1949).
13. J. P. Johansen et al., Proc. Natl. Acad. Sci. U.S.A. 107,
14. A. M. Packer, L. E. Russell, H. W. Dalgleish, M. Häusser, Nat.
Methods 12, 140–146 (2015).
15. J. P. Rickgauer, K. Deisseroth, D. W. Tank, Nat. Neurosci. 17,
16. P. J. Drew et al., Nat. Methods 7, 981–984 (2010).
17. S. L. Brown, J. Joseph, M. Stopfer, Nat. Neurosci. 8, 1568–1576
18. L. Carrillo-Reid et al., J. Neurophysiol. 99, 1435–1450 (2008).
19. L. Carrillo-Reid, J. E. Miller, J. P. Hamm, J. Jackson, R. Yuste, J.
Neurosci. 35, 8813–8828 (2015).
20. M. R. Hunsaker, R. P. Kesner, Neurosci. Biobehav. Rev. 37,
21. R. C. O’Reilly, J. L. McClelland, Hippocampus 4, 661–682
22. E. T. Rolls, A. Treves, Prog. Brain Res. 102, 335–341 (1994).
23. E. T. Rolls, R. P. Kesner, Prog. Neurobiol. 79, 1–48 (2006).
24. J. J. Hopfield, Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558
25. A. Jackson, J. Mavoori, E. E. Fetz, Nature 444, 56–60
26. X. Liu et al., Nature 484, 381–385 (2012).
27. H. Ko et al., Nature 473, 87–91 (2011).
We thank our laboratory members for help and virus injections,
A. Fairhall for comments, and the Stanford Neuroscience Gene
Vector and Virus Core for AAVdj virus. This work was supported by
the National Eye Institute (grants DP1EY024503 and
R01EY011787), National Institute of Mental Health (grants
R01MH101218, R01MH100561, R41MH100895, and R44MH109187),
and Defense Advanced Research Projects Agency (grant
SIMPLEX N66001-15-C-4032). Y.B. holds a fellowship from Uehara
Memorial Foundation. W. Y. holds a Career Award at the
Scientific Interface from Burroughs Wellcome Fund. This material
is based on work supported by, or in part by, the U.S. Army
Research Laboratory and the U.S. Army Research Office (contract
W911NF-12-1-0594, Multidisciplinary University Research Initiative).
We declare no competing financial interests. Author contributions:
L.C.-R. and R. Y. came up with the concept for this work. L.C.-R.,
D.S.P., W. Y., and R. Y. designed the methodology. L.C.-R., W. Y., and
Y.B. carried out the investigation. L.C.-R. wrote the original draft.
L.C.-R. and R. Y. reviewed and edited the paper. D.S.P., W. Y., and
L.C.-R. provided the resources for this work. R. Y. acquired funding.
All of the data are archived in the Neuro Technology
Center at Columbia University.
Materials and Methods
Figs. S1 to S7
24 March 2016; accepted 20 July 2016
The impact of homelessness
William N. Evans,1,2,3 James X. Sullivan,1,3 Melanie Wallskog4
Despite the prevalence of temporary financial assistance programs for those facing
imminent homelessness, there is little evidence of their impact. Using data from Chicago
from 2010 to 2012 (n = 4448), we demonstrate that the volatile nature of funding availability
leads to good-as-random variation in the allocation of resources to individuals seeking
assistance. To estimate impacts, we compare families that call when funds are available with
those who call when they are not. We find that those calling when funding is available are
76% less likely to enter a homeless shelter. The per-person cost of averting homelessness
through financial assistance is estimated as $10,300 and would be much less with better
targeting of benefits to lower-income callers. The estimated benefits, not including many
health benefits, exceed $20,000.
Over 2 million people experience homeless- ness each year in the United States (1). Historically, the primary approach to com- bating homelessness has been to provide mergency shelters or transitional housing services to those who are already homeless.
More recently, policy-makers have increased
their focus on homelessness prevention efforts.
One of the most common prevention strategies
is to provide temporary financial assistance to
people facing eviction in order to keep them in
their residences. In the United States, 93% of
households live in an area that has such a pro-
gram, and these programs receive over 15 million
calls a year (2). Despite the prevalence of these
efforts, there is little evidence about the extent to
which they actually prevent homelessness (3, 4).
Here we examine the effectiveness of temporary financial assistance by using data from the
Homelessness Prevention Call Center (HPCC)
in Chicago, which processes about 75,000 calls
annually. Chicago residents at risk of becoming
homeless can call 311 to request temporary financial assistance for rent, security deposits, or
utility bills. These callers are routed to the HPCC,
which is a centralized processing center that
screens callers for eligibility and connects eligible callers with local funding agencies.
694 12 AUGUST 2016 • VOL 353 ISSUE 6300 sciencemag.org SCIENCE
1Department of Economics, University of Notre Dame, Notre
Dame, IN 46556, USA. 2National Bureau of Economic
Research, Cambridge, MA 02138, USA. 3Wilson Sheehan Lab
for Economic Opportunities, Notre Dame, IN 46556, USA.
4Department of Economics, Stanford University, Stanford, CA
*Corresponding author. Email: firstname.lastname@example.org